Codestral 25.08 vs GLM-4.7 vs Claude Haiku 4.5
Too close to call on our weighted score (Claude Haiku 4.5 47, Codestral 25.08 47, GLM-4.7 41). The right pick depends on what you value most.
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
AnthropicClaude Haiku 4.5
47/100- ECI142.2
- Price$1.00 / $5.00
- Context200K
Too close to call
It is close. Our weighted score puts them within a point (Claude Haiku 4.5 47/100, Codestral 25.08 47/100, GLM-4.7 41/100), so choose by what matters most for your work: Codestral 25.08 on price and GLM-4.7 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 priceCodestral 25.08Codestral 25.08 $0.45 · GLM-4.7 $1.00 · Claude Haiku 4.5 $2.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.7GLM-4.7 204,800 · Claude Haiku 4.5 200,000 · Codestral 25.08 128,000 tokens
- Widest inputsClaude Haiku 4.5Codestral 25.08: Text · GLM-4.7: Text · Claude Haiku 4.5: Text, Images, PDFs
- Self-hostingGLM-4.7Publishes downloadable weights
| Measure | Weight | Codestral 25.08 | GLM-4.7 | Claude Haiku 4.5 |
|---|---|---|---|---|
| Price | 50% | 64 | 48 | 34 |
| Inputs & features | 30% | 35 | 35 | 80 |
| Context window | 20% | 24 | 32 | 32 |
| Overall | 100% | 47/100 | 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 | Codestral 25.08Mistral AI | GLM-4.7Z.ai (Zhipu) | Claude Haiku 4.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.7 (best) | 142.2 |
| ECI rank | — | #94 of 165 (best) | #102 of 165 |
| GPQA DiamondGraduate-level science questions | — | 83.3% (best) | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% (best) | 66.7% |
| SimpleQA VerifiedShort factual questions | — | 32.2% (best) | 13.2% |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.60 | $1.00 |
| Output | $0.90 (best) | $2.20 | $5.00 |
| Cached input | $0.03 (best) | $0.11 | $0.10 |
| Blended (3:1) | $0.45 (best) | $1.00 | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Z.AI API | Official Anthropic API |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens (best) | 200,000 tokens |
| Max output | 8,192 tokens | 131,072 tokens (best) | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | codestral-2508 | glm-4.7 | claude-haiku-4-5 |
| API providers | 11 | 31 | 48 (best) |
| Released | Jul 30, 2025 | Dec 22, 2025 | Oct 15, 2025 |
| Knowledge cutoff | — | Apr 2025 | Feb 28, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Codestral 25.08$4.80
- GLM-4.7$10.40
- Claude Haiku 4.5$20.00
Which should you choose?
Which is better: Codestral 25.08, GLM-4.7 or Claude Haiku 4.5?
It is close. Our weighted score puts them within a point (Claude Haiku 4.5 47/100, Codestral 25.08 47/100, GLM-4.7 41/100), so choose by what matters most for your work: Codestral 25.08 on price and GLM-4.7 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, Codestral 25.08, GLM-4.7 or Claude Haiku 4.5?
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); Claude Haiku 4.5 costs $1.00 input / $5.00 output per million tokens (official Anthropic 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) and $2.00 for Claude Haiku 4.5 (4.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral 25.08 has not been scored yet, GLM-4.7 has an ECI of 143.7 and Claude Haiku 4.5 has an ECI of 142.2.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral 25.08, GLM-4.7 and Claude Haiku 4.5 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?
GLM-4.7 has the largest context window at 204,800 tokens, against 200,000 for Claude Haiku 4.5 and 128,000 for Codestral 25.08. Maximum output per response: Codestral 25.08 up to 8,192, GLM-4.7 up to 131,072, Claude Haiku 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Codestral 25.08 accepts text; GLM-4.7 accepts text; Claude Haiku 4.5 accepts text, images and PDFs. Claude Haiku 4.5 handles the widest range of inputs.
Are any of these open source?
GLM-4.7 publishes its weights and can be self-hosted; Codestral 25.08 and Claude Haiku 4.5 is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Claude Haiku 4.5 came out Oct 15, 2025; Codestral 25.08 came out Jul 30, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Claude Haiku 4.5 Feb 28, 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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