Baichuan M2 32B vs GLM-4.7 vs Claude Haiku 4.5
Baichuan M2 32B comes out ahead, 58 to 47 and 41 on our weighted score, and it is the cheaper option too.
- Our pick
BaichuanBaichuan M2 32B
58/100- ECI—
- Price$0.07 / $0.07
- Context131K
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
Baichuan M2 32B is our pick
Baichuan M2 32B is the better all-round choice, scoring 58/100 against Claude Haiku 4.5 (47) and GLM-4.7 (41). It leads on price. Claude Haiku 4.5 wins on inputs & features. 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 priceBaichuan M2 32BBaichuan M2 32B $0.07 · 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 · Baichuan M2 32B 131,072 tokens
- Widest inputsClaude Haiku 4.5Baichuan M2 32B: Text · GLM-4.7: Text · Claude Haiku 4.5: Text, Images, PDFs
- Self-hostingBaichuan M2 32B and GLM-4.7Publishes downloadable weights (Apache-2.0)
| Measure | Weight | Baichuan M2 32B | GLM-4.7 | Claude Haiku 4.5 |
|---|---|---|---|---|
| Price | 50% | 100 | 48 | 34 |
| Inputs & features | 30% | 10 | 35 | 80 |
| Context window | 20% | 25 | 32 | 32 |
| Overall | 100% | 58/100 | 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 | Baichuan M2 32BBaichuan | 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 | $0.07 (best) | $0.60 | $1.00 |
| Output | $0.07 (best) | $2.20 | $5.00 |
| Cached input | — | $0.11 | $0.10 (best) |
| Blended (3:1) | $0.07 (best) | $1.00 | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Z.AI API | Official Anthropic API |
| Limits | |||
| Context window | 131,072 tokens | 204,800 tokens (best) | 200,000 tokens |
| Max output | 131,072 tokens (best) | 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 | Yes | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | glm-4.7 | claude-haiku-4-5 |
| API providers | 1 | 31 | 48 (best) |
| Released | Aug 13, 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.
- Baichuan M2 32B$0.84
- GLM-4.7$10.40
- Claude Haiku 4.5$20.00
Which should you choose?
Which is better: Baichuan M2 32B, GLM-4.7 or Claude Haiku 4.5?
Baichuan M2 32B is the better all-round choice, scoring 58/100 against Claude Haiku 4.5 (47) and GLM-4.7 (41). It leads on price. Claude Haiku 4.5 wins on inputs & features. 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, Baichuan M2 32B, GLM-4.7 or Claude Haiku 4.5?
Baichuan M2 32B is cheaper at $0.07 input / $0.07 output per million tokens (median across 1 API provider). 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 $0.07 per million tokens for Baichuan M2 32B versus $1.00 for GLM-4.7 (14× as much) and $2.00 for Claude Haiku 4.5 (29× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Baichuan M2 32B 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 Baichuan M2 32B, 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. Note that Baichuan M2 32B does not support tool calling, which most coding agents need.
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 131,072 for Baichuan M2 32B. Maximum output per response: Baichuan M2 32B up to 131,072, GLM-4.7 up to 131,072, Claude Haiku 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Baichuan M2 32B 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?
Baichuan M2 32B and GLM-4.7 publishes its weights (Apache-2.0) and can be self-hosted; 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; Baichuan M2 32B came out Aug 13, 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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