Claude Haiku 4.5 vs Codestral 25.08 vs GLM-4.7
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.
AnthropicClaude Haiku 4.5
47/100- ECI142.2
- Price$1.00 / $5.00
- Context200K
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
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 4.5Claude Haiku 4.5: Text, Images, PDFs · Codestral 25.08: Text · GLM-4.7: Text
- Self-hostingGLM-4.7Publishes downloadable weights
| Measure | Weight | Claude Haiku 4.5 | Codestral 25.08 | GLM-4.7 |
|---|---|---|---|---|
| Price | 50% | 34 | 64 | 48 |
| Inputs & features | 30% | 80 | 35 | 35 |
| Context window | 20% | 32 | 24 | 32 |
| Overall | 100% | 47/100 | 47/100 | 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 | Claude Haiku 4.5Anthropic | Codestral 25.08Mistral AI | GLM-4.7Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 142.2 | — | 143.7 (best) |
| ECI rank | #102 of 165 | — | #94 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 71.2% | — | 83.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.7% | — | 83.3% (best) |
| SimpleQA VerifiedShort factual questions | 13.2% | — | 32.2% (best) |
| Price per million tokens | |||
| Input | $1.00 | $0.30 (best) | $0.60 |
| Output | $5.00 | $0.90 (best) | $2.20 |
| Cached input | $0.10 | $0.03 (best) | $0.11 |
| Blended (3:1) | $2.00 | $0.45 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Anthropic API | Official Mistral API | Official Z.AI API |
| Limits | |||
| Context window | 200,000 tokens | 128,000 tokens | 204,800 tokens (best) |
| Max output | 64,000 tokens | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | claude-haiku-4-5 | codestral-2508 | glm-4.7 |
| API providers | 48 (best) | 11 | 31 |
| Released | Oct 15, 2025 | Jul 30, 2025 | Dec 22, 2025 |
| Knowledge cutoff | Feb 28, 2025 | — | 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.
- Claude Haiku 4.5$20.00
- Codestral 25.08$4.80
- GLM-4.7$10.40
Which should you choose?
Which is better: Claude Haiku 4.5, Codestral 25.08 or GLM-4.7?
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, Claude Haiku 4.5, Codestral 25.08 or GLM-4.7?
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. Claude Haiku 4.5 has an ECI of 142.2, Codestral 25.08 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 Claude Haiku 4.5, Codestral 25.08 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?
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: Claude Haiku 4.5 up to 64,000, Codestral 25.08 up to 8,192, GLM-4.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 4.5 accepts text, images and PDFs; Codestral 25.08 accepts text; GLM-4.7 accepts text. 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; Claude Haiku 4.5 and Codestral 25.08 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: Claude Haiku 4.5 Feb 28, 2025, 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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