Qwen3 235B-A22B Thinking 2507 vs Pixtral Large (25.02) vs GLM-4.7
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, Pixtral Large (25.02) 33). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Too close to call
It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Pixtral Large (25.02) 33/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 for long inputs. 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 · GLM-4.7 $1.00 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsPixtral Large (25.02)Qwen3 235B-A22B Thinking 2507: Text · Pixtral Large (25.02): Text, Images · GLM-4.7: Text
- Self-hostingQwen3 235B-A22B Thinking 2507 and GLM-4.7Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen3 235B-A22B Thinking 2507 | Pixtral Large (25.02) | GLM-4.7 |
|---|---|---|---|---|
| Price | 50% | 49 | 26 | 48 |
| Inputs & features | 30% | 35 | 50 | 35 |
| Context window | 20% | 37 | 24 | 32 |
| Overall | 100% | 42/100 | 33/100 | 41/100 |
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 | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Pixtral Large (25.02)Mistral AI | GLM-4.7Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.8 (best) | — | 143.7 |
| ECI rank | #93 of 165 (best) | — | #94 of 165 |
| GPQA DiamondGraduate-level science questions | 80.1% | — | 83.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.7% (best) | — | 83.3% |
| SimpleQA VerifiedShort factual questions | 40.4% (best) | — | 32.2% |
| Price per million tokens | |||
| Input | $0.30 (best) | $2.00 | $0.60 |
| Output | $2.90 | $6.00 | $2.20 (best) |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $0.95 (best) | $3.00 | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 3 providers | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 204,800 tokens |
| Max output | 81,920 tokens | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Proprietary | Open |
| API model ID | — | — | glm-4.7 |
| API providers | 12 | 3 | 31 (best) |
| Released | Jul 25, 2025 | Apr 8, 2025 | Dec 22, 2025 |
| Knowledge cutoff | — | — | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 235B-A22B Thinking 2507$8.80
- Pixtral Large (25.02)$32.00
- GLM-4.7$10.40
Which should you choose?
Which is better: Qwen3 235B-A22B Thinking 2507, Pixtral Large (25.02) or GLM-4.7?
It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Pixtral Large (25.02) 33/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 for long inputs. 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, Qwen3 235B-A22B Thinking 2507, Pixtral Large (25.02) or GLM-4.7?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 API providers). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price); 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 $1.00 for GLM-4.7 (1.1× as much) and $3.00 for Pixtral Large (25.02) (3.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8, Pixtral Large (25.02) has not been scored yet and GLM-4.7 has an ECI of 143.7.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, Pixtral Large (25.02) and GLM-4.7 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three 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 204,800 for GLM-4.7 and 128,000 for Pixtral Large (25.02). Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Pixtral Large (25.02) up to 8,192, GLM-4.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B Thinking 2507 accepts text; Pixtral Large (25.02) accepts text and images; GLM-4.7 accepts text. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 and GLM-4.7 publishes its weights (Apache 2.0) and can be self-hosted; Pixtral Large (25.02) is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: GLM-4.7 Apr 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.
Related comparisons
- Qwen3 235B-A22B Thinking 2507 vs GPT-5 MiniOpenAI · ECI 145.4
- Qwen3 235B-A22B Thinking 2507 vs DeepSeek R1 0528DeepSeek · ECI 141.2
- Qwen3 235B-A22B Thinking 2507 vs Gemini 2.5 FlashGoogle · ECI 140.6
- Pixtral Large (25.02) vs Gemini 2.5 Pro Preview 06-05Google
- Pixtral Large (25.02) vs o4-mini-deep-researchOpenAI
- Pixtral Large (25.02) vs Jamba LargeAI21 Labs
