MiniMax M2.5 is SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks, boasting scores of 80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp (with context management).

MiniMax-M2.5

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

Kimi-K2.5

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

GLM-4.7-Flash

NVIDIA Nemotron 3 Nano is an open reasoning model optimized for fast, cost-efficient inference. Built with a hybrid MoE and Mamba architecture and trained on NVIDIA-curated synthetic reasoning data, it delivers strong multi-step reasoning with stable latency and predictable performance for agentic and production workloads.

Nemotron-3-Nano-30B-A3B

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

DeepSeek-V3.2

🥳 For a limited time, Fibo Edit is free on DeepInfra 🥳  YOUR AI, YOUR RULES. Visual Generation for Production-Grade. FIBO Edit. An open-source image editing model with native masking and a lightweight 8B architecture.

fibo_edit

Remove unwanted objects or regions from video using a mask, reconstructs the background with intelligent content-aware fill.

video_eraser

Automatically identify and segment foreground objects across video frames and generate a mask. No prompts, just a video.

video_foreground_mask

Increase video resolution up to 8K with advanced AI upscaling. Bring your videos to the big screen, ready for the screens of tomorrow.

video_increase_resolution

Identify and segment objects across video frames using specific coordinate points. Just point in the right direction and the model will figure out by itself which object should be masked.

video_mask_by_key_points

Identify and segment objects across video frames using a text prompt. The easiest way to create a mask to modify your videos.

video_mask_by_prompt

Light and fast. Remove the background of your videos to bring the foreground elements to focus. No more unwanted distractions.

video_remove_background

P-Image is a state-of-the-art real-time generation model  with exceptional text rendering, fine-detail accuracy, and rock-solid prompt adherence. It’s built for instant creativity at high-fidelity images in about one second at a fraction of typical model costs.

p-image

P-Image-Edit is a high-precision image editing model that applies complex transformations, insertions, removals, and style adjustments in under a second. It delivers state-of-the-art accuracy, clean boundaries, and reliable prompt alignment, making multi-step edits fast, consistent, and production-ready.

p-image-Edit

HiggsAudioV2.5 is a high-quality neural text-to-speech (TTS) model designed for natural-sounding voice generation across a wide range of use cases. It focuses on clarity, stable prosody, and consistent pacing, making it suitable for both short prompts and longer narration.

HiggsAudioV2.5

Chatterbox is a family of three state-of-the-art, open-source text-to-speech models by Resemble AI.  We are excited to introduce Chatterbox-Turbo, our most efficient model yet. Built on a streamlined 350M parameter architecture, Turbo delivers high-quality speech with less compute and VRAM than our previous models. We have also distilled the speech-token-to-mel decoder, previously a bottleneck, reducing generation from 10 steps to just one, while retaining high-fidelity audio output.  Paralinguistic tags are now native to the Turbo model, allowing you to use [cough], [laugh], [chuckle], and more to add distinct realism. While Turbo was built primarily for low-latency voice agents, it excels at narration and creative workflows.  If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (link).

chatterbox-turbo

The fastest model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-4b

The best quality-to-latency ratio, production apps model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-9b

Developed by Anthropic, Claude is a family of highly performant, trustworthy AI models built for complex reasoning, advanced coding, and nuanced language understanding. The latest Claude 4 generation delivers breakthrough capabilities in analytical thinking, with Claude 4 Opus setting new standards for intelligence and Claude 4 Sonnet providing exceptional performance with remarkable efficiency.

Claude models excel at understanding context, following complex instructions, and maintaining coherent conversations across extended interactions. With advanced features like extended thinking for deeper reasoning, prompt caching that reduces costs by up to 90%, vision capabilities for image analysis, and robust safety measures, Claude is designed for enterprise applications that demand both sophistication and reliability.

Available with comprehensive API features including streaming responses, batch processing for 50% cost savings, multilingual support across dozens of languages, and flexible context windows up to 200K tokens (1M in beta), Claude is perfect for building intelligent applications like customer support agents, content analysis systems, coding assistants, and complex reasoning workflows that require both accuracy and trustworthiness.

Claude AI family: Claude 4 Opus for complex reasoning, Claude 4 Sonnet for balanced performance, plus advanced capabilities like extended thinking, prompt caching, vision analysis, and enterprise-grade safety APIs.

Claude AI APIs via DeepInfra

claude

Developed by Anthropic, Claude is a family of highly performant, trustworthy AI models built for complex reasoning, advanced coding, and nuanced language understanding

DeepInfra provides access to Anthropic's latest Claude models, featuring the most advanced reasoning capabilities and balanced performance options, all with enterprise-grade safety and reliability.

Claude

DeepSeek develops advanced foundation models optimized for computational efficiency and strong generalization across diverse tasks. The architecture incorporates recent advances in transformer-based systems, delivering robust performance in both zero-shot and fine-tuned scenarios. Models are pretrained on rigorously filtered multilingual corpora with specialized optimizations for mathematical reasoning and algorithmic tasks. The inference stack achieves competitive throughput while maintaining low latency, making it suitable for production deployment. Researchers and engineers can leverage these models for tasks ranging from natural language processing to complex analytical problem-solving.

deepseek

DeepSeek's models are a suite of advanced AI systems that prioritize efficiency, scalability, and real-world applicability.

DeepSeek

Developed by Black Forest Labs (the original creators behind Stable Diffusion), Flux is a family of state-of-the-art image generation and editing models that deliver exceptional visual quality with breakthrough prompt accuracy and photorealism. Built on advanced 12 billion parameter architecture, Flux models excel at understanding exactly what you want to create or modify.

The revolutionary FLUX.1 Kontext introduces game-changing image editing capabilities—simply describe what you want to change in an existing image, and it makes precise modifications while keeping everything else intact. Character faces, lighting, and composition remain consistent across multiple edits, enabling truly iterative creative workflows.

Flux offers specialized variants for every need: Pro delivers maximum quality, Dev provides open-weight flexibility for research, Schnell generates images in just 1-4 steps for rapid iteration, plus dedicated editing tools for specific tasks. Available from open-source to commercial licensing, Flux is perfect for developers building creative applications, product visualization tools, and next-generation image editing experiences.


Flux AI image generation family: FLUX.1 Kontext for in-context editing, FLUX.1 Pro/Dev for text-to-image synthesis, plus comprehensive editing tools and state-of-the-art visual generation APIs.

Flux Image Generation APIs via DeepInfra

flux

Developed by Black Forest Labs, Flux is a family of state-of-the-art image generation and editing models that deliver exceptional visual quality with breakthrough prompt accuracy and photorealism.

DeepInfra provides access to Black Forest Labs' complete Flux ecosystem, offering everything from lightning-fast generation to sophisticated in-context editing capabilities with industry-leading prompt adherence and visual quality.

Flux

Developed by Google DeepMind, Gemini is a family of state-of-the-art thinking models with native multimodal capabilities, designed for advanced reasoning, complex problem-solving, and comprehensive understanding across text, audio, video, and images. Built with revolutionary thinking architecture, Gemini models reason through problems step-by-step before responding, delivering enhanced accuracy and performance for sophisticated applications.

Gemini 2.5 Pro sets new standards for complex reasoning and coding excellence, while Gemini 2.5 Flash provides optimal price-performance for high-volume tasks. With massive context windows up to 1 million tokens, native multimodal processing that handles hours of video and audio, and transparent reasoning capabilities that show step-by-step thinking processes, Gemini excels at document analysis, code generation, scientific research, and agentic workflows.

Perfect for building intelligent applications that require deep reasoning, multimodal understanding, long-context processing, and transparent AI decision-making with Google's enterprise-grade reliability and performance.


Gemini AI family: Advanced thinking models with native multimodal processing for text, audio, video, and image understanding APIs

Gemini AI Model APIs via DeepInfra

gemini

Developed by Google DeepMind, Gemini is a family of state-of-the-art thinking models with native multimodal capabilities

DeepInfra provides access to Google's latest Gemini models, featuring advanced thinking capabilities, native multimodal processing, and industry-leading performance for complex reasoning and development tasks.

Gemini

Developed by Meta, Llama (Large Language Model Meta AI) is a family of state-of-the-art open-weight models designed for efficiency and performance. The latest versions feature Mixture-of-Experts (MoE) architectures, enabling cost-effective inference by dynamically activating subsets of parameters. With support for multimodal inputs (text + images) and extended context windows (up to 10M tokens), Llama excels in tasks like code generation, multilingual understanding, and long-form reasoning. The models support FP8 quantization and batch inference, ensuring low-latency, high-throughput performance for production workloads. With permissive licensing and robust tooling (e.g., Llama Guard for safety), Llama is ideal for developers seeking powerful, customizable AI with minimal overhead.

llama

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences.

Llama 4

Meta Llama 3 are a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes.

Llama 3

Llama

Developed by Mistral AI, a leading French research lab, Mistral is a family of open-source AI models built for multilingual excellence, advanced reasoning, and cost-effective performance. These models excel at complex reasoning, mathematics, coding, and specialized tasks while offering complete transparency and deployment freedom through open-source licensing.

Mistral Small 3.2 delivers breakthrough efficiency with native fluency in European languages, while specialized variants handle specific needs: Devstral for coding, Voxtral for audio processing, and Mixtral for high-performance tasks. With Apache 2.0 licensing, extensive context windows up to 128K tokens, and comprehensive customization options, Mistral provides enterprise-grade capabilities without vendor lock-in.

Perfect for building multilingual applications, coding assistants, and reasoning systems where you need both powerful performance and complete control over your AI deployment.

Mistral AI model family: Mistral Small 3.2 for efficient performance, Mixtral for specialized tasks, Devstral for coding, plus multilingual reasoning, mathematics, and open-source flexibility APIs.

Mistral AI Model APIs via DeepInfra

mistral

Developed by Mistral AI, a leading French research lab, Mistral is a family of open-source AI models built for multilingual excellence, advanced reasoning, and cost-effective performance

DeepInfra provides access to Mistral AI's comprehensive open-source model ecosystem, from efficient small models to specialized coding and audio processing variants, all with complete Apache 2.0 licensing freedom.

Mistral

Voxtral is a family of audio models with state-of-the-art speech to text capabilities.

Voxtral

The Nemotron family is a group of large language models developed by NVIDIA, specifically engineered to excel at generating high-quality synthetic data for training other, more powerful AI models. Unlike models focused solely on end-user chat or content creation, Nemotron's core strength lies in producing diverse and realistic text-based training examples—including question-answer pairs, instructions, and conversations—that are crucial for the "supervised fine-tuning" stage of AI development. By providing a robust toolkit for creating these datasets, Nemotron acts as a powerful "force multiplier" in the AI training pipeline, enabling developers to build more capable and refined specialized models efficiently and at scale, without relying solely on scarce, human-curated data.

nemotron

NVIDIA Nemotron is a family of open models customized for efficiency, accuracy, and specialized workloads.

Nemotron

Developed by Alibaba Group's Qwen Team, Qwen is a family of state-of-the-art large language and multimodal models designed for comprehensive AI capabilities and multilingual performance. The latest Qwen3 generation features balanced model architectures including reintroduced Mixture-of-Experts (MoE) variants (Qwen3-30B-A3B and Qwen3-235B-A22B) alongside dense models up to 32B parameters, enabling efficient resource utilization through dynamic parameter activation. 

With support for 119 languages and dialects, hybrid thinking modes that seamlessly alternate between reasoning and instruction-following without model switching, and extended context windows (up to 1M tokens in Qwen3-2507), Qwen excels in tasks like multilingual understanding, code generation, agentic workflows, and complex problem-solving. The models utilize advanced Byte-level Byte Pair Encoding with a 151,646-token vocabulary, structured ChatML formatting for conversational interactions, and robust tool calling capabilities with parallel execution support. 

Available in both proprietary and open-weight versions with flexible licensing, comprehensive model variants (Base, Instruct, Thinking, and hybrid modes), and enhanced Model Context Protocol support, Qwen is ideal for developers seeking powerful, multilingual AI systems with sophisticated reasoning capabilities and minimal deployment complexity.

Qwen AI model family: Qwen3 language models, specialized coding & reasoning models, plus state-of-the-art embedding & reranking APIs for search and RAG applications.

Qwen Model APIs via DeepInfra

qwen

Qwen series offers a comprehensive suite of dense and mixture-of-experts models.

DeepInfra provides access to Qwen's latest generation of large language models, offering both specialized coding models and general-purpose AI systems with advanced reasoning capabilities.

Qwen

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B).

FLUX.1 Kontext [dev] is a 12-billion-parameter image editing model that transforms visuals based on natural language instructions. It allows highly consistent, multi-step edits and is released with open weights under a non-commercial license to empower artists and researchers.

We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

DeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs  The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows.

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Maverick, a 17 billion parameter model with 128 experts

We introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. 

The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528.

FLUX.1 [schnell] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. This model offers cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps. 

ByteDance's Seedance 1.5 Pro is a professional video model using V2A native generation for integrated, synced audio-visual output, enhancing efficiency of professional video creation.

Qwen3-235B-A22B-Thinking-2507 is the Qwen3's new model with scaling the thinking capability of Qwen3-235B-A22B, improving both the quality and depth of reasoning. 

The Phi-3-Medium-4K-Instruct is a powerful and lightweight language model with 14 billion parameters, trained on high-quality data to excel in instruction following and safety measures. It demonstrates exceptional performance across benchmarks, including common sense, language understanding, and logical reasoning, outperforming models of similar size.

This offers the imaginative writing style of chronos while still retaining coherency and being capable. Outputs are long and utilize exceptional prose. Supports a maxium context length of 4096. The model follows the Alpaca prompt format.

Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.  This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.

This model is part of the GLM-V family of models, introduced in the paper GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response.  The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so.

MiniMax-M2 is a Mini model built for Max coding & agentic workflows with just 10 billion activated parameters

WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger open-source leading models

Gemma is a family of lightweight, state-of-the-art open models from Google. Gemma-2-27B delivers the best performance for its size class, and even offers competitive alternatives to models more than twice its size. 

CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.

Gemini 2.5 Pro is Google's the most advanced thinking model, designed to tackle increasingly complex problems. Gemini 2.5 Pro leads common benchmarks by meaningful margins and showcases strong reasoning and code capabilities.  Gemini 2.5 models are thinking models, capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.  The Gemini 2.5 Pro model is now available on DeepInfra.

ClarityAI/flux integrates the Flux AI model into the upscaling process, enabling high-resolution enhancements with superior face preservation and support for LoRAs to apply specific styles or identities.

Black Forest Labs' first flagship model based on Flux latent rectified flow transformers

Nemotron-4-340B-Instruct is a chat model intended for use for the English language, designed for Synthetic Data Generation

Most widely used version of Stable Diffusion. Trained on 512x512 images, it can generate realistic images given text description

A LLM-based embedding model with in-context learning capabilities that achieves SOTA performance on BEIR and AIR-Bench. It leverages few-shot examples to enhance task performance.

A drop-in replacement for Flux [Dev] that delivers sharper details, richer colors, and enhanced realism, while instantly boosting LoRAs and LyCORIS with full compatibility.

Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes.

Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers.  Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz.

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

The Llama 90B Vision model is a top-tier, 90-billion-parameter multimodal model designed for the most challenging visual reasoning and language tasks. It offers unparalleled accuracy in image captioning, visual question answering, and advanced image-text comprehension. Pre-trained on vast multimodal datasets and fine-tuned with human feedback, the Llama 90B Vision is engineered to handle the most demanding image-based AI tasks.  This model is perfect for industries requiring cutting-edge multimodal AI capabilities, particularly those dealing with complex, real-time visual and textual analysis.

Bria Background Generation allows for efficient swapping of backgrounds in images via text prompts or reference image, delivering realistic and polished results. Trained exclusively on licensed data for safe and risk-free commercial use.

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

Gemini 1.5 Flash is Google's foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots.  Gemini 1.5 Flash is designed for high-volume, high-frequency tasks where cost and latency matter. 

ClarityAI/creative is an AI-powered image upscaler that enhances details, adds realism, and creatively modifies images to improve their quality and visual appeal.

Qwen3-Coder-480B-A35B-Instruct is the Qwen3's most agentic code model, featuring Significant Performance on Agentic Coding, Agentic Browser-Use and other foundational coding tasks, achieving results comparable to Claude Sonnet.

Qwen2.5 is a model pretrained on a large-scale dataset of up to 18 trillion tokens, offering significant improvements in knowledge, coding, mathematics, and instruction following compared to its predecessor Qwen2. The model also features enhanced capabilities in generating long texts, understanding structured data, and generating structured outputs, while supporting multilingual capabilities for over 29 languages.

Mistral-Small-3.2-24B-Instruct is a drop-in upgrade over the 3.1 release, with markedly better instruction following, roughly half the infinite-generation errors, and a more robust function-calling interface—while otherwise matching or slightly improving on all previous text and vision benchmarks.

Qwen2.5-Coder-7B is a powerful code-specific large language model with 7.61 billion parameters. It's designed for code generation, reasoning, and fixing tasks. The model covers 92 programming languages and has been trained on 5.5 trillion tokens of data, including source code, text-code grounding, and synthetic data.

A sentence transformation model that has been trained on a wide range of datasets, including but not limited to S2ORC, WikiAnwers, PAQ, Stack Exchange, and Yahoo! Answers. Our model can be used for various NLP tasks such as clustering, sentiment analysis, and question answering.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

Meta developed and released the Meta Llama 3.1 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes

The 7 billion parameter Qwen2.5 excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning

Black Forest Labs' latest state-of-the art proprietary model sporting top of the line prompt following, visual quality, details and output diversity.

Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper "Robust Speech Recognition via Large-Scale Weak Supervision" by Alec Radford  et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

Faster version of Gryphe/MythoMax-L2-13b running on multiple H100 cards in fp8 precision. Up to 160 tps. 

Euryale 3.1 - 70B v2.2 is a model focused on creative roleplay from Sao10k

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).

BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in over 100 languages and with input sizes up to 8192 tokens. The model can be used in a retrieval pipeline with hybrid retrieval and re-ranking to achieve higher accuracy and stronger generalization capabilities. BGE-M3 has shown state-of-the-art performance on several benchmarks, including MKQA, MLDR, and NarritiveQA, and can be used as a drop-in replacement for other embedding models like DPR and BGE-v1.5.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labeled data without fine-tuning. It's a Transformer based encoder-decoder model, trained on English-only or multilingual data, predicting transcriptions in the same or different language as the audio. Whisper checkpoints come in five configurations of varying model sizes.

The Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:” or “passage:”, as the model was explicitly trained with this format.

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B)

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrated a strong ability to generalise to many datasets and domains without fine-tuning. Whisper checks pens are available in five configurations of varying model sizes, including a smallest configuration trained on English-only data and a largest configuration trained on multilingual data. This one is English-only.

Anthropic's mid-size model with superior intelligence for high-volume uses in coding, in-depth research, agents, & more.

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

Compared with GLM-4.5, GLM-4.6 brings several key improvements:  Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability. More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks. Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Gemini 2.5 Flash is Google's latest thinking model, designed to tackle increasingly complex problems. It's capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.  Gemini 2.5 Flash: best for balancing reasoning and speed.

Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries. This model reaches Arena Hard of 85.0, AlpacaEval 2 LC of 57.6 and GPT-4-Turbo MT-Bench of 8.98, which are known to be predictive of LMSys Chatbot Arena Elo.  As of 16th Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.

We present a sentence similarity model based on the Sentence Transformers architecture, which maps sentences to a 384-dimensional dense vector space. The model uses a pre-trained BERT encoder and applies mean pooling on top of the contextualized word embeddings to obtain sentence embeddings. We evaluate the model on the Sentence Embeddings Benchmark.

DeepSeek-V3.2-Exp is an intermediate step toward the next-generation architecture of the DeepSeek models by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.

Llama 3.3-70B is a multilingual LLM trained on a massive dataset of 15 trillion tokens, fine-tuned for instruction-following and conversational dialogue. The model is designed to be helpful, safe, and flexible, with a focus on responsible deployment and mitigating potential risks such as bias, toxicity, and misinformation. It achieves state-of-the-art performance on various benchmarks, including conversational tasks, language translation, and text generation.

The Wan2.1 1.3B model is a lightweight, efficient text-to-video generator. Despite its compact size, it delivers impressive performance across benchmarks and generates high-quality 480P videos.

The latest flagship reasoning model in the Qwen3 family. Further enhanced by multiple innovations like adaptive tool-use and advanced test-time scaling techniques

olmOCR is a specialized AI tool that converts PDF documents into clean, structured text while preserving important formatting and layout information. What makes olmOCR particularly valuable for developers is its ability to handle challenging PDFs that traditional OCR tools struggle with—including complex layouts, poor-quality scans, handwritten text, and documents with mixed content types. Built on a fine-tuned 7B vision-language model, olmOCR provides enterprise-grade PDF processing at a fraction of the cost of proprietary solutions.

Bria GenFill enables high-quality object addition or visual transformation. Trained exclusively on licensed data for safe and risk-free commercial use.

The CLIP model maps text and images to a shared vector space, enabling various applications such as image search, zero-shot image classification, and image clustering. The model can be used easily after installation, and its performance is demonstrated through zero-shot ImageNet validation set accuracy scores. Multilingual versions of the model are also available for 50+ languages.

LLaMa 2 is a collections of LLMs trained by Meta. This is the 70B chat optimized version. This endpoint has per token pricing.

Openchat 3.6 is a LLama-3-8b fine tune that outperforms it on multiple benchmarks.

L3.3-70B-Euryale-v2.3 is a model focused on creative roleplay from Sao10k

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

A generalist / roleplaying model merge based on Llama 3. Sao10K has carefully selected the values based on extensive personal experimentation and has fine-tuned them to create a customized recipe.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrates a strong ability to generalize to many datasets and domains without fine-tuning.  The model is based on a Transformer encoder-decoder architecture.  Whisper models are available for various languages including English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, and many more.

DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high compression ratios to ensure an optimal and manageable number of vision tokens. Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10x), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20x, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs.

This is the instruction fine-tuned version of Mixtral-8x22B - the latest and largest mixture of experts large language model (LLM) from Mistral AI. This state of the art machine learning model uses a mixture 8 of experts (MoE) 22b models. During inference 2 experts are selected. This architecture allows large models to be fast and cheap at inference.

Stable Diffusion is a latent text-to-image diffusion model. Generate realistic images given text description

09/04 🔥 Introducing Chatterbox Multilingual in 23 Languages!  We're excited to introduce Chatterbox and Chatterbox Multilingual, Resemble AI's production-grade open source TTS models. Chatterbox Multilingual supports Arabic, Danish, German, Greek, English, Spanish, Finnish, French, Hebrew, Hindi, Italian, Japanese, Korean, Malay, Dutch, Norwegian, Polish, Portuguese, Russian, Swedish, Swahili, Turkish, Chinese out of the box. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without fine-tuning. The primary intended users of these models are AI researchers studying robustness, generalisation, and capabilities of the current model.

Mixtral is mixture of expert large language model (LLM) from Mistral AI. This is state of the art machine learning model using a mixture 8 of experts (MoE) 7b models. During inference 2 expers are selected. This architecture allows large models to be fast and cheap at inference. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks.

Llama-3.3-Nemotron-Super-49B-v1.5 is a large language model (LLM) optimized for advanced reasoning, conversational interactions, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta's Llama-3.3-70B-Instruct, it employs a Neural Architecture Search (NAS) approach, significantly enhancing efficiency and reducing memory requirements. 

BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval), and Multi-Granularity (handling inputs from short queries to 8192-token documents). It achieves state-of-the-art retrieval performance across diverse benchmarks while maintaining a single model for multiple retrieval modes.

You can POST to our OpenAI Chat Completions compatible endpoint.

#### Simple messages and prompts

Given a list of messages from a conversation, the model will return a response.

```bash
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "model": "zai-org/GLM-4.6V",
 "messages": [
 {
 "role": "user",
 "content": "Hello!"
 }
 ]
 }'
```

To which you'd get something like:

```json
{
 "id": "chatcmpl-guMTxWgpFf",
 "object": "chat.completion",
 "created": 1694623155,
 "model": "zai-org/GLM-4.6V",
 "choices": [
 {
 "index": 0,
 "message": {
 "role": "assistant",
 "content": " Hello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
 },
 "finish_reason": "stop"
 }
 ],
 "usage": {
 "prompt_tokens": 15,
 "completion_tokens": 16,
 "total_tokens": 31,
 "estimated_cost": 0.0000268
 }
}
```

#### Conversations

To create a longer chat-like conversation you just have to add each response message and each of the user messages to every request. This way the model will have the context and will be able to provide better answers. You can tweak it even further by providing a system message.

```bash
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "model": "zai-org/GLM-4.6V",
 "messages": [
 {
 "role": "system",
 "content": "Respond like a michelin starred chef."
 },
 {
 "role": "user",
 "content": "Can you name at least two different techniques to cook lamb?"
 },
 {
 "role": "assistant",
 "content": "Bonjour! Let me tell you, my friend, cooking lamb is an art form, and I'"'"'m more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\""
 },
 {
 "role": "user",
 "content": "Tell me more about the second method."
 }
 ]
 }'
```

The conversation above might return something like the following

```json
{
 "id": "chatcmpl-b23a3fb60cde42ce8f24bb980b4dee87",
 "object": "chat.completion",
 "created": 1715688169,
 "model": "zai-org/GLM-4.6V",
 "choices": [
 {
 "index": 0,
 "message": {
 "role": "assistant",
 "content": "Sous le Sable, my friend! It's an ancient technique that's been used for centuries in the Middle East and North Africa. The name itself..."
 },
 "finish_reason": "stop"
 }
 ],
 "usage": {
 "prompt_tokens": 149,
 "total_tokens": 487,
 "completion_tokens": 338,
 "estimated_cost": 0.00035493
 }
}
```

The longer the conversation gets, the more time it takes the model to generate the response. The number of messages that you can have in a conversation is limited by the context size of a model. Larger models also usually take more time to respond.

 

### Streaming

You can turn any of the requests above into a streaming request by passing `"stream": true`:

```bash
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "model": "zai-org/GLM-4.6V",
 "stream": true,
 "messages": [
 {
 "role": "user",
 "content": "Hello!"
 }
 ]
 }'
```

to which you'd get a sequence of [SSE](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events) events, finishing with `[DONE]`.

```
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " "}, "finish_reason": null}]}

data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Hi"}, "finish_reason": null}]}

data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "!"}, "finish_reason": null}]}

data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": null}]}

data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "</s>"}, "finish_reason": null}]}

data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "zai-org/GLM-4.6V", "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}

data: [DONE]
```

You can use the official openai python client to run inferences with us

```python
# Assume openai>=1.0.0
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
 api_key="$DEEPINFRA_TOKEN",
 base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
 model="zai-org/GLM-4.6V",
 messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
```

#### Conversations

To create a longer chat-like conversation you just have to add each response message and each of the user messages to every request. This way the model will have the context and will be able to provide better answers. You can tweak it even further by providing a system message.

```python
# Assume openai>=1.0.0
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
 api_key="$DEEPINFRA_TOKEN",
 base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
 model="zai-org/GLM-4.6V",
 messages=[
 {"role": "system", "content": "Respond like a michelin starred chef."},
 {"role": "user", "content": "Can you name at least two different techniques to cook lamb?"},
 {"role": "assistant", "content": "Bonjour! Let me tell you, my friend, cooking lamb is an art form, and I'm more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\""},
 {"role": "user", "content": "Tell me more about the second method."},
 ],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Sous le Sable! It's an ancient technique that never goes out of style, n'est-ce pas? Literally ...
# 149 324
```

The longer the conversation gets, the more time it takes the model to generate the response. The number of messages that you can have in a conversation is limited by the context size of a model. Larger models also usually take more time to respond.

 

### Streaming

Streaming any of the chat completions above is supported by adding the `stream=True` option.


```python
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
 api_key="$DEEPINFRA_TOKEN",
 base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
 model="zai-org/GLM-4.6V",
 messages=[{"role": "user", "content": "Hello"}],
 stream=True,
)

for event in chat_completion:
 if event.choices[0].finish_reason:
 print(event.choices[0].finish_reason, event.usage["prompt_tokens"], event.usage["completion_tokens"])
 else:
 print(event.choices[0].delta.content)

# Hello
# !
# It
# 's
# nice
# ...
# 11 25
```

You can use JavaScript in the browser or in node.js to make requests

```bash
npm install openai
```

then

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
 baseURL: 'https://api.deepinfra.com/v1/openai',
 apiKey: "$DEEPINFRA_TOKEN",
});

async function main() {
 const completion = await openai.chat.completions.create({
 messages: [{ role: "user", content: "Hello" }],
 model: "zai-org/GLM-4.6V",
 });

 console.log(completion.choices[0].message.content);
 console.log(completion.usage.prompt_tokens, completion.usage.completion_tokens);
}

main();

// Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
// 11 25
```

#### Conversations

To create a longer chat-like conversation you just have to add each response message and each of the user messages to every request. This way the model will have the context and will be able to provide better answers. You can tweak it even further by providing a system message.

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
 baseURL: 'https://api.deepinfra.com/v1/openai',
 apiKey: "$DEEPINFRA_TOKEN",
});

async function main() {
 const completion = await openai.chat.completions.create({
 messages: [
 {role: "system", content: "Respond like a michelin starred chef."},
 {role: "user", content: "Can you name at least two different techniques to cook lamb?"},
 {role: "assistant", content: "Bonjour! Let me tell you, my friend, cooking lamb is an art form, and I'm more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\""},
 {role: "user", "content": "Tell me more about the second method."}
 ],
 model: "zai-org/GLM-4.6V",
 });

 console.log(completion.choices[0].message.content);
 console.log(completion.usage.prompt_tokens, completion.usage.completion_tokens);
}

main();

// Sous le Sable, my friend! This traditional technique hails from the ancient Mediterranean, wher...
// 149 324
```

The longer the conversation gets, the more time it takes the model to generate the response. The number of messages that you can have in a conversation is limited by the context size of a model. Larger models also usually take more time to respond.

 

### Streaming

Streaming any of the chat completions above is supported by adding the `stream: true` option.

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
 baseURL: 'https://api.deepinfra.com/v1/openai',
 apiKey: "$DEEPINFRA_TOKEN",
});

async function main() {
 const completion = await openai.chat.completions.create({
 messages: [{ role: "user", content: "Hello" }],
 model: "zai-org/GLM-4.6V",
 stream: true,
 });

 for await (const chunk of completion) {
 if (chunk.choices[0].finish_reason) {
 console.log(chunk.choices[0].finish_reason, chunk.usage.prompt_tokens, chunk.usage.completion_tokens);
 } else {
 console.log(chunk.choices[0].delta.content);
 }
 }
}

main();

// Hello
// !
// It
// 's
// nice
// ...
// 11 25
```

The [AI SDK](https://sdk.vercel.ai/) by Vercel makes it very easy to do inference.

```bash
npm install ai @ai-sdk/deepinfra
```

then

```javascript
import { createDeepInfra } from "@ai-sdk/deepinfra";
import { generateText } from "ai";

const deepinfra = createDeepInfra({
 apiKey: "$DEEPINFRA_TOKEN",
});

const { text, usage, finishReason } = await generateText({
 model: deepinfra("zai-org/GLM-4.6V"),
 prompt: "Write a vegetarian lasagna recipe for 4 people.",
});

console.log(text);
console.log(usage);
console.log(finishReason);
```

#### Conversations

To create a longer chat-like conversation you just have to add each response message and each of the user messages to every request. This way the model will have the context and will be able to provide better answers. You can tweak it even further by providing a system message.

```javascript
import { createDeepInfra } from "@ai-sdk/deepinfra";
import { generateText } from "ai";

const deepinfra = createDeepInfra({
 apiKey: "$DEEPINFRA_TOKEN",
});

const { text, usage, finishReason } = await generateText({
 model: deepinfra("zai-org/GLM-4.6V"),
 messages: [
 { role: "system", content: "Respond like a michelin starred chef." },
 {
 role: "user",
 content: "Can you name at least two different techniques to cook lamb?",
 },
 {
 role: "assistant",
 content:
 'Bonjour! Let me tell you, my friend, cooking lamb is an art form, and I\'m more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic "Sous Vide" method. Next, we have the ancient art of "Sous le Sable". And finally, we have the more modern technique of "Hot Smoking."',
 },
 { role: "user", content: "Tell me more about the second method." },
 ],
});

console.log(text);
console.log(usage);
console.log(finishReason);
```

The longer the conversation gets, the more time it takes the model to generate the response. The number of messages that you can have in a conversation is limited by the context size of a model. Larger models also usually take more time to respond.

 

### Streaming

Streaming text responses is easy just replace `generateText` with `streamText` and read the response chunk by chunk

```javascript
import { createDeepInfra } from "@ai-sdk/deepinfra";
import { streamText } from "ai";

const deepinfra = createDeepInfra({
 apiKey: "$DEEPINFRA_TOKEN",
});

const result = streamText({
 model: deepinfra("meta-llama/Llama-3.3-70B-Instruct-Turbo"),
 prompt: "Invent a new holiday and describe its traditions.",
 system:
 "You are a professional writer. You write simple, clear, and concise content.",
});

for await (const textPart of result.textStream) {
 console.log(textPart);
}

console.log(await result.usage);
console.log(await result.finishReason);
```

It works for conversations, too.

This is an advanced and more complex API. We strongly recommend that you use OpenAI Chat Completions instead.

#### Simple prompt

To query this model you need to provide a properly formatted input string.

```bash
curl "https://api.deepinfra.com/v1/inference/zai-org/GLM-4.6V" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "input": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
 "stop": []
 }'
```

That will respond with

```json
{
 "request_id": "RWZDRhS5kdoM1XWwXLEshynO",
 "inference_status": {
 "status": "succeeded",
 "runtime_ms": 243,
 "cost": 0.0000436,
 "tokens_input": 12,
 "tokens_generated": 25
 },
 "results": [
 {
 "generated_text": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
 }
 ],
 "num_tokens": 25,
 "num_input_tokens":12
}
```

#### Conversations

The OpenAI Chat Completions API is better suited for chat-like conversations, use it instead.

To query this model you need to provide a properly formatted input string.

However, you can still do it if you really need to. You have to add each response and each of the user prompts to every request.
You need a properly formatted input string to make it understand the current context. See the example below for some of them.
You can tweak it even further by providing a system message.

```bash
curl "https://api.deepinfra.com/v1/inference/zai-org/GLM-4.6V" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "input": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nRespond like a michelin starred chef.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCan you name at least two different techniques to cook lamb?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nBonjour! Let me tell you, my friend, cooking lamb is an art form, and I'"'"'m more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\"<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTell me more about the second method.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
 "stop": []
 }'
```

The conversation above might return something like the following

```json
{
 "request_id": "RWZDRhS5kdoM1XWwXLEshynO",
 "inference_status": {
 "status": "succeeded",
 "runtime_ms": 243,
 "cost": 0.000436,
 "tokens_input": 149,
 "tokens_generated": 338
 },
 "results": [
 {
 "generated_text": "Sous le Sable, my friend! It's an ancient technique that's been used for centuries in the Middle East and North Africa. The name itself..."
 }
 ],
 "num_tokens": 338,
 "num_input_tokens": 149
}
```

The longer the conversation gets, the more time it takes the model to generate the response. The conversation is limited by the context size of a model. Larger models also usually take more time to respond.

 

### Streaming


To do a streaming request, just pass `"stream": true`:

```bash
curl "https://api.deepinfra.com/v1/inference/zai-org/GLM-4.6V" \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -d '{
 "input": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
 "stop": [],
 "stream": true
 }'
```

which outputs:

```json
data: {"token": {"id": null, "text": "Hello", "logprob": 0.0, "special": false}, "generated_text": "", "details": null, "estimated_cost": null}

data: {"token": {"id": null, "text": "!", "logprob": 0.0, "special": false}, "generated_text": "", "details": null, "estimated_cost": null}

data: {"token": {"id": null, "text": " It", "logprob": 0.0, "special": false}, "generated_text": "", "details": null, "estimated_cost": null}

data: {"token": {"id": null, "text": "'s", "logprob": 0.0, "special": false}, "generated_text": "", "details": null, "estimated_cost": null}

....

data: {"token": {"id": null, "text": "", "logprob": 0.0, "special": false}, "generated_text": null, "details": {"finish_reason": "stop"}, "num_output_tokens": 25, "num_input_tokens": 12, "estimated_cost": 0.0000386}
```

#### Input format

You can see below the basic format of the input. Bear in mind that newlines often matter.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

Conversation prompts contain the history of the exchanged prompts and responses.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

If you want to add system prompt, it is done like this

```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

System prompt<|eot_id|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

You can use our command-line tool [deepctl](/docs/getting-started) to run inferences:

This is an advanced and more complex API. We strongly recommend that you use OpenAI Chat Completions instead.

#### Simple prompt

To query this model you need to provide a properly formatted input string.

```bash
deepctl infer \
 -m 'zai-org/GLM-4.6V' \
 -i 'input="<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"' \
 -i 'stop=[]'
```

That will respond with

```json
{
 "inference_status": {
 "status": "succeeded",
 "runtime_ms": 243,
 "cost": 0.0000436,
 "tokens_input": 12,
 "tokens_generated": 25
 },
 "results": [
 {
 "generated_text": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
 }
 ],
 "num_tokens": 25,
 "num_input_tokens":12
}
```

#### Conversations

The OpenAI Chat Completions API is better suited for chat-like conversations, use it instead.

To query this model you need to provide a properly formatted input string.

However, you can still do it if you really need to. You have to add each response and each of the user prompts to every request.
You need a properly formatted input string to make it understand the current context. See the example below for some of them.
You can tweak it even further by providing a system message.

```bash
deepctl infer \
 -m 'zai-org/GLM-4.6V' \
 -i 'input="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nRespond like a michelin starred chef.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCan you name at least two different techniques to cook lamb?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nBonjour! Let me tell you, my friend, cooking lamb is an art form, and I'"'"'m more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\"<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTell me more about the second method.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"' \
 -i 'stop=[]'
```

The conversation above might return something like the following

```json
{
 "inference_status": {
 "status": "succeeded",
 "runtime_ms": 243,
 "cost": 0.000436,
 "tokens_input": 149,
 "tokens_generated": 338
 },
 "results": [
 {
 "generated_text": "Sous le Sable, my friend! It's an ancient technique that's been used for centuries in the Middle East and North Africa. The name itself..."
 }
 ],
 "num_tokens": 338,
 "num_input_tokens": 149
}
```

The longer the conversation gets, the more time it takes the model to generate the response. The conversation is limited by the context size of a model. Larger models also usually take more time to respond.

#### Input format

You can see below the basic format of the input. Bear in mind that newlines often matter.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

Conversation prompts contain the history of the exchanged prompts and responses.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

If you want to add system prompt, it is done like this

```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

System prompt<|eot_id|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

We recommend using our NodeJS client https://github.com/deepinfra/deepinfra-node.

You can install it with

```bash
npm install deepinfra
```

#### Simple prompt

To query this model you need to provide a properly formatted input string.

```javascript
import { TextGeneration } from "deepinfra";

const DEEPINFRA_API_KEY = '$DEEPINFRA_TOKEN';
const MODEL_URL = 'https://api.deepinfra.com/v1/inference/zai-org/GLM-4.6V';

async function main() {
 const client = new TextGeneration(MODEL_URL, DEEPINFRA_API_KEY);
 const res = await client.generate({
 "input": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
 "stop": []
 });
 console.log(res.results[0].generated_text);
}

main();

// Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
```

#### Conversations

The OpenAI Chat Completions API is better suited for chat-like conversations, use it instead.

However, you can still do it if you really need to. You have to add each response and each of the user prompts to every request.
You need a properly formatted input string to make it understand the current context. See the example below for some of them.
You can tweak it even further by providing a system message.

```javascript
import { TextGeneration } from "deepinfra";

const DEEPINFRA_API_KEY = '$DEEPINFRA_TOKEN';
const MODEL_URL = 'https://api.deepinfra.com/v1/inference/zai-org/GLM-4.6V';

async function main() {
 const client = new TextGeneration(MODEL_URL, DEEPINFRA_API_KEY);
 const res = await client.generate({
 "input": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nRespond like a michelin starred chef.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCan you name at least two different techniques to cook lamb?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nBonjour! Let me tell you, my friend, cooking lamb is an art form, and I'm more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\"<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTell me more about the second method.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
 "stop": []
 });
 console.log(res.results[0].generated_text);
}

main();

// Sous le Sable! It's an ancient technique that never goes out of style, n'est-ce pas? Literally ...
```

The longer the conversation gets, the more time it takes the model to generate the response.
The number of messages that you can have in a conversation is limited by the context size of a model.
Larger models also usually take more time to respond.

#### Input format

You can see below the basic format of the input. Bear in mind that newlines often matter.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

Conversation prompts contain the history of the exchanged prompts and responses.

```text
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

If you want to add system prompt, it is done like this

```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

System prompt<|eot_id|><|start_header_id|>user<|end_header_id|>

First question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

First answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Second question<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Second answer<|eot_id|><|start_header_id|>user<|end_header_id|>

Final question<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

input

maximum length of the newly generated generated text.If explicitly set to None it will be the model's max context length minus input length or 16384, whichever is smaller

max_new_tokens

temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity

temperature

Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens

top_p

Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this.

min_p

Sample from the best k (number of) tokens. 0 means off

top_k

repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition.

repetition_penalty

Up to 16 strings that will terminate generation immediately

stop

Number of output sequences to return. Incompatible with streaming

num_responses

Optional nested object with "type" set to "json_object"

response_format

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

presence_penalty

Positive values penalize new tokens based on how many times they appear in the text so far, increasing the model's likelihood to talk about new topics.

frequency_penalty

A unique identifier representing your end-user, which can help monitor and detect abuse. Avoid sending us any identifying information. We recommend hashing user identifiers.