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GPT OSS 120B

openai/gpt-oss-120b
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GPT OSS 120B

openai/gpt-oss-120b
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An open-weight, 117B-parameter Mixture-of-Experts (MoE) language model designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.

Added Feb 3, 2026

Model weights

Context Window

128.0K

Max Output

16.4K

Avg output tokens (7d)

295 tokens

26%

Input Price (Auto)

$0.030/1M

Output Price (Auto)

$0.17/1M

Cache Read (Auto)

$0.030/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

11.6

Better than 46% of models compared

Coding Index

30.4

Better than 38% of models compared

Agentic Index

3.7

Better than 35% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

0.2%

Better than 3% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

13.9%

Better than 0% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

0 Elo

Better than 3% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

596 Elo

Better than 31% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

4.0%

Better than 22% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

52.0%

Better than 45% of models compared

MLCR-AA

Medical long-context reasoning

1.1%

Better than 7% of models compared

Reasoning

HLE

Humanity's Last Exam

19.6%

Better than 67% of models compared

IFBench

Instruction-following benchmark

69.0%

Better than 83% of models compared

CritPt

Research-level physics reasoning

1.1%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

34.0%

Better than 14% of models compared

LiveCodeBench

Contamination-free coding benchmark

87.8%

Better than 98% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

93.4%

Better than 94% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

80.8%

Better than 71% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

21.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

90.8%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

78.2%

Better than 64% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

23.5%

Better than 63% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

65.8%

Better than 60% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

52.0%

Better than 45% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

4.8%

Last updated Oct 2, 2026

Artificial Analysis

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