> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-feat-cli-docs-generator.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         | 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:

```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/)