GLM-4.7 vs GPT-5 Chat vs Qwen3 235B-A22B Thinking 2507
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, GPT-5 Chat 31). 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
OpenAIGPT-5 Chat
31/100- ECI—
- Price$1.25 / $10.00
- Context400K
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, GPT-5 Chat 31/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and GPT-5 Chat 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 · GPT-5 Chat $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 tokens
- Widest inputsGPT-5 ChatGLM-4.7: Text · GPT-5 Chat: Text, Images · 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 | GPT-5 Chat | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| Price | 50% | 48 | 23 | 49 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 44 | 37 |
| Overall | 100% | 41/100 | 31/100 | 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) | GPT-5 ChatOpenAI | 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 | $1.25 | $0.30 (best) |
| Output | $2.20 (best) | $10.00 | $2.90 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $3.44 | $0.95 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 5 providers | Median of 11 providers |
| Limits | |||
| Context window | 204,800 tokens | 400,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 81,920 tokens |
| 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 | Yes | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | glm-4.7 | — | — |
| API providers | 31 (best) | 5 | 12 |
| Released | Dec 22, 2025 | Aug 7, 2025 | Jul 25, 2025 |
| Knowledge cutoff | Apr 2025 | Sep 30, 2024 | — |
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
- GPT-5 Chat$32.50
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: GLM-4.7, GPT-5 Chat 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, GPT-5 Chat 31/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and GPT-5 Chat 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, GPT-5 Chat 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); GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 5 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 $3.44 for GPT-5 Chat (3.6× 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, GPT-5 Chat 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, GPT-5 Chat 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 GPT-5 Chat does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5 Chat has the largest context window at 400,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, GPT-5 Chat up to 128,000, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; GPT-5 Chat accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text. GPT-5 Chat handles the widest range of inputs.
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; GPT-5 Chat is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. GPT-5 Chat came out Aug 7, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GPT-5 Chat Sep 30, 2024.
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.
