Pixtral Large (25.02) vs Qwen3 235B-A22B Thinking 2507
Qwen3 235B-A22B Thinking 2507 comes out ahead, 42 to 33 on our weighted score, and it is the cheaper option too.
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
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Make it a three-way comparison.
Qwen3 235B-A22B Thinking 2507 is our pick
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against Pixtral Large (25.02) (33). It leads on price and context window. Pixtral Large (25.02) wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsPixtral Large (25.02)Pixtral Large (25.02): Text, Images · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingQwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Pixtral Large (25.02) | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|
| Price | 50% | 26 | 49 |
| Inputs & features | 30% | 50 | 35 |
| Context window | 20% | 24 | 37 |
| Overall | 100% | 33/100 | 42/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Pixtral Large (25.02)Mistral AI | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 143.8 |
| ECI rank | — | #93 of 165 |
| GPQA DiamondGraduate-level science questions | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% |
| SimpleQA VerifiedShort factual questions | — | 40.4% |
| Price per million tokens | ||
| Input | $2.00 | $0.30 (best) |
| Output | $6.00 | $2.90 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.00 | $0.95 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 11 providers |
| Limits | ||
| Context window | 128,000 tokens | 262,144 tokens (best) |
| Max output | 8,192 tokens | 81,920 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | OpenApache 2.0 |
| API model ID | — | — |
| API providers | 3 | 12 (best) |
| Released | Apr 8, 2025 | Jul 25, 2025 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Pixtral Large (25.02)$32.00
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: Pixtral Large (25.02) or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against Pixtral Large (25.02) (33). It leads on price and context window. Pixtral Large (25.02) wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Pixtral Large (25.02) or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $3.00 for Pixtral Large (25.02) (3.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Pixtral Large (25.02) has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.
Which is better for coding?
There are no published SWE-bench Verified results for Pixtral Large (25.02) and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 128,000 for Pixtral Large (25.02). Maximum output per response: Pixtral Large (25.02) up to 8,192, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
Pixtral Large (25.02) accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Pixtral Large (25.02) is proprietary.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 2025. Pixtral Large (25.02) came out Apr 8, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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