Model comparison
GPT-5.2 vs Qwen3 235B 2507
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.2 #64 (Estimated); Qwen3 235B 2507 #78 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2 and Qwen3 235B 2507 share 1 comparable benchmark result. 1 of 8 categories are comparable. 27 results are unique to GPT-5.2; 3 to Qwen3 235B 2507.
Updated July 20, 2026- Shared results
- 1
- GPT-5.2 only
- 27
- Qwen3 235B 2507 only
- 3
- Comparable categories
- 1 / 8
Pick GPT-5.2 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.2 has the cleaner BenchAlign overall profile here, landing at 58.43 versus 56.02. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.2's sharpest advantage is in knowledge, where it averages 92.4 against 78.9. The single biggest benchmark swing on the page is GPQA, 92.4% to 77.5%.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507. That is roughly Infinityx on output cost alone. GPT-5.2 is the reasoning model in the pair, while Qwen3 235B 2507 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.2 gives you the larger context window at 400K, compared with 128K for Qwen3 235B 2507.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-5.2 | Δ | Qwen3 235B 2507 |
|---|---|---|---|
| Knowledge | GPT-5.292.4 | Margin← 13.5 | Qwen3 235B 250778.9 |
| Agentic | GPT-5.255.7 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Coding | GPT-5.270.6 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Reasoning | GPT-5.252.9 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Math | GPT-5.235.2 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Multilingual | GPT-5.2Not measured | MarginNo overlap | Qwen3 235B 250779.4 |
| Multimodal | GPT-5.280.4 | MarginNo overlap | Qwen3 235B 2507Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 92.4%B 77.5%Winner: GPT-5.2Δ 14.9GPQA: GPT-5.2 scored 92.4%; Qwen3 235B 2507 scored 77.5%. GPT-5.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.2 | Qwen3 235B 2507 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2$1.75 input / $14 output | Qwen3 235B 2507$0 input / $0 output | Qwen3 235B 2507 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.273 tok/s | Qwen3 235B 2507Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.2130.34 s | Qwen3 235B 2507Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.2400K | Qwen3 235B 2507128K | GPT-5.2 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding4 benchmarks
Reasoning3 benchmarks
KnowledgeGPT-5.2 wins9 benchmarks
| Benchmark | GPT-5.2 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| GPQASource | 92.4% | 77.5% | GPT-5.2 leads |
| Artificial Analysis Intelligence IndexSource | 42.2% | — | Not comparable |
| AA-GPQA DiamondSource | 90.3% | — | Not comparable |
| AA-HLESource | 35.4% | — | Not comparable |
| AA-Omniscience IndexSource | -1.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 43.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 79.7% | — | Not comparable |
| SuperGPQASource | — | 62.6% | Not comparable |
| MMLU-ProSource | — | 83% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | GPT-5.2 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 79.4% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.2 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.2 or Qwen3 235B 2507?
GPT-5.2 is ahead on BenchLM's BenchAlign leaderboard, 58.43 to 56.02. The biggest single separator in this matchup is GPQA, where the scores are 92.4% and 77.5%.
Which is better for knowledge tasks, GPT-5.2 or Qwen3 235B 2507?
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 78.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.