Available models

Browse the foundation models available through Serverless Inference

Serverless 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:

Model	Model ID (for API usage)	Type	Context Window	Parameters	Description
DeepSeek V4-Flash	deepseek-ai/DeepSeek-V4-Flash	Text	1049k	13B-284B (Active-Total)	DeepSeek V4-Flash is an MoE model with 1M context length great for coding, reasoning, and agentic workloads.
DeepSeek V4-Pro	deepseek-ai/DeepSeek-V4-Pro	Text	1049k	49B-1.6T (Active-Total)	DeepSeek V4-Pro is a 1.6T-parameter MoE model with 49B active parameters excelling at advanced reasoning, coding, and complex agentic workloads.
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.
JetBrains Mellum2 12B A2.5B	JetBrains/Mellum2-12B-A2.5B-Instruct	Text	131k	2.5B-12B (Active-Total)	Mellum2-12B-A2.5B-Instruct is a fast MoE model with 131K context built for coding, tool use, and low-latency AI workflows.
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.
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 K2.7 Code	moonshotai/Kimi-K2.7-Code	Text, Vision	262k	32B-1T (Active-Total)	Kimi K2.7 Code is a 1T-parameter MoE model with 32B active parameters purpose-built for long-horizon agentic coding and software engineering.
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.
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.
NVIDIA Nemotron 3 Ultra	nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B	Text	262k	55B-550B (Active-Total)	Nemotron 3 Ultra is a powerful MoE model designed for long-running agents across coding, deep research, and enterprise automation.
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.
Qwen3.6 35B A3B	Qwen/Qwen3.6-35B-A3B	Text, Vision	262k	3B-35B (Active-Total)	Qwen3.6-35B-A3B is an MoE multimodal model with 262K context optimized for agentic coding workflows.
Qwen3.6 27B	Qwen/Qwen3.6-27B	Text, Vision	262k	27B (Total)	Qwen3.6-27B is a 27B dense multimodal model with 262K context built for flagship-level agentic coding.
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 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 5.2	zai-org/GLM-5.2	Text	262k	40B-744B (Active-Total)	GLM-5.2 is a Mixture-of-Experts language model featuring 40 billion activated parameters and a total of 744 billion parameters.
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:

None currently

Deprecated models

The following models are deprecated:

Model	Model ID (for API usage)	Type	Context Window	Parameters	Description
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.
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.
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.

Use model IDs

To specify a model when calling the API, use its Model ID from the preceding tables. For example:

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

Next steps

After you’ve chosen a model, continue with one of the following resources:

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