openai/gpt-oss-20b
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
gpt-oss-20b
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Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
gpt-oss-120b— for production, general purpose, high reasoning use cases that fit into a single H100 GPU (117B parameters with 5.1B active parameters)gpt-oss-20b— for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
[!NOTE] This model card is dedicated to the larger
gpt-oss-120bmodel. Check outgpt-oss-20bfor the smaller model.
Highlights
- Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
- Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
- Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
- Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
- Agentic capabilities: Use the models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
- Native MXFP4 quantization: The models are trained with native MXFP4 precision for the MoE layer, making
gpt-oss-120brun on a single H100 GPU and thegpt-oss-20bmodel run within 16GB of memory.
Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
- Low: Fast responses for general dialogue.
- Medium: Balanced speed and detail.
- High: Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
Tool use
The gpt-oss models are excellent for:
- Web browsing (using built-in browsing tools)
- Function calling with defined schemas
- Agentic operations like browser tasks
Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This larger model gpt-oss-120b can be fine-tuned on a single H100 node, whereas the smaller gpt-oss-20b can even be fine-tuned on consumer hardware.
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