Claude Opus 4.5 vs DeepSeek R1
Detailed comparison between Claude Opus 4.5 and DeepSeek R1 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
Claude Opus 4.5 takes the lead.
Both Claude Opus 4.5 and DeepSeek R1 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 281 higher ELO rating
- Claude Opus 4.5 is 10.0s faster on average
- Claude Opus 4.5 has a 35.8% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Claude Opus 4.5
DeepSeek R1
Win Rate
Head-to-head performance
Claude Opus 4.5
DeepSeek R1
Quality Score
Overall quality metric
Claude Opus 4.5
DeepSeek R1
Average Latency
Response time
Claude Opus 4.5
DeepSeek R1
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 | Claude Opus 4.5 | DeepSeek R1 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1619 | 1338 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 56.0% | 20.3% | Percentage of comparisons won against other models |
| Quality Score | 4.91 | 4.86 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $5.00 | $0.30 | Cost per million input tokens |
| Output Price per 1M | $25.00 | $1.20 | Cost per million output tokens |
| Context Window | 200K | 164K | Maximum context window size |
| Release Date | 2025-11-24 | 2025-01-20 | Model release date |
| Performance Metrics | |||
| Avg Latency | 8.3s | 18.3s | 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 | Claude Opus 4.5 | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.97 | 4.73 | Factual accuracy of responses |
| Faithfulness | 4.97 | 4.77 | Adherence to source material |
| Grounding | 4.97 | 4.77 | Citations and context usage |
| Relevance | 4.97 | 4.87 | Query alignment and focus |
| Completeness | 4.97 | 4.37 | Coverage of all aspects |
| Overall | 4.97 | 4.70 | Average across all metrics |
| Latency Metrics | |||
| Mean | 5992ms | 16654ms | Average response time |
| Min | 2590ms | 9675ms | Fastest response time |
| Max | 8072ms | 31255ms | Slowest response time |
PG
| Metric | Claude Opus 4.5 | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.93 | 4.93 | Factual accuracy of responses |
| Faithfulness | 4.93 | 4.93 | Adherence to source material |
| Grounding | 4.93 | 4.90 | Citations and context usage |
| Relevance | 4.93 | 4.97 | Query alignment and focus |
| Completeness | 4.80 | 4.60 | Coverage of all aspects |
| Overall | 4.91 | 4.87 | Average across all metrics |
| Latency Metrics | |||
| Mean | 11489ms | 23334ms | Average response time |
| Min | 7945ms | 12280ms | Fastest response time |
| Max | 15934ms | 85633ms | Slowest response time |
SciFact
| Metric | Claude Opus 4.5 | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.73 | 4.93 | Factual accuracy of responses |
| Faithfulness | 4.80 | 4.97 | Adherence to source material |
| Grounding | 4.80 | 4.93 | Citations and context usage |
| Relevance | 4.97 | 5.00 | Query alignment and focus |
| Completeness | 4.70 | 4.83 | Coverage of all aspects |
| Overall | 4.80 | 4.93 | Average across all metrics |
| Latency Metrics | |||
| Mean | 7276ms | 14826ms | Average response time |
| Min | 4210ms | 7765ms | Fastest response time |
| Max | 10496ms | 33129ms | Slowest response time |
Explore More
Compare more LLMs
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