GLM-4.7 vs Qwen MT Plus vs Qwen3 235B-A22B Thinking 2507
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, Qwen MT Plus 11). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)Qwen MT Plus
11/100- ECI—
- Price$2.46 / $7.37
- Context16K
- Alibaba (Qwen)
Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
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, Qwen MT Plus 11/100), 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 · Qwen MT Plus $3.69 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Qwen MT Plus 16,384 tokens
- Widest inputsSame inputsGLM-4.7: Text · Qwen MT Plus: Text · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingGLM-4.7 and Qwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GLM-4.7 | Qwen MT Plus | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| Price | 50% | 48 | 22 | 49 |
| Inputs & features | 30% | 35 | 0 | 35 |
| Context window | 20% | 32 | 0 | 37 |
| Overall | 100% | 41/100 | 11/100 | 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 | GLM-4.7Z.ai (Zhipu) | Qwen MT PlusAlibaba (Qwen) | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.7 | — | 143.8 (best) |
| ECI rank | #94 of 165 | — | #93 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% (best) | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | — | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | 32.2% | — | 40.4% (best) |
| Price per million tokens | |||
| Input | $0.60 | $2.46 | $0.30 (best) |
| Output | $2.20 (best) | $7.37 | $2.90 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $3.69 | $0.95 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 11 providers |
| Limits | |||
| Context window | 204,800 tokens | 16,384 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 8,192 tokens | 81,920 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | glm-4.7 | qwen-mt-plus | — |
| API providers | 31 (best) | 3 | 12 |
| Released | Dec 22, 2025 | Jul 24, 2025 | Jul 25, 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.
- GLM-4.7$10.40
- Qwen MT Plus$39.34
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: GLM-4.7, Qwen MT Plus or Qwen3 235B-A22B Thinking 2507?
It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Qwen MT Plus 11/100), 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, GLM-4.7, Qwen MT Plus 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). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price); Qwen MT Plus costs $2.46 input / $7.37 output per million tokens (official Alibaba API price). 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 $3.69 for Qwen MT Plus (3.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.7, Qwen MT Plus 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 GLM-4.7, Qwen MT Plus 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. Note that Qwen MT Plus does not support tool calling, which most coding agents need.
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 16,384 for Qwen MT Plus. Maximum output per response: GLM-4.7 up to 131,072, Qwen MT Plus up to 8,192, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen MT Plus accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. They handle the same number of input types.
Are any of these open source?
GLM-4.7 and Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen MT Plus 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; Qwen MT Plus came out Jul 24, 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.
