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gpt-oss-120b

gpt-oss-120b

gpt-oss-120b · Family: gpt-oss
OpenAI open-weight reasoning model (MoE, 117B/5.1B active) under Apache 2.0; fits on a single 80GB GPU. Configurable reasoning effort.
✓ Active✓ Public access⚖ Open weightsReasoning modelLLMTool-using model📁 gpt-oss
Context window
128K
tokens
Parameters
117B (5,1B aktywnych)
parameters
Max output
131,072
tokens
Release date
5 August 2025
Access:APIDownloadHostedDeployment:💻 Local☁ Cloud

Overview

gpt-oss-120b is an open-weight reasoning model from OpenAI, released on 5 August 2025 alongside the smaller gpt-oss-20b. The models are provided under the Apache 2.0 license with full access to the weights, permitting commercial and non-commercial deployment as well as fine-tuning.

The model uses a transformer-based mixture-of-experts (MoE) architecture: 117B total parameters, of which 5.1B are active per token; 128 experts (4 active per token) and 36 layers. Thanks to MXFP4 quantization of the MoE weights, the model fits on a single 80GB GPU (e.g. NVIDIA H100 or AMD MI300X).

gpt-oss-120b is a reasoning model with configurable effort levels (low/medium/high) and a visible chain of thought. It supports a 131,072-token context window, tool use (web browsing, Python code execution), function calling and structured outputs; it requires the harmony response format. It accepts and produces text only. Its knowledge cutoff is June 2024.

In evaluations (high reasoning mode) the model scores 96.6% on AIME 2024 and 97.9% on AIME 2025 (with tools), 80.9% on GPQA Diamond, 90.0% on MMLU, 2622 Elo on Codeforces and 57.6% on HealthBench. OpenAI reports that gpt-oss-120b achieves results comparable to the o4-mini model. The model is available for download on Hugging Face and through many inference providers.

Classification
Reasoning modelLLMTool-using model
Family: gpt-oss
Access & deployment
APIDownloadHosted
LocalCloud
Weights: Open weights
Key parameters
📏 Context: 128K
🧩 Parameters: 117B (5,1B aktywnych)
Tools · ✓ Fine-tuning
📥 Input: text

Technical specification

Context window
128K
tokens
Parameters
117B (5,1B aktywnych)
parameters
Max output tokens
131,072
tokens per response
Knowledge cutoff
1 Jun 2024
Knowledge boundary
License
Apache 2.0
Hardware requirements
Fits on a single 80GB GPU (e.g. NVIDIA H100 or AMD MI300X) thanks to MXFP4 quantization of the MoE weights.
Features:Tool useFine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode

Capabilities and applications

Native model capabilities
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Adaptive reasoning effort
The model decides how much 'thinking' to allocate to a given query: simple questions are answered quickly, complex problems receive more inference cycles. A GPT-5.1 feature (both Instant and Thinking) that shortens time on easy tasks and extends it for hard ones.
Category: reasoning

Benchmark results

7 benchmarks
AIME 2024
accuracy · high reasoning, with tools
96.6%
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
AIME 2025
accuracy · high reasoning, with tools
97.9%
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
GPQA
accuracy · GPQA Diamond, high reasoning
80.9%
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
MMLU
accuracy
90.0%
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
Codeforces
Elo · with tools
2622 Elo
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
HealthBench
score
57.6%
📄 OpenAI gpt-oss model card (arXiv:2508.10925)
Artificial Analysis Intelligence Index
index · high reasoning mode
24
📄 Artificial Analysis

Technical architecture

Core Architecture
Training Techniques

Deployment and security