> ## Documentation Index > Fetch the complete documentation index at: https://wb-21fd5541-serverless-sft-revamp.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 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 | 161K | 37B-671B (Active-Total) | A large hybrid model that supports both thinking and non-thinking modes via prompt templates. | | Meta Llama 4 Scout | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Text, Vision | 64K | 17B-109B (Active-Total) | Multimodal model integrating text and image understanding, ideal for visual tasks and combined analysis. | | 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 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 3.1 8B | `meta-llama/Llama-3.1-8B-Instruct` | Text | 128K | 8B (Total) | Efficient conversational model optimized for responsive multilingual chatbot interactions. | | Microsoft Phi 4 Mini 3.8B | `microsoft/Phi-4-mini-instruct` | Text | 128K | 3.8B (Total) | Compact, efficient model ideal for fast responses in resource-constrained environments. | | Moonshot AI Kimi K2.5 | `moonshotai/Kimi-K2.5` | Text, Vision | 262K | 32B-1T (Active-Total) | Kimi K2.5 is a multimodal Mixture-of-Experts language model featuring 32 billion activated parameters and a total of 1 trillion parameters. | | 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. | | 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. | | OpenPipe Qwen3 14B Instruct | `OpenPipe/Qwen3-14B-Instruct` | Text | 32.8K | 14.8B (Total) | An efficient multilingual, dense, instruction-tuned model, optimized by OpenPipe for building agents with finetuning. | | 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 30B A3B | `Qwen/Qwen3-30B-A3B-Instruct-2507` | Text | 262K | 3.3B-30.5B (Active-Total) | Qwen3-30B-A3B-Instruct-2507 is a 30.5B MoE instruction-tuned model with enhanced reasoning, coding, and long-context understanding. | | Qwen3 Coder 480B A35B | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | Text | 262K | 35B-480B (Active-Total) | Mixture-of-Experts model optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning. | | Z.AI GLM 5 | `zai-org/GLM-5-FP8` | Text | 200K | 40B-744B (Active-Total) | Mixture-of-Experts model for long-horizon agentic tasks with strong performance on reasoning and coding. | | Moonshot AI Kimi K2 (deprecated) | `moonshotai/Kimi-K2-Instruct` | Text | 131K | 32B-1T (Active-Total) | Mixture-of-Experts model optimized for complex tool use, reasoning, and code synthesis. | | Moonshot AI Kimi K2 Instruct 0905 (deprecated) | `moonshotai/Kimi-K2-Instruct-0905` | Text | 262K | 32B-1T (Active-Total) | Latest version of Kimi K2 mixture-of-experts language model, featuring 32 billion activated parameters and a total of 1 trillion parameters. | | Qwen2.5 14B Instruct (deprecated) | `Qwen/Qwen2.5-14B-Instruct` | Text | 32.8K | 14.7B (Total) | Dense multilingual instruction-tuned model with tool-use and structured output support. | | Z.AI GLM 4.5 (deprecated) | `zai-org/GLM-4.5` | Text | 131K | 32B-355B (Active-Total) | Mixture-of-Experts model with user-controllable thinking/non-thinking modes for strong reasoning, code generation, and agent alignment. | ## Using model IDs When using the API, specify the model using its `Model ID` from the table above. For example: ```python theme={null} response = client.chat.completions.create( model="meta-llama/Llama-3.1-8B-Instruct", messages=[...] ) ``` ## Next steps * Check [usage limits and pricing](/inference/usage-limits/) for each model * See [API reference](/inference/api-reference/) for how to use these models * Try models in the [W\&B Playground](/inference/ui-guide/)