GLM-5 is an advanced, open-source large language model designed for developers tackling the toughest challenges. It excels at long-context reasoning, multi-step tool orchestration, and complex systems engineering, making it the ideal choice for powering sophisticated agents and applications that require high-level cognitive tasks.

GLM-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

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

12B model trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.

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. 

Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.

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

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

LLaMA2-13B-Tiefighter is a highly creative and versatile language model, fine-tuned for storytelling, adventure, and conversational dialogue. It combines the strengths of multiple models and datasets, including retro-rodeo and choose-your-own-adventure, to generate engaging and imaginative content. With its ability to improvise and adapt to different styles and formats, Tiefighter is perfect for writers, creators, and anyone looking to spark their imagination.

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

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.

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.

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.

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).

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.

Blazing-fast, high-quality images rendered at five times the speed of Flux. Perfect for mood boards and mass ideation, this model excels in both realism and prompt adherence.

Kimi K2 Thinking is the latest, most capable version of open-source thinking model developed by MoonshotAI

DeepSeek R1 Distill Qwen 32B is a distilled large language model based on Qwen 2.5 32B, using outputs from DeepSeek R1. It outperforms OpenAI's o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.  Other benchmark results include:  AIME 2024: 72.6 | MATH-500: 94.3 | CodeForces Rating: 1691.

Mistral-7B-Instruct-v0.3 is an instruction-tuned model, next iteration of of Mistral 7B that has larger vocabulary, newer tokenizer and supports function calling.

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

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.

Kimi K2 is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks.

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.

Bria RMBG 2.0 enables seamless removal of backgrounds from images, ideal for professional editing tasks. Trained exclusively on licensed data for safe and risk-free commercial use.

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to be on par to leading open-source models, and outperforming other licensed models.

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

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

GLM-4.7 is a state-of-the-art, multilingual Mixture-of-Experts (MoE) language model designed for complex reasoning, agentic coding, and tool use. Building on its predecessor GLM-4.6, it delivers significant improvements across key benchmarks, including multilingual SWE-bench, Terminal Bench, and reasoning-heavy evaluations like HLE. The model features advanced "Interleaved Thinking" and new "Preserved Thinking" modes, allowing it to reason before actions and maintain consistency across long, multi-turn tasks. With 358 billion parameters, GLM-4.7 excels in generating clean code, modern UI elements, and sophisticated reasoning outputs.

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

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)

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.2 generative text model using a variety of publicly available conversation datasets.

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. 

Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment.  The model achieves 81% accuracy on the MMLU benchmark and performs competitively with larger models like Llama 3.3 70B and Qwen 32B, while operating at three times the speed on equivalent hardware.

A Mythomax/MLewd_13B-style merge of selected 70B models  A multi-model merge of several  LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.

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.

Bria Expand expands images beyond their borders in high quality. Resizing the image by generating new pixels to expand to the desired aspect ratio. Trained exclusively on licensed data for safe and risk-free commercial use.

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

Qwen-Image-Edit is a next-generation image editing model built on top of Qwen-Image, designed for both semantic and appearance-level edits. It excels at tasks like precise text modifications, style transfers, viewpoint transformations, and element adjustments while preserving overall visual consistency.

Janus-Pro is a novel autoregressive framework that unifies multimodal understanding and generation. It addresses the limitations of previous approaches by decoupling visual encoding into separate pathways, while still utilizing a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder’s roles in understanding and generation, but also enhances the framework’s flexibility. Janus-Pro surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus-Pro make it a strong candidate for next-generation unified multimodal models.

The 72 billion parameter Qwen2 excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.

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.

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.

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

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.

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.

Seedream 4.0 is a SOTA multimodal image creation model built on leading architecture. It breaks through the boundaries of traditional text-to-image models by natively supporting text, single-image, and multi-image inputs. Users can freely combine text and images to achieve diverse creative modes within a single model—such as multi-image blending, image editing, and sequentially batch image generation, featuring subject consistency, making image creation more free and controllable.

Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This variant contains implementation to predict word timestamps and provide a more accurate estimation of speech segments when transcribing with Whisper models.

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.

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.

Veo 3.1 is the latest text-to-video model from Google that generates high-fidelity, cinematic videos with synchronized audio from a simple text prompt. It excels at creating realistic and imaginative scenes with a deep understanding of natural language and visual dynamics.

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Model has 24 layers and 1024 out dim. 

The latest flagship model in the Qwen family. State-of-the-art results across a comprehensive suite of benchmarks — including knowledge, reasoning, coding, instruction following, human preference alignment, agent tasks, and multilingual understanding.

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.

The model is an auto-regressive vision language model that uses an optimized transformer architecture. The model enables multi-image reasoning and video understanding, along with strong document intelligence, visual Q&A and summarization capabilities.

The new top-tier image model from Black Forest Labs, significantly pushing image quality and editing consistency

Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.

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.

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.

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

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.

DeepSeek-V3-0324, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token, an improved iteration over DeepSeek-V3.

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.

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.

WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to those leading proprietary models.

FIBO is an open-source, JSON-native text-to-image model trained on detailed structured descriptions (over 1,000+ words per image), providing fine-grained control over light, composition, and camera parameters.

The DeepSeek R1 0528 turbo model is a state of the art reasoning model that can generate very quick responses

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.

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)

The Wan2.1 14B model is a high-capacity, state-of-the-art video foundation model capable of producing both 480P and 720P videos. It excels at capturing complex prompts and generating visually rich, detailed scenes, making it ideal for high-end creative tasks.

Hermes 3 is a cutting-edge language model that offers advanced capabilities in roleplaying, reasoning, and conversation. It's a fine-tuned version of the Llama-3.1 405B foundation model, designed to align with user needs and provide powerful control. Key features include reliable function calling, structured output, generalist assistant capabilities, and improved code generation. Hermes 3 is competitive with Llama-3.1 Instruct models, with its own strengths and weaknesses.

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

Veo 3 Fast is a speed-optimized version of the Veo 3 model, designed for rapid video creation. While maintaining high quality, it delivers results in a fraction of the time, making it ideal for quick iterations and dynamic content generation.

Brand-new Flux2 Dev introduces a faster, more modular architecture for next-generation image generation pipelines. It delivers improved performance, cleaner control APIs, and a significantly more flexible development workflow for custom inference setups.

Multi-reference visual intelligence with unprecedented detail, color precision, and spatial reasoning.  The most advanced image generation and editing model. Generate photorealistic images with precise control.

CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes.

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 Multilingual-E5-large model is a 24-layer text embedding model with an embedding size of 1024, trained on a mixture of multilingual datasets and supporting 100 languages.

Dolphin 2.9.1, a fine-tuned Llama-3-70b model. The new model, trained on filtered data, is more compliant but uncensored. It demonstrates improvements in instruction, conversation, coding, and function calling abilities.

FLUX.1 Redux [dev] is an image variation generation adapter for all FLUX.1 base models. It enables users to refine images with slight variations and supports text-based restyling via API. Integrated with FLUX1.1 [pro] Ultra, it allows for high-quality 4-megapixel outputs. The model can be used with Diffusers in Python for efficient image generation. While powerful, it has ethical and factual limitations and is governed by a non-commercial license.

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.

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

QVQ-72B-Preview is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities. QVQ-72B-Preview has achieved remarkable performance on various benchmarks. It scored a remarkable 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark

Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances.

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"input": "The quick brown fox jumps over the lazy dog"}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/canopylabs/orpheus-3b-0.1-ft'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


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

```bash
deepctl infer \
    -m 'canopylabs/orpheus-3b-0.1-ft'  \
    -i 'input=The quick brown fox jumps over the lazy dog'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of pythong code to generate an audio file from your text input:

```python
from pathlib import Path
from openai import OpenAI

client = OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                api_key="$DEEPINFRA_TOKEN")

speech_file_path = Path(__file__).parent / "speech.mp3"
with client.audio.speech.with_streaming_response.create(
  model="canopylabs/orpheus-3b-0.1-ft",
  voice="tara",
  input="The quick brown fox jumped over the lazy dog.",
  response_format="mp3",
) as response:
  response.stream_to_file(speech_file_path)
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of js code to generate an audio file from your text input:

```javascript
import fs from "fs";
import path from "path";
import OpenAI from "openai";

const openai = new OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                          api_key="$DEEPINFRA_TOKEN");

const speechFile = path.resolve("./speech.mp3");

async function main() {
  const mp3 = await openai.audio.speech.create({
    model: "canopylabs/orpheus-3b-0.1-ft",
    voice: "tara",
    input: "The quick brown fox jumped over the lazy dog.",
    response_format: "mp3",
  });
  console.log(speechFile);
  const buffer = Buffer.from(await mp3.arrayBuffer());
  await fs.promises.writeFile(speechFile, buffer);
}
main();
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example `curl` request to generate an audio file from your text input:

```bash
curl https://api.deepinfra.com/v1/openai/audio/speech \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "canopylabs/orpheus-3b-0.1-ft",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "tara",
    "response_format": "mp3"
  }' \
  --output speech.mp3
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `py` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/",
)
client.text_to_speech.convert(
    voice_id="tara",
    output_format="mp3",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="canopylabs/orpheus-3b-0.1-ft",
)
```

## Create Speech with Streaming

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
)
client.text_to_speech.convert_as_stream(
    voice_id="tara",
    output_format="pcm",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="canopylabs/orpheus-3b-0.1-ft",
)
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `js` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN", base_url: "https://api.deepinfra.com/" });
await client.textToSpeech.convert("tara", {
    output_format: "mp3",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "canopylabs/orpheus-3b-0.1-ft"
});
```

## Create Speech with Streaming

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN" });
await client.textToSpeech.convert("tara", {
    output_format: "pcm",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "canopylabs/orpheus-3b-0.1-ft"
});
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `curl` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/tara" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "canopylabs/orpheus-3b-0.1-ft",
  "output_format": "mp3",
}' --output speech.mp3
```

## Create Speech with Streaming

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/tara/stream" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "canopylabs/orpheus-3b-0.1-ft",
  "output_format": "pcm",
}' --output speech.pcm
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority (only applies to models that support it).

service_tier

input

voice

Select the desired format for the speech output. Supported formats include mp3, opus, flac, wav, and pcm.

response_format

temperature

top_p

Maximum number of tokens for the generation

max_tokens

repetition_penalty

stream

The webhook to call when inference is done, by default you will get the output in the response of your inference request

orpheus-3b-0.1-ft

Input

Output