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All models listed below are accessible through any of our SDKs. See the SDK pages for language-specific usage examples.

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Available Models

Below is a list of all models currently supported on Tinfoil, including their model IDs and types. The available models may change at any time.
Don’t see the model you need? Contact us at [email protected] and we’ll work with you to add support for your desired model.

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Chat Models

Description: Chat models support conversational AI capabilities through the standard chat completions API. All chat models follow the OpenAI chat completion format.

DeepSeek
DeepSeek R1
deepseek-r1-0528
Parameters: 671BContext: 128K tokensStrengths: State-of-the-art reasoning, advanced mathematical capabilities, enhanced function calling, reduced hallucination rateStructured Outputs: Structured response formatting supportBest for: Complex reasoning tasks, mathematical problem-solving, advanced coding, and tasks requiring deep analytical thinking

DeepSeek
DeepSeek V3.1 Terminus
deepseek-v31-terminus
Parameters: 685B (37B active)Context: 128K tokensStrengths: Hybrid reasoning with enhanced agentic capabilities, improved language consistency, multi-step tool orchestration, code generation with execution loops, complex web research workflowsStructured Outputs: Structured response formatting supportBest for: Agentic workflows requiring multi-step tool use, code generation with debugging, complex research tasks, and applications needing reliable structured output
🤖 Agentic Workflows: Dual operation modes for flexible reasoning depth vs. speed tradeoffs. Excels at multi-step tool orchestration and debugging loops.

OpenAI
GPT-OSS 120B
gpt-oss-120b
Parameters: 117B Context: 128K tokens
Strengths: Powerful reasoning, configurable reasoning effort levels, full chain-of-thought access, native agentic abilities including function calling, web browsing, and Python code execution
Structured Outputs: Structured response formatting support
Best for: Production use cases requiring high reasoning capabilities, agentic operations, and specialized applications

Mistral
Mistral Small 3.1 24B
mistral-small-3-1-24b
Context: 128K tokens
Strengths: Vision capabilities for image analysis, efficient performance, strong multilingual support
Multimodal Support: Supports image inputs (in base64) alongside text for vision tasks
Structured Outputs: Structured response formatting support
Best for: General-purpose chat applications, image analysis, and diverse language requirements
🖼️ Multimodal: See Image Processing Guide for vision examples.

Llama
Llama 3.3 70B
llama3-3-70b
Context: 128K tokens
Strengths: Multilingual understanding, dialogue optimization, strong reasoning
Structured Outputs: Structured response formatting support
Best for: Conversational AI applications and complex dialogue systems

Qwen
Qwen3 Coder 480B
qwen3-coder-480b
Parameters: 480B total (35B activated)
Context: 128K tokens
Strengths: Advanced agentic coding capabilities, repository-scale understanding, browser automation, exceptional tool calling with specialized function call format
Tool Calling: State-of-the-art agentic coding and function calling capabilities
Structured Outputs: Structured response formatting support
Best for: Agentic coding workflows, large codebase analysis, browser automation, complex multi-step programming tasks, repository-level code understanding
Advanced Agentic Coding: Optimized for CLINE, Qwen Code, and other agentic platforms. See Tool Calling Guide for implementation examples.

Qwen
Qwen 2.5 72B
qwen2-5-72b
Context: 128K tokens
Strengths: Exceptional function calling, multilingual capabilities, programming, mathematical reasoning, structured data processing
Tool Calling: Best-in-class function calling capabilities with high reliability
Structured Outputs: Structured response formatting support
Best for: Complex reasoning tasks, function calling workflows, coding applications, mathematical problems, long-form content generation
Excellent Tool Calling: See Tool Calling Guide for implementation examples.
Structured Outputs: All chat models support structured outputs for reliable data extraction and API integration. Full JSON schema validation available in Python, Node, and Go SDKs. See the Structured Outputs Guide for implementation examples.

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Audio Models

Description: Audio models provide speech-to-text transcription and text-to-speech synthesis capabilities. Supporting both audio file transcription and high-quality speech generation.

OpenAI
Whisper Large V3 Turbo
whisper-large-v3-turbo
Capabilities: Speech-to-text transcription
Strengths: Fast processing, high accuracy, multiple language support
Best for: Audio transcription, voice-to-text applications
Audio Format: Supports .mp3 and .wav files

Kokoro
Kokoro
kokoro
Capabilities: Text-to-speech synthesis
Strengths: High-quality speech generation, multiple voice options, natural-sounding output
Voices: Supports voice combinations like af_sky, af_bella, af_sky+af_bella
Best for: Text-to-speech applications, voice synthesis, audio content creation

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Embedding Models

Description: Embedding models convert text into high-dimensional vectors for semantic search, similarity comparisons, and other vector-based operations.

Nomic
Nomic Embed Text
nomic-embed-text
Dimensions: 768
Strengths: Open-source, outperforms OpenAI Ada-002 on benchmarks
Best for: Semantic search, document similarity, clustering

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Document Processing Models

Description: Document processing models handle file conversion, text extraction, and document parsing operations.

Docling
Docling Document Processing
docling
Capabilities: Document processing and conversion service
Strengths: PDF processing, Word document parsing, text extraction, format conversion with high accuracy
Best for: Document upload, processing, conversion, and text extraction workflows
📄 File Support: Supports PDF, Word documents, and other common document formats. See Document Processing Guide for usage examples.

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Using Models

To use any of these models, you’ll need:
  1. API Key: Get your key from the Tinfoil dashboard
  2. SDK: Install the SDK for your preferred language
  3. Model ID: Use the model ID from the cards above in your API requests
For detailed usage examples and code samples, see the SDK documentation: