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GPT-OSS 120B

Active Parameters

117B

Context Length

128K

Modality

Text

Architecture

Mixture of Experts (MoE)

License

Apache 2.0

Release Date

5 Aug 2025

Knowledge Cutoff

Jun 2024

API Pricing (per 1M)

Input: $0.15 · Output: $0.60

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

230.40 GB VRAM

Consumer

12x RTX 4090

24GB VRAM

Datacenter

4x NVIDIA A100

80GB VRAM

Apple Silicon

3x Apple M3 Max

128GB VRAM

128,000 tokens

239.55 GB VRAM

Consumer

12x RTX 4090

24GB VRAM

Datacenter

4x NVIDIA A100

80GB VRAM

Apple Silicon

3x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 2.9k · Context: 128K · Vocab: 201.1kx 36 layersRMSNormPre-AttentionMulti-Head Attention64Q / 8KV heads · SW: 128Head dim: 64+RMSNormPre-FFNSparse MoE FFN (4/128 experts)SwiGLU+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#135

BenchmarkScoreRank

Professional Knowledge

MMLU Pro
high

0.807

28

Software Engineering

SWE-bench Verified

0.26

36

Graduate-Level QA

GPQA
high

0.809

Standard

0.801

64

66

Agentic Index

Artificial Analysis
high

0.04

104

high

0.30

low

0.21

118

130

General Text

Text Arena

1352

134

Intelligence Index

Artificial Analysis
high

0.12

low

0.10

251

259

Summarization

Archived
ProLLM Summarization

0.976

🥈

2

General Knowledge

Reference
MMLU

0.9

🥈

2

Coding

Archived
Aider Coding
high

0.42

17

Rankings

Overall Rank

#135

Coding Rank

#120

About GPT-OSS 120B

GPT-OSS 120B is a large open-weight model from OpenAI, designed to operate in data centers and on high-end desktops and laptops. It is developed to support advanced reasoning, agentic tasks, and diverse developer use cases, functioning as a text-only model for both input and output modalities.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

64

Key-Value Heads

8

Attention Head Dimension

64

Position Embedding

Absolute Position Embedding

RoPE Theta

150,000

Sliding Window Attention

Yes

Sliding Window Size

128

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

2,880

Number of Layers

36

FFN Intermediate Size (Dense)

2,880

Tokenizer

Vocabulary Size

201,088

Mixture of Experts

Total Expert Parameters

5.1B

Number of Experts

128

Active Experts

4

About GPT-OSS

Open-weight language models from OpenAI.


Other GPT-OSS Models