GLM-4.7 vs Codestral 25.08
Codestral 25.08 comes out ahead, 47 to 41 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
- Our pick
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
Add a model
Make it a three-way comparison.
Codestral 25.08 is our pick
Codestral 25.08 is the better all-round choice, scoring 47/100 against GLM-4.7 (41). It leads on price. GLM-4.7 wins on context window. 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 priceCodestral 25.08Codestral 25.08 $0.45 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.7GLM-4.7 204,800 · Codestral 25.08 128,000 tokens
- Widest inputsSame inputsGLM-4.7: Text · Codestral 25.08: Text
- Self-hostingGLM-4.7Publishes downloadable weights
| Measure | Weight | GLM-4.7 | Codestral 25.08 |
|---|---|---|---|
| Price | 50% | 48 | 64 |
| Inputs & features | 30% | 35 | 35 |
| Context window | 20% | 32 | 24 |
| Overall | 100% | 41/100 | 47/100 |
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) | Codestral 25.08Mistral AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 143.7 | — |
| ECI rank | #94 of 165 | — |
| GPQA DiamondGraduate-level science questions | 83.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | — |
| SimpleQA VerifiedShort factual questions | 32.2% | — |
| Price per million tokens | ||
| Input | $0.60 | $0.30 (best) |
| Output | $2.20 | $0.90 (best) |
| Cached input | $0.11 | $0.03 (best) |
| Blended (3:1) | $1.00 | $0.45 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Mistral API |
| Limits | ||
| Context window | 204,800 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | glm-4.7 | codestral-2508 |
| API providers | 31 (best) | 11 |
| Released | Dec 22, 2025 | Jul 30, 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
- Codestral 25.08$4.80
Which should you choose?
Which is better: GLM-4.7 or Codestral 25.08?
Codestral 25.08 is the better all-round choice, scoring 47/100 against GLM-4.7 (41). It leads on price. GLM-4.7 wins on context window. 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 or Codestral 25.08?
Codestral 25.08 is cheaper at $0.30 input / $0.90 output per million tokens (official Mistral API price). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Codestral 25.08 versus $1.00 for GLM-4.7 (2.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.7 has an ECI of 143.7 and Codestral 25.08 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7 and Codestral 25.08 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?
GLM-4.7 has the largest context window at 204,800 tokens, against 128,000 for Codestral 25.08. Maximum output per response: GLM-4.7 up to 131,072, Codestral 25.08 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Codestral 25.08 accepts text. They handle the same number of input types.
Are any of these open source?
GLM-4.7 publishes its weights and can be self-hosted; Codestral 25.08 is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Codestral 25.08 came out Jul 30, 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.
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