GLM-5.1 vs Claude Haiku 4.5 vs Kimi K2.7 Code
Kimi K2.7 Code comes out ahead, 64 to 58 and 57 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
AnthropicClaude Haiku 4.5
58/100- ECI142.4
- Price$1.00 / $5.00
- Context200K
- Our pick
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Kimi K2.7 Code is our pick
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against Claude Haiku 4.5 (58) and GLM-5.1 (57). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.0 · GLM-5.1 149.9 · Claude Haiku 4.5 142.4
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · Claude Haiku 4.5 $2.00 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · GLM-5.1 200,000 · Claude Haiku 4.5 200,000 tokens
- Widest inputsClaude Haiku 4.5 and Kimi K2.7 CodeGLM-5.1: Text · Claude Haiku 4.5: Text, Images, PDFs · Kimi K2.7 Code: Text, Images, Video
- Self-hostingGLM-5.1 and Kimi K2.7 CodePublishes downloadable weights
| Measure | Weight | GLM-5.1 | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 69 | 78 |
| Price | 25% | 34 | 36 | 39 |
| Inputs & features | 15% | 45 | 80 | 80 |
| Context window | 10% | 32 | 32 | 37 |
| Overall | 100% | 57/100 | 58/100 | 64/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.1Z.ai (Zhipu) | Claude Haiku 4.5Anthropic | Kimi K2.7 CodeMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 142.4 | 150.0 (best) |
| ECI rank | #51 of 148 | #90 of 148 | #49 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 71.2% | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | — | 54.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 66.7% | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | 13.2% | 36.5% (best) |
| Price per million tokens | |||
| Input | $1.40 | $1.00 | $0.95 (best) |
| Output | $4.40 | $5.00 | $4.00 (best) |
| Cached input | $0.26 | $0.10 (best) | $0.19 |
| Blended (3:1) | $2.15 | $2.00 | $1.71 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Anthropic API | Official Moonshot AI API |
| Limits | |||
| Context window | 200,000 tokens | 200,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 64,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5.1 | claude-haiku-4-5 | kimi-k2.7-code |
| API providers | 40 | 34 | 51 (best) |
| Released | Apr 7, 2026 | Oct 15, 2025 | Jun 12, 2026 |
| Knowledge cutoff | — | Feb 28, 2025 | Jan 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-5.1$22.80
- Claude Haiku 4.5$20.00
- Kimi K2.7 Code$17.50
Which should you choose?
Which is better: GLM-5.1, Claude Haiku 4.5 or Kimi K2.7 Code?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against Claude Haiku 4.5 (58) and GLM-5.1 (57). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Claude Haiku 4.5 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Claude Haiku 4.5 costs $1.00 input / $5.00 output per million tokens (official Anthropic API price); GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.7 Code versus $2.00 for Claude Haiku 4.5 (1.2× as much) and $2.15 for GLM-5.1 (1.3× as much).
Which scores higher on benchmarks?
Kimi K2.7 Code scores higher on the Capabilities Index (ECI): Kimi K2.7 Code 150.0 (#49 of 148), GLM-5.1 149.9 (#51 of 148) and Claude Haiku 4.5 142.4 (#90 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 148.0–151.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Kimi K2.7 Code 87.9%, Claude Haiku 4.5 71.2%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Claude Haiku 4.5 66.7%; SimpleQA Verified — Kimi K2.7 Code 36.5%, GLM-5.1 34.0%, Claude Haiku 4.5 13.2%.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Haiku 4.5 and Kimi K2.7 Code yet, so there is no like-for-like coding score. On overall capability, Kimi K2.7 Code leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2.7 Code has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1 and 200,000 for Claude Haiku 4.5. Maximum output per response: GLM-5.1 up to 131,072, Claude Haiku 4.5 up to 64,000, Kimi K2.7 Code up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Claude Haiku 4.5 accepts text, images and PDFs; Kimi K2.7 Code accepts text, images and video. Claude Haiku 4.5 handles the widest range of inputs.
Are any of these open source?
GLM-5.1 and Kimi K2.7 Code publishes its weights and can be self-hosted; Claude Haiku 4.5 is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; Claude Haiku 4.5 came out Oct 15, 2025. Knowledge cutoff: Claude Haiku 4.5 Feb 28, 2025, Kimi K2.7 Code Jan 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 Oct 4, 2026.
