GPT-OSS 120B vs DeepSeek R1
Detailed comparison between GPT-OSS 120B and DeepSeek R1 for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
GPT-OSS 120B takes the lead.
Both GPT-OSS 120B and DeepSeek R1 are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why GPT-OSS 120B:
- GPT-OSS 120B has 14 higher ELO rating
- GPT-OSS 120B is 7.1s faster on average
Overview
Key metrics
ELO Rating
Overall ranking quality
GPT-OSS 120B
DeepSeek R1
Win Rate
Head-to-head performance
GPT-OSS 120B
DeepSeek R1
Quality Score
Overall quality metric
GPT-OSS 120B
DeepSeek R1
Average Latency
Response time
GPT-OSS 120B
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 | GPT-OSS 120B | DeepSeek R1 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1303 | 1288 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 17.9% | 18.9% | Percentage of comparisons won against other models |
| Quality Score | 4.85 | 4.84 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.04 | $0.30 | Cost per million input tokens |
| Output Price per 1M | $0.19 | $1.20 | Cost per million output tokens |
| Context Window | 131K | 164K | Maximum context window size |
| Release Date | 2025-08-05 | 2025-01-20 | Model release date |
| Performance Metrics | |||
| Avg Latency | 11.2s | 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 | GPT-OSS 120B | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.93 | 4.67 | Factual accuracy of responses |
| Faithfulness | 4.90 | 4.70 | Adherence to source material |
| Grounding | 4.90 | 4.67 | Citations and context usage |
| Relevance | 4.97 | 4.90 | Query alignment and focus |
| Completeness | 4.80 | 4.60 | Coverage of all aspects |
| Overall | 4.90 | 4.71 | Average across all metrics |
| Latency Metrics | |||
| Mean | 5616ms | 16654ms | Average response time |
| Min | 1255ms | 9675ms | Fastest response time |
| Max | 20330ms | 31255ms | Slowest response time |
PG
| Metric | GPT-OSS 120B | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.87 | 4.90 | Factual accuracy of responses |
| Faithfulness | 4.87 | 4.90 | Adherence to source material |
| Grounding | 4.87 | 4.87 | Citations and context usage |
| Relevance | 4.90 | 4.93 | Query alignment and focus |
| Completeness | 4.83 | 4.60 | Coverage of all aspects |
| Overall | 4.87 | 4.84 | Average across all metrics |
| Latency Metrics | |||
| Mean | 19128ms | 23334ms | Average response time |
| Min | 1317ms | 12280ms | Fastest response time |
| Max | 69491ms | 85633ms | Slowest response time |
SciFact
| Metric | GPT-OSS 120B | DeepSeek R1 | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.80 | 5.00 | Factual accuracy of responses |
| Faithfulness | 4.87 | 5.00 | Adherence to source material |
| Grounding | 4.87 | 4.97 | Citations and context usage |
| Relevance | 4.77 | 5.00 | Query alignment and focus |
| Completeness | 4.67 | 4.87 | Coverage of all aspects |
| Overall | 4.79 | 4.97 | Average across all metrics |
| Latency Metrics | |||
| Mean | 8854ms | 14826ms | Average response time |
| Min | 0ms | 7765ms | Fastest response time |
| Max | 35709ms | 33129ms | Slowest response time |
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