| Signal | GPT-5.2 | Delta | R1 Distill Qwen 32B |
|---|---|---|---|
Capabilities | 86 | +43 | |
Context window size | 89 | +17 | |
Output Capacity | 85 | +10 | |
Pricing Tier | 14 | +14 | |
Recency | 100 | +40 | |
Versatility | 67 | +33 | |
| Overall Result | 6 wins | of 6 | 0 wins |
30
days ranked higher
0
days
0
days ranked higher
OpenAI
DeepSeek
R1 Distill Qwen 32B saves you $831.50/month
That's $9978.00/year compared to GPT-5.2 at your current usage level of 100K calls/month.
| Metric | GPT-5.2 | R1 Distill Qwen 32B | Winner |
|---|---|---|---|
| Overall Score | 68 | 42 | GPT-5.2 |
| Rank | #13 | #225 | GPT-5.2 |
| Quality Rank | #13 | #225 | GPT-5.2 |
| Adoption Rank | #13 | #225 | GPT-5.2 |
| Parameters | -- | -- | -- |
| Context Window | 400K | 33K | GPT-5.2 |
| Pricing | $1.75/$14.00/M | $0.29/$0.29/M | -- |
| Signal Scores | |||
| Capabilities | 86 | 43 | GPT-5.2 |
| Context window size | 89 | 72 | GPT-5.2 |
| Output Capacity | 85 | 75 | GPT-5.2 |
| Pricing Tier | 14 | 0 | GPT-5.2 |
| Recency | 100 | 60 | GPT-5.2 |
| Versatility | 67 | 33 | GPT-5.2 |
GPT-5.2 clearly outperforms R1 Distill Qwen 32B with a significant 26.900000000000006-point lead. For most general use cases, GPT-5.2 is the stronger choice. However, R1 Distill Qwen 32B may still excel in niche scenarios.
Best for Quality
GPT-5.2
Marginally better benchmark scores; both are excellent
Best for Cost
R1 Distill Qwen 32B
96% lower pricing; better value at scale
Best for Reliability
GPT-5.2
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.2
Stronger community support and better developer experience
Best for Production
GPT-5.2
Wider enterprise adoption and proven at scale
by OpenAI
GPT-5.2 currently scores higher (68 vs 42), but the best choice depends on your specific use case, budget, and requirements.
GPT-5.2 is ranked #13 and R1 Distill Qwen 32B is ranked #225. Rankings are based on a composite score from multiple signals including benchmarks, community sentiment, and adoption metrics.
Compare the detailed pricing breakdown above to see which model offers better value for your usage pattern.