GPT-OSS 120B vs Claude Sonnet 4.6

Detailed comparison between GPT-OSS 120B and Claude Sonnet 4.6 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs. If you want to compare these models on your data, try Agentset.

Model Comparison

Claude Sonnet 4.6 takes the lead.

Both GPT-OSS 120B and Claude Sonnet 4.6 are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.

Why Claude Sonnet 4.6:

  • Claude Sonnet 4.6 has 496 higher ELO rating
  • Claude Sonnet 4.6 delivers better overall quality (4.96 vs 4.83)
  • Claude Sonnet 4.6 is 1.8s faster on average
  • Claude Sonnet 4.6 has a 53.4% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

GPT-OSS 120B

1242

Claude Sonnet 4.6

1738

Win Rate

Head-to-head performance

GPT-OSS 120B

14.2%

Claude Sonnet 4.6

67.6%

Quality Score

Overall quality metric

GPT-OSS 120B

4.83

Claude Sonnet 4.6

4.96

Average Latency

Response time

GPT-OSS 120B

11302ms

Claude Sonnet 4.6

9498ms

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Visual Performance Analysis

Performance

ELO Rating Comparison

Win/Loss/Tie Breakdown

Quality Across Datasets (Overall Score)

Latency Distribution (ms)

Breakdown

How the models stack up

MetricGPT-OSS 120BClaude Sonnet 4.6Description
Overall Performance
ELO Rating
1242
1738
Overall ranking quality based on pairwise comparisons
Win Rate
14.2%
67.6%
Percentage of comparisons won against other models
Quality Score
4.83
4.96
Average quality across all RAG metrics
Pricing & Context
Input Price per 1M
$0.04
$3.00
Cost per million input tokens
Output Price per 1M
$0.19
$15.00
Cost per million output tokens
Context Window
131K
200K
Maximum context window size
Release Date
2025-08-05
2026-02-17
Model release date
Performance Metrics
Avg Latency
11.3s
9.5s
Average response time across all datasets

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Dataset Performance

By benchmark

Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.

MSMARCO

MetricGPT-OSS 120BClaude Sonnet 4.6Description
Quality Metrics
Correctness
4.93
4.97
Factual accuracy of responses
Faithfulness
4.93
4.97
Adherence to source material
Grounding
4.93
4.97
Citations and context usage
Relevance
4.97
5.00
Query alignment and focus
Completeness
4.80
4.97
Coverage of all aspects
Overall
4.91
4.98
Average across all metrics
Latency Metrics
Mean
5616ms
5785ms
Average response time
Min1255ms2066msFastest response time
Max20330ms8195msSlowest response time

PG

MetricGPT-OSS 120BClaude Sonnet 4.6Description
Quality Metrics
Correctness
4.87
5.00
Factual accuracy of responses
Faithfulness
4.87
5.00
Adherence to source material
Grounding
4.87
5.00
Citations and context usage
Relevance
4.90
5.00
Query alignment and focus
Completeness
4.83
5.00
Coverage of all aspects
Overall
4.87
5.00
Average across all metrics
Latency Metrics
Mean
19128ms
12740ms
Average response time
Min1317ms8720msFastest response time
Max69491ms20930msSlowest response time

SciFact

MetricGPT-OSS 120BClaude Sonnet 4.6Description
Quality Metrics
Correctness
4.70
4.87
Factual accuracy of responses
Faithfulness
4.80
4.87
Adherence to source material
Grounding
4.80
4.87
Citations and context usage
Relevance
4.73
5.00
Query alignment and focus
Completeness
4.57
4.83
Coverage of all aspects
Overall
4.72
4.89
Average across all metrics
Latency Metrics
Mean
9160ms
9969ms
Average response time
Min1606ms2886msFastest response time
Max35709ms19276msSlowest response time

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