Claude Sonnet 4.5 vs GPT-OSS 120B

Detailed comparison between Claude Sonnet 4.5 and GPT-OSS 120B 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 4.5 takes the lead.

Both Claude Sonnet 4.5 and GPT-OSS 120B are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.

Why Claude Sonnet 4.5:

  • Claude Sonnet 4.5 has 259 higher ELO rating
  • Claude Sonnet 4.5 delivers better overall quality (4.90 vs 4.80)
  • Claude Sonnet 4.5 is 1.5s faster on average
  • Claude Sonnet 4.5 has a 22.4% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Claude Sonnet 4.5

1533

GPT-OSS 120B

1274

Win Rate

Head-to-head performance

Claude Sonnet 4.5

39.0%

GPT-OSS 120B

16.6%

Quality Score

Overall quality metric

Claude Sonnet 4.5

4.90

GPT-OSS 120B

4.80

Average Latency

Response time

Claude Sonnet 4.5

9659ms

GPT-OSS 120B

11199ms

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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

MetricClaude Sonnet 4.5GPT-OSS 120BDescription
Overall Performance
ELO Rating
1533
1274
Overall ranking quality based on pairwise comparisons
Win Rate
39.0%
16.6%
Percentage of comparisons won against other models
Quality Score
4.90
4.80
Average quality across all RAG metrics
Pricing & Context
Input Price per 1M
$3.00
$0.04
Cost per million input tokens
Output Price per 1M
$15.00
$0.19
Cost per million output tokens
Context Window
200K
131K
Maximum context window size
Release Date
2025-09-29
2025-08-05
Model release date
Performance Metrics
Avg Latency
9.7s
11.2s
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

MetricClaude Sonnet 4.5GPT-OSS 120BDescription
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
4.93
5.00
Query alignment and focus
Completeness
4.87
5.00
Coverage of all aspects
Overall
4.88
5.00
Average across all metrics
Latency Metrics
Mean
9825ms
5616ms
Average response time
Min2325ms1255msFastest response time
Max21762ms20330msSlowest response time

PG

MetricClaude Sonnet 4.5GPT-OSS 120BDescription
Quality Metrics
Correctness
5.00
4.78
Factual accuracy of responses
Faithfulness
5.00
4.78
Adherence to source material
Grounding
5.00
4.78
Citations and context usage
Relevance
5.00
4.83
Query alignment and focus
Completeness
5.00
4.72
Coverage of all aspects
Overall
5.00
4.78
Average across all metrics
Latency Metrics
Mean
12322ms
19128ms
Average response time
Min9247ms1317msFastest response time
Max20544ms69491msSlowest response time

SciFact

MetricClaude Sonnet 4.5GPT-OSS 120BDescription
Quality Metrics
Correctness
4.80
4.64
Factual accuracy of responses
Faithfulness
4.87
4.64
Adherence to source material
Grounding
4.77
4.64
Citations and context usage
Relevance
5.00
4.64
Query alignment and focus
Completeness
4.73
4.55
Coverage of all aspects
Overall
4.83
4.62
Average across all metrics
Latency Metrics
Mean
6830ms
8854ms
Average response time
Min2621ms0msFastest response time
Max10722ms35709msSlowest response time

Explore More

Compare more LLMs

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