Qwen3 30B A3B Thinking vs GPT-OSS 120B
Detailed comparison between Qwen3 30B A3B Thinking and GPT-OSS 120B for RAG applications. See which LLM best meets your accuracy, performance, and cost needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Qwen3 30B A3B Thinking takes the lead.
Both Qwen3 30B A3B Thinking and GPT-OSS 120B are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why Qwen3 30B A3B Thinking:
- Qwen3 30B A3B Thinking has 47 higher ELO rating
- Qwen3 30B A3B Thinking delivers better overall quality (4.89 vs 4.80)
- Qwen3 30B A3B Thinking has a 11.0% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Qwen3 30B A3B Thinking
GPT-OSS 120B
Win Rate
Head-to-head performance
Qwen3 30B A3B Thinking
GPT-OSS 120B
Quality Score
Overall quality metric
Qwen3 30B A3B Thinking
GPT-OSS 120B
Average Latency
Response time
Qwen3 30B A3B Thinking
GPT-OSS 120B
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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 | Qwen3 30B A3B Thinking | GPT-OSS 120B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1322 | 1274 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 27.6% | 16.6% | Percentage of comparisons won against other models |
| Quality Score | 4.89 | 4.80 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $0.05 | $0.04 | Cost per million input tokens |
| Output Price per 1M | $0.34 | $0.19 | Cost per million output tokens |
| Context Window | 33K | 131K | Maximum context window size |
| Release Date | 2025-08-28 | 2025-08-05 | Model release date |
| Performance Metrics | |||
| Avg Latency | 12.3s | 11.2s | Average response time across all datasets |
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for (const result of results) {
console.log(result.text);
}Dataset Performance
By benchmark
Comprehensive comparison of RAG quality metrics (correctness, faithfulness, grounding, relevance, completeness) and latency for each benchmark dataset.
MSMARCO
| Metric | Qwen3 30B A3B Thinking | GPT-OSS 120B | 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 | 5.00 | 5.00 | Coverage of all aspects |
| Overall | 5.00 | 5.00 | Average across all metrics |
| Latency Metrics | |||
| Mean | 12522ms | 5616ms | Average response time |
| Min | 1541ms | 1255ms | Fastest response time |
| Max | 49799ms | 20330ms | Slowest response time |
PG
| Metric | Qwen3 30B A3B Thinking | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.78 | 4.78 | Factual accuracy of responses |
| Faithfulness | 4.78 | 4.78 | Adherence to source material |
| Grounding | 4.78 | 4.78 | Citations and context usage |
| Relevance | 4.89 | 4.83 | Query alignment and focus |
| Completeness | 4.61 | 4.72 | Coverage of all aspects |
| Overall | 4.77 | 4.78 | Average across all metrics |
| Latency Metrics | |||
| Mean | 16030ms | 19128ms | Average response time |
| Min | 3483ms | 1317ms | Fastest response time |
| Max | 44237ms | 69491ms | Slowest response time |
SciFact
| Metric | Qwen3 30B A3B Thinking | GPT-OSS 120B | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.91 | 4.64 | Factual accuracy of responses |
| Faithfulness | 4.91 | 4.64 | Adherence to source material |
| Grounding | 4.91 | 4.64 | Citations and context usage |
| Relevance | 5.00 | 4.64 | Query alignment and focus |
| Completeness | 4.82 | 4.55 | Coverage of all aspects |
| Overall | 4.91 | 4.62 | Average across all metrics |
| Latency Metrics | |||
| Mean | 8384ms | 8854ms | Average response time |
| Min | 2185ms | 0ms | Fastest response time |
| Max | 19414ms | 35709ms | Slowest response time |
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