GPT-5.2 vs Qwen3 30B A3B Thinking
Detailed comparison between GPT-5.2 and Qwen3 30B A3B Thinking for RAG applications. See which LLM best meets your accuracy, performance, and cost needs.
Model Comparison
GPT-5.2 takes the lead.
Both GPT-5.2 and Qwen3 30B A3B Thinking are powerful language models designed for RAG applications. However, their performance characteristics differ in important ways.
Why GPT-5.2:
- GPT-5.2 has 257 higher ELO rating
- GPT-5.2 delivers better overall quality (4.97 vs 4.90)
- GPT-5.2 is 6.9s faster on average
- GPT-5.2 has a 13.8% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
GPT-5.2
Qwen3 30B A3B Thinking
Win Rate
Head-to-head performance
GPT-5.2
Qwen3 30B A3B Thinking
Quality Score
Overall quality metric
GPT-5.2
Qwen3 30B A3B Thinking
Average Latency
Response time
GPT-5.2
Qwen3 30B A3B Thinking
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-5.2 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1588 | 1331 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 45.7% | 31.9% | Percentage of comparisons won against other models |
| Quality Score | 4.97 | 4.90 | Average quality across all RAG metrics |
| Pricing & Context | |||
| Input Price per 1M | $1.75 | $0.05 | Cost per million input tokens |
| Output Price per 1M | $14.00 | $0.34 | Cost per million output tokens |
| Context Window | 400K | 33K | Maximum context window size |
| Release Date | 2025-12-11 | 2025-08-28 | Model release date |
| Performance Metrics | |||
| Avg Latency | 5.4s | 12.3s | 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-5.2 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.90 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.90 | Adherence to source material |
| Grounding | 5.00 | 4.90 | Citations and context usage |
| Relevance | 4.97 | 5.00 | Query alignment and focus |
| Completeness | 4.87 | 4.80 | Coverage of all aspects |
| Overall | 4.97 | 4.90 | Average across all metrics |
| Latency Metrics | |||
| Mean | 2652ms | 12522ms | Average response time |
| Min | 796ms | 1541ms | Fastest response time |
| Max | 5810ms | 49799ms | Slowest response time |
PG
| Metric | GPT-5.2 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 5.00 | 4.90 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.87 | Adherence to source material |
| Grounding | 5.00 | 4.87 | Citations and context usage |
| Relevance | 5.00 | 4.93 | Query alignment and focus |
| Completeness | 4.97 | 4.77 | Coverage of all aspects |
| Overall | 4.99 | 4.87 | Average across all metrics |
| Latency Metrics | |||
| Mean | 8702ms | 16030ms | Average response time |
| Min | 2755ms | 3483ms | Fastest response time |
| Max | 14361ms | 44237ms | Slowest response time |
SciFact
| Metric | GPT-5.2 | Qwen3 30B A3B Thinking | Description |
|---|---|---|---|
| Quality Metrics | |||
| Correctness | 4.87 | 4.97 | Factual accuracy of responses |
| Faithfulness | 5.00 | 4.97 | Adherence to source material |
| Grounding | 4.97 | 4.93 | Citations and context usage |
| Relevance | 4.97 | 5.00 | Query alignment and focus |
| Completeness | 4.73 | 4.83 | Coverage of all aspects |
| Overall | 4.91 | 4.94 | Average across all metrics |
| Latency Metrics | |||
| Mean | 4785ms | 8384ms | Average response time |
| Min | 1318ms | 2185ms | Fastest response time |
| Max | 10172ms | 19414ms | Slowest response time |
Explore More
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