gpt-oss-20b

// Overview

gpt-oss-20b is the smaller of OpenAI's two open-weight language models, released August 5, 2025 alongside gpt-oss-120b. It is a 20.9B-parameter Mixture-of-Experts transformer that activates only about 3.6B parameters per token, and it ships under the permissive Apache 2.0 license — part of the first open-weight model release from OpenAI since GPT-2. The weights are freely downloadable from Hugging Face and the openai/gpt-oss GitHub repository.

The headline feature of gpt-oss-20b is that it fits in roughly 16GB of memory. Its MoE weights are quantized to MXFP4 out of the box (a 12.8 GiB checkpoint), so the model runs on a single high-end laptop or consumer GPU rather than a datacenter card. That makes it OpenAI's pick for on-device reasoning, local inference, and rapid iteration. OpenAI reports it delivers results similar to its proprietary o3-mini on common benchmarks, even edging it out on competition math and health questions.

Like its larger sibling, gpt-oss-20b exposes configurable reasoning effort (low / medium / high) with visible chain-of-thought, and natively supports function calling, web browsing, Python tool use, and structured outputs via OpenAI's harmony response format. It is text-only, has a June 2024 knowledge cutoff and a 131,072-token context window. Because it is open-weight rather than served first-party, it runs across many hosts — Ollama, LM Studio, vLLM, llama.cpp, Hugging Face, OpenRouter, Fireworks, Together, AWS, Azure and others — each setting its own price, with several offering a free tier.

// Benchmarks

1. MMLU85.3%
2. GPQA Diamond (no tools)71.5%
3. AIME 2024 (no tools)92.1%
4. AIME 2025 (with tools)98.7%
5. SWE-bench Verified60.7%
6. HealthBench42.5%
7. Humanity's Last Exam (with tools)17.3%
8. Tau-Bench Retail54.8%

Scores on a 0–100 scale (25-point gridlines); higher is better. Each benchmark links to its published source.

// Pricing

gpt-oss-20b is open-weight, so it is not served first-party by OpenAI — prices vary by host. Figures shown are a representative low rate from OpenRouter providers; several hosts also offer a free tier (e.g. openai/gpt-oss-20b:free). Self-hosting incurs only your own compute cost.

Pricing source ↗

// Strengths

• Runs on-device in ~16GB of memory thanks to native MXFP4 quantization (12.8 GiB checkpoint)
• Strong reasoning for its size — comparable to OpenAI o3-mini, beating it on competition math and health
• Permissive Apache 2.0 license allowing commercial use, fine-tuning, and self-hosting
• Efficient MoE design: only ~3.6B of 20.9B parameters active per token for low latency
• Configurable reasoning effort (low/medium/high) with full chain-of-thought visibility
• Native agentic tooling: function calling, web browsing, Python execution, structured outputs
• 131K-token context window for long documents and agent traces
• Fine-tunable on consumer hardware with no vendor lock-in

// Best for

• On-device and local reasoning assistants that keep data off the cloud
• Edge and offline deployments where a 16GB memory footprint is the constraint
• Cost-controlled high-volume inference via self-hosting or cheap third-party providers
• Agentic workflows needing tool calling, browsing, and code execution at low latency
• Fine-tuning an open reasoning model for domain-specific applications
• Math, science, and coding tasks where o3-mini-class quality is enough
• Rapid prototyping and research into chain-of-thought with fully visible reasoning traces

// How to access

// gpt-oss (Open Weight) — every version

The full lineage of the gpt-oss (Open Weight) line, newest first. Every version has its own page — click any to compare specs, benchmarks and pricing.