Can I run gpt-oss-20b locally?

Yes. The weights are open under Apache 2.0 and the model fits within 16 GB of memory thanks to MXFP4 quantization of the MoE weights. It runs via Transformers or vLLM.

What does it cost and what is the context window?

Via API it is priced at $0.02 per million input tokens and $0.09 per million output tokens, with a 131,072-token context window.

How does gpt-oss-20b differ from gpt-oss-120b?

gpt-oss-20b is the lower-latency, lower-memory variant aimed at local or specialized deployment; gpt-oss-120b has 117B parameters and is intended for production, general-purpose and high-reasoning workloads on a single 80 GB GPU.

Gpt oss 20b

5516°

#llm #openai #reasoning #agents #coding #local

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Guides for this model

How to Install Ollama and Run a Local AI (Windows, Mac, Linux)

Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs

Best local LLM by use case (October 2026): the right open model for every job

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All guides

OpenAI | 131,072 token context | $0.02/M input, $0.09/M output. 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 and deployability on consumer or single-GPU hardware. 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.

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

gpt-oss-20b is OpenAI's smaller open-weight reasoning model: 21B parameters, MoE architecture, only 3.6B active per forward pass, and fits within 16 GB of memory. Apache 2.0 licensed, fine-tunable, and built for agentic work including function calling and structured outputs. It suits developers who want an OpenAI-trained reasoning model they can run locally or deploy cheaply without a usage agreement.

Known for

MoE design keeps active parameters at 3.6B, enabling fast, low-memory inference
Configurable reasoning effort: low, medium or high to trade latency for depth
Full chain-of-thought output visible to developers for debugging
Native function calling, tool use and structured outputs for agentic pipelines
Apache 2.0 licence: commercial use and fine-tuning with no copyleft restrictions

Watch out for

Must be used with OpenAI's Harmony response format; standard prompting will not work correctly.
Chain-of-thought output is for developer inspection only, not intended for end users.
Smaller sibling to gpt-oss-120b; for high-stakes reasoning tasks the larger model is the intended choice.

Best for

Prompts to try

Debug a function call chain

I have a Python agent that calls three tools in sequence: get_weather, get_forecast, and send_alert. The send_alert fires even when conditions are normal. Trace the likely logic error, suggest a fix, and show the corrected tool-call flow with proper conditional branching.

Step-by-step reasoning puzzle

A farmer has 17 sheep. All but 9 die. How many are left? Now explain why this question trips up most language models, what the correct answer is, and what reasoning pattern you used to avoid the trap.

Structured output extraction

Extract the following fields from this job posting as a JSON object: job_title, company, location, salary_range, required_years_experience, remote_policy. Job posting: Senior ML Engineer at Acme Corp, San Francisco or remote, $180k–$220k, 5+ years required, hybrid 2 days in office.

Compare with

GPT-6 Luna
API-only model
1,050,000 token context · $0.10/M input, $0.50/M output
GPT-5.6 Luna
API-only model
1,050,000 token context · $0.20/M input, $1.20/M output
GPT-5.6 Sol
API-only model
1,050,000 token context · $4.00/M input, $20.00/M output
GPT-6.1 Sol
API-only model
1,050,000 token context · $2.00/M input, $10.00/M output

More text models from openai

Questions people ask

Can I run gpt-oss-20b locally?: Yes. The weights are open under Apache 2.0 and the model fits within 16 GB of memory thanks to MXFP4 quantization of the MoE weights. It runs via Transformers or vLLM.
What does it cost and what is the context window?: Via API it is priced at $0.02 per million input tokens and $0.09 per million output tokens, with a 131,072-token context window.
How does gpt-oss-20b differ from gpt-oss-120b?: gpt-oss-20b is the lower-latency, lower-memory variant aimed at local or specialized deployment; gpt-oss-120b has 117B parameters and is intended for production, general-purpose and high-reasoning workloads on a single 80 GB GPU.

Captain's verdict
A genuinely open, fine-tunable reasoning model that fits on a 16 GB GPU — rare enough from any lab, let alone OpenAI.

Model card on Hugging Face

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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