DeepSeek R1 vs GPT-OSS 120B
Detailed comparison between DeepSeek R1 and GPT-OSS 120B for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
DeepSeek R1 takes the lead.
Both DeepSeek R1 and GPT-OSS 120B are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why DeepSeek R1:
- DeepSeek R1 has 22 higher ELO rating
Overview
Key metrics
ELO Rating
Overall ranking quality
DeepSeek R1
GPT-OSS 120B
Win Rate
Head-to-head performance
DeepSeek R1
GPT-OSS 120B
Quality Score
Overall quality metric
DeepSeek R1
GPT-OSS 120B
Average Latency
Response time
DeepSeek R1
GPT-OSS 120B
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 | DeepSeek R1 | GPT-OSS 120B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1338 | 1316 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 20.3% | 18.9% | Percentage of comparisons won against other models |
| Quality Score | 4.86 | 4.85 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.30 | $0.04 | Cost per million input tokens |
| Output Price per 1M | $1.20 | $0.19 | Cost per million output tokens |
| Context Window | 164K | 131K | Maximum context window size |
| Release Date | 2025-01-20 | 2025-08-05 | Model release date |
| Performance Metrics | |||
| Avg Latency | 18.3s | 11.2s | 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 | DeepSeek R1 | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.73 | 4.93 | Factual accuracy of responses |
| Faithfulness | 4.77 | 4.90 | Adherence to source material |
| Grounding | 4.77 | 4.90 | Citations and context usage |
| Relevance | 4.87 | 4.97 | Query alignment and focus |
| Completeness | 4.37 | 4.87 | Coverage of all aspects |
| Overall | 4.70 | 4.91 | Average across all metrics |
| Latency Metrics | |||
| Mean | 16654ms | 5616ms | Average response time |
| Min | 9675ms | 1255ms | Fastest response time |
| Max | 31255ms | 20330ms | Slowest response time |
PG
| Metric | DeepSeek R1 | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.93 | 4.80 | Factual accuracy of responses |
| Faithfulness | 4.93 | 4.80 | Adherence to source material |
| Grounding | 4.90 | 4.80 | Citations and context usage |
| Relevance | 4.97 | 4.83 | Query alignment and focus |
| Completeness | 4.60 | 4.73 | Coverage of all aspects |
| Overall | 4.87 | 4.79 | Average across all metrics |
| Latency Metrics | |||
| Mean | 23334ms | 19128ms | Average response time |
| Min | 12280ms | 1317ms | Fastest response time |
| Max | 85633ms | 69491ms | Slowest response time |
SciFact
| Metric | DeepSeek R1 | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.93 | 4.87 | Factual accuracy of responses |
| Faithfulness | 4.97 | 4.87 | Adherence to source material |
| Grounding | 4.93 | 4.87 | Citations and context usage |
| Relevance | 5.00 | 4.80 | Query alignment and focus |
| Completeness | 4.83 | 4.70 | Coverage of all aspects |
| Overall | 4.93 | 4.82 | Average across all metrics |
| Latency Metrics | |||
| Mean | 14826ms | 8854ms | Average response time |
| Min | 7765ms | 0ms | Fastest response time |
| Max | 33129ms | 35709ms | Slowest response time |
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