Available Models

Browse the foundation models available through W&B Inference

W&B Inference provides access to several open-source foundation models. Each model has different strengths and use cases.

Model catalog

Model	Model ID (for API usage)	Type	Context Window	Parameters	Description
DeepSeek R1-0528	deepseek-ai/DeepSeek-R1-0528	Text	161K	37B-680B (Active-Total)	Optimized for precise reasoning tasks including complex coding, math, and structured document analysis
DeepSeek V3-0324	deepseek-ai/DeepSeek-V3-0324	Text	161K	37B-680B (Active-Total)	Robust Mixture-of-Experts model tailored for high-complexity language processing and comprehensive document analysis
DeepSeek V3.1	deepseek-ai/DeepSeek-V3.1	Text	128K	37B-671B (Active-Total)	A large hybrid model that supports both thinking and non-thinking modes via prompt templates
Meta Llama 3.1 8B	meta-llama/Llama-3.1-8B-Instruct	Text	128K	8B (Total)	Efficient conversational model optimized for responsive multilingual chatbot interactions
Meta Llama 3.1 70B	meta-llama/Llama-3.1-70B-Instruct	Text	128K	70B (Total)	Efficient conversational model optimized for responsive multilingual chatbot interactions
Meta Llama 3.3 70B	meta-llama/Llama-3.3-70B-Instruct	Text	128K	70B (Total)	Multilingual model excelling in conversational tasks, detailed instruction-following, and coding
Meta Llama 4 Scout	meta-llama/Llama-4-Scout-17B-16E-Instruct	Text, Vision	64K	17B-109B (Active-Total)	Multi-modal model integrating text and image understanding, ideal for visual tasks and combined analysis
Microsoft Phi 4 Mini 3.8B	microsoft/Phi-4-mini-instruct	Text	128K	3.8B (Active-Total)	Compact, efficient model ideal for fast responses in resource-constrained environments
Moonshot AI Kimi K2	moonshotai/Kimi-K2-Instruct	Text	128K	32B-1T (Active-Total)	Mixture-of-Experts model optimized for complex tool use, reasoning, and code synthesis
Moonshot AI Kimi K2 Instruct 0905	moonshotai/Kimi-K2-Instruct-0905	Text	262K	32B-1T	Latest version of Kimi K2 mixture-of-experts language model, featuring 32 billion activated parameters and a total of 1 trillion parameters
OpenAI GPT OSS 20B	openai/gpt-oss-20b	Text	131K	3.6B-20B (Active-Total)	Lower latency Mixture-of-Experts model trained on OpenAI’s Harmony response format with reasoning capabilities
OpenAI GPT OSS 120B	openai/gpt-oss-120b	Text	131K	5.1B-117B (Active-Total)	Efficient Mixture-of-Experts model designed for high-reasoning, agentic and general-purpose use cases
OpenPipe Qwen3 14B Instruct	OpenPipe/Qwen3-14B-Instruct	Text	32.8K	14.8B (Active-Total)	An efficient multilingual, dense, instruction-tuned model, optimized by OpenPipe for building agents with finetuning.
Qwen2.5 14B Instruct	Qwen/Qwen2.5-14B-Instruct	Text	32.8K	14.7B-14.7B (Active-Total)	Dense multilingual instruction-tuned model with tool-use and structured output support
Qwen3 235B A22B Thinking-2507	Qwen/Qwen3-235B-A22B-Thinking-2507	Text	262K	22B-235B (Active-Total)	High-performance Mixture-of-Experts model optimized for structured reasoning, math, and long-form generation
Qwen3 235B A22B-2507	Qwen/Qwen3-235B-A22B-Instruct-2507	Text	262K	22B-235B (Active-Total)	Efficient multilingual, Mixture-of-Experts, instruction-tuned model, optimized for logical reasoning
Qwen3 Coder 480B A35B	Qwen/Qwen3-Coder-480B-A35B-Instruct	Text	262K	35B-480B (Active-Total)	Mixture-of-Experts model optimized for coding tasks such as function calling, tooling use, and long-context reasoning
Z.AI GLM 4.5	zai-org/GLM-4.5	Text	131K	32B-355B (Active-Total)	Mixture-of-Experts model with user-controllable thinking/non-thinking modes for reasoning, code, and agents

Using model IDs

When using the API, specify the model using its ID from the table above. For example:

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[...]
)

Next steps

• Check usage limits and pricing for each model
• See API reference for how to use these models
• Try models in the W&B Playground