> ## Documentation Index > Fetch the complete documentation index at: https://docs.wandb.ai/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. ## Generally available models The following models are [generally available](/inference/lifecycle#model-lifecycle-stages): | Model | Model ID (for API usage) | Type | Context Window | Parameters | Description | | ----------------------------- | ---------------------------------------------- | ------------ | -------------- | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | 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. | | Google Gemma 4 31B | `google/gemma-4-31B-it` | Text, Vision | 262k | 31B (Total) | Gemma 4 31B Dense is designed for advanced reasoning, agentic workflows, and longer context and is natively trained on 140+ languages. | | IBM Granite 4.1 8B | `ibm-granite/granite-4.1-8b` | Text | 131k | 8B (Total) | Granite 4.1 8B is a long-context instruct model capable of enhanced tool calling, instruction following, and chat capabilities. | | 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. | | MiniMax M2.5 | `MiniMaxAI/MiniMax-M2.5` | Text | 197k | 10B-230B (Active-Total) | MoE model with a highly sparse architecture designed for high-throughput and low latency with strong coding capabilities. | | Moonshot AI Kimi K2.6 | `moonshotai/Kimi-K2.6` | Text, Vision | 262k | 32B-1T (Active-Total) | Kimi K2.6 is a multimodal Mixture-of-Experts language model featuring 32 billion activated parameters and a total of 1 trillion parameters. | | 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. | | NVIDIA Nemotron 3 Super 120B | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8` | Text | 262k | 12B-120B (Active-Total) | Nemotron 3 is a LatentMoE model designed to deliver strong agentic, reasoning, and conversational 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. | | 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. | | Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` | Text, Vision | 262k | 3B-35B (Active-Total) | Qwen3.5-35B-A3B is an open-weights multimodal MoE model built for efficient, high-throughput inference across chat, reasoning, and agentic tasks. | | 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 5.1 | `zai-org/GLM-5.1` | Text | 203k | 40B-744B (Active-Total) | Powerful MoE model for long-horizon agentic engineering and advanced reasoning. | ## Experimental models The following models are [experimental](/inference/lifecycle#model-lifecycle-stages): | Model | Model ID (for API usage) | Type | Context Window | Parameters | Description | | ----------------- | ------------------------------- | ------------ | -------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------- | | DeepSeek V4-Flash | `deepseek-ai/DeepSeek-V4-Flash` | Text | 1000k | 13B-284B (Active-Total) | DeepSeek V4-Flash is an MoE model with 1M context length great for coding, reasoning, and agentic workloads. | | Qwen3.5 27B | `Qwen/Qwen3.5-27B` | Text, Vision | 262k | 27B (Total) | Qwen3.5-27B is a dense model from the Qwen3.5 family built for high performance across a large range of benchmarks. | ## Deprecated models The following models are [deprecated](/inference/lifecycle#model-lifecycle-stages): | Model | Model ID (for API usage) | Type | Context Window | Parameters | Description | | ---------- | ------------------------ | ---- | -------------- | ----------------------- | -------------------------------------------------------------------------------------------------------- | | 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. | ## 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/)