Model comparison
DeepSeek V4 Pro vs Qwen3 235B 2507 (Reasoning)
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro #46 (Supported); Qwen3 235B 2507 (Reasoning) #66 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro and Qwen3 235B 2507 (Reasoning) share 0 comparable benchmark results. 1 of 8 categories are comparable. 23 results are unique to DeepSeek V4 Pro; 2 to Qwen3 235B 2507 (Reasoning).
Updated July 23, 2026- Shared results
- 0
- DeepSeek V4 Pro only
- 23
- Qwen3 235B 2507 (Reasoning) only
- 2
- Comparable categories
- 1 / 8
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. Qwen3 235B 2507 (Reasoning) only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Pro has the cleaner BenchAlign overall profile here, landing at 60.66 versus 58.01. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V4 Pro's sharpest advantage is in mathematics, where it averages 31.7 against 6.4.
DeepSeek V4 Pro is also the more expensive model on tokens at $0.43 input / $0.87 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507 (Reasoning). That is roughly Infinityx on output cost alone. Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while DeepSeek V4 Pro 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. DeepSeek V4 Pro gives you the larger context window at 1M, compared with 128K for Qwen3 235B 2507 (Reasoning).
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro | Qwen3 235B 2507 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro$0.435 input / $0.87 output | Qwen3 235B 2507 (Reasoning)$0 input / $0 output | Qwen3 235B 2507 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 ProNot available | Qwen3 235B 2507 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 ProNot available | Qwen3 235B 2507 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro1M | Qwen3 235B 2507 (Reasoning)128K | DeepSeek V4 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
MathDeepSeek V4 Pro wins6 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 31.7% | — | Not comparable |
| IMOAnswerBenchSource | 35.3% | — | Not comparable |
| ApexSource | 0.4% | — | Not comparable |
| Apex ShortlistSource | 9.2% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 8.481% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 0.000% | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1264 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Pro or Qwen3 235B 2507 (Reasoning)?
DeepSeek V4 Pro is ahead on BenchLM's BenchAlign leaderboard, 60.66 to 58.01.
Which is better for math, DeepSeek V4 Pro or Qwen3 235B 2507 (Reasoning)?
DeepSeek V4 Pro has the edge for math in this comparison, averaging 31.7 versus 6.4. Qwen3 235B 2507 (Reasoning) stays close enough that the answer can still flip depending on your workload.
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