DeepSeek V4 Pro is an MoE model with 1.6T total parameters (49B active) and a 1M-token context window. It's built for advanced reasoning, coding, and long-running agent tasks, and performs well on knowledge, math, and software engineering benchmarks.

DeepSeek-V4-Pro

DeepSeek V4 Flash is an efficiency-focused MoE model with 284B total parameters (13B active) and a 1M-token context window. It's tuned for fast inference and high-throughput use cases while still holding up on reasoning and coding tasks.

DeepSeek-V4-Flash

Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.

Kimi-K2.6

Qwen3.6-35B-A3B is Alibaba's latest flagship Mixture-of-Experts model, with 35B total parameters and only 3B activated per token (256 experts, 8 routed + 1 shared). Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.

Qwen3.6-35B-A3B

GLM-5.1 is Z-AI's next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

GLM-5.1

Step 3.5 Flash is an open-source reasoning model by StepFun with 196B total parameters (11B active) using Mixture of Experts. It features a 256K context window, deep reasoning, tool calling, and agentic capabilities, achieving 97.3 on AIME 2025 and 74.4% on SWE-bench Verified.

Step-3.5-Flash

Qwen3.5-397B-A17B is Alibaba's most capable Qwen3.5 model, a Mixture-of-Experts architecture with 397B total parameters and 17B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling with MCP integration, and support for 201 languages. Sets state-of-the-art results on reasoning, coding, math, and multimodal benchmarks.

Qwen3.5-397B-A17B

Efficient, MoE variant of Gemma 4. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

gemma-4-26B-A4B-it

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

gemma-4-31B-it

Qwen3.5-122B-A10B is a large Mixture-of-Experts model from Alibaba's Qwen3.5 series with 122B total parameters and 10B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling, and support for 201 languages. Excels at complex reasoning, coding, multimodal understanding, and agentic tasks with the efficiency of sparse activation.

Qwen3.5-122B-A10B

Qwen3.5-35B-A3B is an efficient Mixture-of-Experts model from Alibaba's Qwen3.5 series with 35B total parameters and only 3B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling, and support for 201 languages. Delivers strong performance on reasoning, coding, and vision-language tasks at a fraction of the compute cost.

Qwen3.5-35B-A3B

Qwen3.5-27B is Alibaba's largest dense Qwen3.5 model, delivering near-frontier quality across reasoning, coding, and instruction following. It features a 262K token context window (extensible to 1M), thinking/reasoning mode, tool calling, multi-token prediction, and support for 201 languages. Best suited for production deployments and complex enterprise tasks requiring top-tier performance.

Qwen3.5-27B

Qwen3.5-4B is a mid-size model from Alibaba's Qwen3.5 series that delivers a strong balance of performance and efficiency. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling, and support for 201 languages. Well-suited for complex reasoning, code generation, and agentic applications.

Qwen3.5-4B

Qwen3.5-2B is a compact yet capable model from Alibaba's Qwen3.5 series. It features a 262K token context window, support for 201 languages, thinking/reasoning mode, and tool calling for agentic workflows. A strong choice for prototyping, fine-tuning, and efficient multilingual deployments.

Qwen3.5-2B

Qwen3.5-0.8B is Alibaba's smallest model in the Qwen3.5 series, featuring a hybrid Gated Delta Networks and sparse Mixture-of-Experts architecture. Despite its compact size, it supports a 262K token context window, 201 languages, thinking/reasoning mode, and tool calling. Ideal for edge deployments, resource-constrained environments, and lightweight inference tasks.

Qwen3.5-0.8B

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

NVIDIA-Nemotron-3-Super-120B-A12B

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

  Qwen3-TTS is an advanced text-to-speech model by Alibaba's Qwen team, delivering stable, expressive, and low-latency speech generation across 10 languages.                                                                                                                                                                                                                                                                                                                                           Key capabilities:                                                                                                                                                                                                                                  - 9 preset voices — Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee — covering diverse genders, ages, and accents                                                                                                              - Voice cloning — clone any voice from a short (~3s) audio sample via the voice_id parameter   - Instruction control — adjust tone, emotion, and speaking style with natural language (e.g. "speak slowly and calmly", "excited tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese   - Streaming support — real-time PCM streaming with ~97ms first-byte latency   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on a 1.7B parameter architecture using discrete multi-codebook language modeling for end-to-end speech synthesis without cascading errors. Uses a custom 12Hz acoustic tokenizer that preserves paralinguistic information and   environmental audio details.

Qwen3-TTS

● Qwen3-TTS-VoiceDesign is a voice design variant of Qwen3-TTS by Alibaba's Qwen team. Instead of selecting from preset voices, you describe the voice you want in natural language — and the model generates speech in that voice.                                                                                                                                                                                                                                                                     Key capabilities:                                                                                                                                                                                                                                  - Natural language voice control — describe any voice with free text (e.g. "a deep male voice with a calm, authoritative presence", "a young cheerful female with a warm and friendly tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese                                                                                                                                         - Streaming support — real-time PCM streaming   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on the same 1.7B parameter architecture as Qwen3-TTS, using discrete multi-codebook language modeling and a custom 12Hz acoustic tokenizer for high-quality end-to-end speech synthesis.

Qwen3-TTS-VoiceDesign

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

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.

Qwen3-Max

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

Qwen3-Max-Thinking

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

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

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.
FLUX 2 represents the next generation of the Flux family, introducing significantly improved image quality, faster generation, and more precise prompt following. The lineup includes Max for ultimate quality, Pro for production workflows, and Klein variants (9B and 4B) optimized for speed and efficiency — making high-quality image generation accessible at every scale.
Flux offers specialized variants for every need: 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

[NVIDIA NemotronTM](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) is a family of open models, datasets, and training recipes engineered for high performance, efficiency and customization. Nemotron models support synthetic data workflows and supervised fine-tuning — and are equally optimized for real-time inference, reasoning agents, and production AI systems.

nemotron

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

The Nemotron family spans Nano, Super, and specialized instruct variants, enabling you to balance accuracy, reasoning depth, latency, and cost for your specific workload.
- **Nano** for maximum efficiency and stable inference
- **Super** for multi-agent systems and advance reasoning
- **Instruct variants** for instruction-following and conversational 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

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.

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. 

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.

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

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

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

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).

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.

🥳 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.

Latest version of the Airoboros model fine-tunned version of llama-2-70b using the Airoboros dataset. This model is currently running jondurbin/airoboros-l2-70b-2.2.1 

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.

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

Wan2.6 text to image, Upgraded visual quality, aesthetics, and instruction-following deliver precise style control, realistic portraits, long-text understanding, and broad historical/cultural IP coverage, enabling high-quality, highly expressive visual generation.

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.

The CLIP model was developed by OpenAI to investigate the robustness of computer vision models. It uses a Vision Transformer architecture and was trained on a large dataset of image-caption pairs. The model shows promise in various computer vision tasks but also has limitations, including difficulties with fine-grained classification and potential biases in certain applications.

Euryale 70B v2.1 is a model focused on creative roleplay from Sao10k

Bria Eraser enables precise removal of unwanted objects from images while maintaining high-quality outputs. Trained exclusively on licensed data for safe and risk-free commercial use

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.

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.

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.

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.

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

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.

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.

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.

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.

Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following: - Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian - Vision: English - Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese

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.

Optimized specifically for multimodal agent scenarios. It features enhanced agent capabilities, upgraded multimodal comprehension, and more flexible context management.

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 Scout, a 17 billion parameter model with 16 experts

At 8 billion parameters, with superior quality and prompt adherence, this base model is the most powerful in the Stable Diffusion family. This model is ideal for professional use cases at 1 megapixel resolution

OpenChat is a library of open-source language models that have been fine-tuned with C-RLFT, a strategy inspired by offline reinforcement learning. These models can learn from mixed-quality data without preference labels and have achieved exceptional performance comparable to ChatGPT. The developers of OpenChat are dedicated to creating a high-performance, commercially viable, open-source large language model and are continuously making progress towards this goal.

NVIDIA Nemotron 3 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compute cost while maintaining strong reasoning quality.

DeepSeek-R1-Distill-Llama-70B is a highly efficient language model that leverages knowledge distillation to achieve state-of-the-art performance. This model distills the reasoning patterns of larger models into a smaller, more agile architecture, resulting in exceptional results on benchmarks like AIME 2024, MATH-500, and LiveCodeBench. With 70 billion parameters, DeepSeek-R1-Distill-Llama-70B offers a unique balance of accuracy and efficiency, making it an ideal choice for a wide range of natural language processing tasks. 

Real-time AI video generation from text, images, and audio. Supports up to 1080p at 48 FPS with built-in audio generation, draft mode for 4x faster previews, and prompt upsampling.

PaddleOCR-VL is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.

Veo 3 is a state-of-the-art 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.

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.

The 1080p high-fidelity mode in PixVerse renders videos with significantly enhanced sharpness and visual clarity, capturing intricate details and providing a crisp, professional-grade quality suitable for more polished projects.

BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned

Anthropic’s most powerful model yet and the state-of-the-art coding model. It delivers sustained performance on long-running tasks that require focused effort and thousands of steps, significantly expanding what AI agents can solve. Claude Opus 4 is ideal for powering frontier agent products and features.

Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.  Its ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.

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)

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.

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

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.

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

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.

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.

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.

Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and extends context capabilities up to 128K tokens while maintaining top-tier text performance. Its 24 billion parameters and instruction fine-tuning deliver fast, local deployment for both text and vision tasks.

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.

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

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.

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.

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.

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

gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.

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.

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. 

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

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.

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.

A sentence similarity model that can be used for various NLP tasks such as text classification, sentiment analysis, named entity recognition, question answering, and more. It utilizes the CoSENT architecture, which consists of a transformer encoder and a pooling module, to encode input texts into vectors that capture their semantic meaning. The model was trained on the nli_zh dataset and achieved high performance on various benchmark datasets.

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.

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.

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.

Phind-CodeLlama-34B-v2 is an open-source language model that has been fine-tuned on 1.5B tokens of high-quality programming-related data and achieved a pass@1 rate of 73.8% on HumanEval. It is multi-lingual and proficient in Python, C/C++, TypeScript, Java, and more. It has been trained on a proprietary dataset of instruction-answer pairs instead of code completion examples.  The model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use. It accepts the Alpaca/Vicuna instruction format and can generate one completion for each prompt.

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

Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models Qwen3-Next.

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.

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.

The Wan2.2 T2V A14B is a next-generation 14B-parameter video foundation model by Wan-AI featuring a novel two-stage denoising architecture. It produces 480P videos with improved visual coherence and detail, generating 2 or 5 second clips at 16fps from text prompts.

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.

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.

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

Model Details 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.

MiniMax-M2.1 is a model optimized specifically for robustness in coding, tool use, instruction following, and long-horizon planning. From automating multilingual software development to executing complex, multi-step office workflows, MiniMax-M2.1 empowers developers to build the next generation of autonomous applications—all while being fully transparent, controllable, and accessible.

This is a 32B reasoning model trained from Qwen2.5-32B-Instruct with 17K data. The performance is on par with o1-preview model on both math and coding.

DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats.

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

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.

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

```bash
curl -X POST \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -F audio=@my_voice.mp3  \
    'https://api.deepinfra.com/v1/inference/openai/whisper-large-v3'
```

which will give you back something similar to:

```json
{
  "text": "",
  "segments": [
    {
      "end": 1.0,
      "id": 0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "id": 1,
      "start": 4.0,
      "text": "World"
    }
  ],
  "language": "en",
  "input_length_ms": 0,
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "duration": 0.0,
  "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 'openai/whisper-large-v3'  \
    -i audio=@my_voice.mp3
```

which will give you back something similar to:

```json
{
  "text": "",
  "segments": [
    {
      "end": 1.0,
      "id": 0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "id": 1,
      "start": 4.0,
      "text": "World"
    }
  ],
  "language": "en",
  "input_length_ms": 0,
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "duration": 0.0,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


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

You can install it with

```bash
npm install deepinfra
```

and then

```javascript
import { AutomaticSpeechRecognition } from "deepinfra";
import path from "path";
import { fileURLToPath } from 'url';


const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);

const DEEPINFRA_API_KEY = "$DEEPINFRA_TOKEN";
const MODEL = "openai/whisper-large-v3";

const main = async () => {
  const client = new AutomaticSpeechRecognition(MODEL, DEEPINFRA_API_KEY);

  const input = {
    audio: path.join(__dirname, "audio.mp3"),
  };
  const response = await client.generate(input);
  console.log(response.text);
};

main();
```


You can POST to our OpenAI Transcriptions and Translations compatible endpoint.

# Create transcription

For a given audio file and model, the endpoint will return the **transcription object** or a **verbose transcription object**.

## Request body

- **file** (Required): The audio file object to transcribe. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **language** (Optional): The language of the input audio. Supplying the input language in ISO-639-1 format can improve accuracy and latency.
- **prompt** (Optional): An optional text prompt to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): Controls the sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 make it more focused and deterministic. If set to 0, the model will adjust automatically to increase temperature as needed.
- **timestamp_granularities[]** (Optional): Specifies the timestamp granularity for transcription. Requires `response_format` to be set to `verbose_json`. Options: `word` - generates timestamps for individual words, `segment` - generates timestamps for segments. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency.

## Response body

The transcription object or a verbose transcription object.

### Basic request

```bash
curl "https://api.deepinfra.com/v1/openai/audio/transcriptions" \
 -H "Content-Type: multipart/form-data" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -F file="@/path/to/file/audio.mp3" \
 -F model="openai/whisper-large-v3"
```
```json
{
 "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
}
```

### Word timestamp request

```bash
curl "https://api.deepinfra.com/v1/openai/audio/transcriptions" \
 -H "Content-Type: multipart/form-data" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -F file="@/path/to/file/audio.mp3" \
 -F model="openai/whisper-large-v3" \
 -F response_format="verbose_json" \
 -F "timestamp_granularities[]=word"
```

```json
{
 "task": "transcribe",
 "language": "english",
 "duration": 8.470000267028809,
 "text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
 "words": [
 {
 "word": "The",
 "start": 0.0,
 "end": 0.23999999463558197
 },
 ...
 {
 "word": "volleyball",
 "start": 7.400000095367432,
 "end": 7.900000095367432
 }
 ]
}
```

### Segment timestamp request

```bash
curl "https://api.deepinfra.com/v1/openai/audio/transcriptions" \
 -H "Content-Type: multipart/form-data" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -F file="@/path/to/file/audio.mp3" \
 -F model="openai/whisper-large-v3" \
 -F response_format="verbose_json" \
 -F "timestamp_granularities[]=segment"
```

```json
{
 "task": "transcribe",
 "language": "english",
 "duration": 8.470000267028809,
 "text": "The beach was a popular spot on a hot summer day. People were swimming in the ocean, building sandcastles, and playing beach volleyball.",
 "segments": [
 {
 "id": 0,
 "seek": 0,
 "start": 0.0,
 "end": 3.319999933242798,
 "text": " The beach was a popular spot on a hot summer day.",
 "tokens": [
 50364, 440, 7534, 390, 257, 3743, 4008, 322, 257, 2368, 4266, 786, 13, 50530
 ],
 "temperature": 0.0,
 "avg_logprob": -0.2860786020755768,
 "compression_ratio": 1.2363636493682861,
 "no_speech_prob": 0.00985979475080967
 },
 ...
 ]
}
```

# Create translation

For a given audio file and model, the endpoint will return the translated text to English.

## Request body

- **file** (Required): The audio file object to translate. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **prompt** (Optional): An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

## Response body

The translated text to English.

### Basic request

```bash
curl "https://api.deepinfra.com/v1/openai/audio/translations" \
 -H "Content-Type: multipart/form-data" \
 -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
 -F file="@/path/to/file/german.m4a" \
 -F model="openai/whisper-large-v3"
```

```json
{
 "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
```

You can use OpenAI's Python SDK to interact with our OpenAI Transcriptions and Translations compatible endpoint.

# Create transcription

For a given audio file and model, the endpoint will return the **transcription object** or a **verbose transcription object**.

## Request body

- **file** (Required): The audio file object to transcribe. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **language** (Optional): The language of the input audio. Supplying the input language in ISO-639-1 format can improve accuracy and latency.
- **prompt** (Optional): An optional text prompt to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): Controls the sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 make it more focused and deterministic. If set to 0, the model will adjust automatically to increase temperature as needed.
- **timestamp_granularities[]** (Optional): Specifies the timestamp granularity for transcription. Requires `response_format` to be set to `verbose_json`. Options: `word` - generates timestamps for individual words, `segment` - generates timestamps for segments. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency.

## Response body

The transcription object or a verbose transcription object.

### Example

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

audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
 model="whisper-1",
 file=audio_file
)
```
```json
{
 "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
}
```

# Create translation

For a given audio file and model, the endpoint will return the translated text to English.

## Request body

- **file** (Required): The audio file object to translate. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **prompt** (Optional): An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

## Response body

The translated text to English.

### Basic request

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

audio_file = open("speech.mp3", "rb")
transcript = client.audio.translations.create(
 model="whisper-1",
 file=audio_file
)
```

```json
{
 "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
```

You can use OpenAI's JavaScript SDK to interact with our OpenAI Transcriptions and Translations compatible endpoint.

```bash
npm install openai
```

# Create transcription

For a given audio file and model, the endpoint will return the **transcription object** or a **verbose transcription object**.

## Request body

- **file** (Required): The audio file object to transcribe. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **language** (Optional): The language of the input audio. Supplying the input language in ISO-639-1 format can improve accuracy and latency.
- **prompt** (Optional): An optional text prompt to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): Controls the sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 make it more focused and deterministic. If set to 0, the model will adjust automatically to increase temperature as needed.
- **timestamp_granularities[]** (Optional): Specifies the timestamp granularity for transcription. Requires `response_format` to be set to `verbose_json`. Options: `word` - generates timestamps for individual words, `segment` - generates timestamps for segments. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency.

## Response body

The transcription object or a verbose transcription object.

### Example

```js
import fs from "fs";
import OpenAI from "openai";

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

async function main() {
 const translation = await openai.audio.translations.create({
 file: fs.createReadStream("speech.mp3"),
 model: "openai/whisper-large-v3",
 });

 console.log(translation.text);
}
main();
```
```json
{
 "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
```

# Create translation

For a given audio file and model, the endpoint will return the translated text to English.

## Request body

- **file** (Required): The audio file object to translate. Supported formats are `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, and `webm`.
- **model** (Required): ID of the model to use. Only `openai/whisper-large-v3` for this case. For other models, refer to [models/automatic-speech-recognition](models/automatic-speech-recognition).
- **prompt** (Optional): An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.
- **response_format** (Optional): The format of the output. Options include: `json` (default), `text`, `srt`, `verbose_json`, `vtt`.
- **temperature** (Optional): The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

## Response body

The translated text to English.

### Basic request

```js
import fs from "fs";
import OpenAI from "openai";

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

async function main() {
 const translation = await openai.audio.translations.create({
 file: fs.createReadStream("speech.mp3"),
 model: "openai/whisper-large-v3",
 });

 console.log(translation.text);
}
main();
```

```json
{
 "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}
```