GPT-5.2 vs Claude Opus 4.5
Detailed comparison between GPT-5.2 and Claude Opus 4.5 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
Claude Opus 4.5 takes the lead.
Both GPT-5.2 and Claude Opus 4.5 are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why Claude Opus 4.5:
- Claude Opus 4.5 has 31 higher ELO rating
- Claude Opus 4.5 has a 10.4% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
GPT-5.2
Claude Opus 4.5
Win Rate
Head-to-head performance
GPT-5.2
Claude Opus 4.5
Quality Score
Overall quality metric
GPT-5.2
Claude Opus 4.5
Average Latency
Response time
GPT-5.2
Claude Opus 4.5
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
| Metric | GPT-5.2 | Claude Opus 4.5 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1588 | 1619 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 45.7% | 56.0% | Percentage of comparisons won against other models |
| Quality Score | 4.97 | 4.91 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $1.75 | $5.00 | Cost per million input tokens |
| Output Price per 1M | $14.00 | $25.00 | Cost per million output tokens |
| Context Window | 400K | 200K | Maximum context window size |
| Release Date | 2025-12-11 | 2025-11-24 | Model release date |
| Performance Metrics | |||
| Avg Latency | 5.4s | 8.3s | Average response time across all datasets |
Dataset Performance
By benchmark
Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.
MSMARCO
| Metric | GPT-5.2 | Claude Opus 4.5 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.97 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.97 | Adherence to source material |
| Grounding | 5.00 | 4.97 | Citations and context usage |
| Relevance | 4.97 | 4.97 | Query alignment and focus |
| Completeness | 4.87 | 4.97 | Coverage of all aspects |
| Overall | 4.97 | 4.97 | Average across all metrics |
| Latency Metrics | |||
| Mean | 2652ms | 5992ms | Average response time |
| Min | 796ms | 2590ms | Fastest response time |
| Max | 5810ms | 8072ms | Slowest response time |
PG
| Metric | GPT-5.2 | Claude Opus 4.5 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.93 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.93 | Adherence to source material |
| Grounding | 5.00 | 4.93 | Citations and context usage |
| Relevance | 5.00 | 4.93 | Query alignment and focus |
| Completeness | 4.97 | 4.80 | Coverage of all aspects |
| Overall | 4.99 | 4.91 | Average across all metrics |
| Latency Metrics | |||
| Mean | 8702ms | 11489ms | Average response time |
| Min | 2755ms | 7945ms | Fastest response time |
| Max | 14361ms | 15934ms | Slowest response time |
SciFact
| Metric | GPT-5.2 | Claude Opus 4.5 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.87 | 4.73 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.80 | Adherence to source material |
| Grounding | 4.97 | 4.80 | Citations and context usage |
| Relevance | 4.97 | 4.97 | Query alignment and focus |
| Completeness | 4.73 | 4.70 | Coverage of all aspects |
| Overall | 4.91 | 4.80 | Average across all metrics |
| Latency Metrics | |||
| Mean | 4785ms | 7276ms | Average response time |
| Min | 1318ms | 4210ms | Fastest response time |
| Max | 10172ms | 10496ms | Slowest response time |
Explore More
Compare more LLMs
See how all LLMs stack up for RAG applications. Compare GPT-5, Claude, Gemini, and more. View comprehensive benchmarks and find the perfect LLM for your needs.