GPT-5.1 vs GPT-5.2
Detailed comparison between GPT-5.1 and GPT-5.2 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
GPT-5.1 takes the lead.
Both GPT-5.1 and GPT-5.2 are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why GPT-5.1:
- GPT-5.1 has 123 higher ELO rating
- GPT-5.1 has a 23.6% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
GPT-5.1
GPT-5.2
Win Rate
Head-to-head performance
GPT-5.1
GPT-5.2
Quality Score
Overall quality metric
GPT-5.1
GPT-5.2
Average Latency
Response time
GPT-5.1
GPT-5.2
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-5.1 | GPT-5.2 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1711 | 1588 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 69.3% | 45.7% | Percentage of comparisons won against other models |
| Quality Score | 4.98 | 4.97 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $1.25 | $1.75 | Cost per million input tokens |
| Output Price per 1M | $10.00 | $14.00 | Cost per million output tokens |
| Context Window | 400K | 400K | Maximum context window size |
| Release Date | 2025-11-13 | 2025-12-11 | Model release date |
| Performance Metrics | |||
| Avg Latency | 16.2s | 5.4s | 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-5.1 | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 5.00 | Factual accuracy of responses |
| Faithfulness | 5.00 | 5.00 | Adherence to source material |
| Grounding | 5.00 | 5.00 | Citations and context usage |
| Relevance | 5.00 | 4.97 | Query alignment and focus |
| Completeness | 4.93 | 4.87 | Coverage of all aspects |
| Overall | 4.99 | 4.97 | Average across all metrics |
| Latency Metrics | |||
| Mean | 9111ms | 2652ms | Average response time |
| Min | 3841ms | 796ms | Fastest response time |
| Max | 34731ms | 5810ms | Slowest response time |
PG
| Metric | GPT-5.1 | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 5.00 | Factual accuracy of responses |
| Faithfulness | 5.00 | 5.00 | Adherence to source material |
| Grounding | 5.00 | 5.00 | Citations and context usage |
| Relevance | 5.00 | 5.00 | Query alignment and focus |
| Completeness | 4.73 | 4.97 | Coverage of all aspects |
| Overall | 4.95 | 4.99 | Average across all metrics |
| Latency Metrics | |||
| Mean | 29008ms | 8702ms | Average response time |
| Min | 4393ms | 2755ms | Fastest response time |
| Max | 43887ms | 14361ms | Slowest response time |
SciFact
| Metric | GPT-5.1 | GPT-5.2 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.87 | Factual accuracy of responses |
| Faithfulness | 5.00 | 5.00 | Adherence to source material |
| Grounding | 5.00 | 4.97 | Citations and context usage |
| Relevance | 5.00 | 4.97 | Query alignment and focus |
| Completeness | 4.97 | 4.73 | Coverage of all aspects |
| Overall | 4.99 | 4.91 | Average across all metrics |
| Latency Metrics | |||
| Mean | 10454ms | 4785ms | Average response time |
| Min | 4700ms | 1318ms | Fastest response time |
| Max | 21205ms | 10172ms | Slowest response time |
Explore More
Compare more LLMs
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