GLM-5.1 vs GLM-5-Turbo vs Kimi K2.7 Code
Kimi K2.7 Code comes out ahead, 51 to 38 and 37 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.1
37/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
- Z.ai (Zhipu)
GLM-5-Turbo
38/100- ECI—
- Price$1.20 / $4.00
- Context200K
- Our pick
Moonshot AIKimi K2.7 Code
51/100- 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 51/100 against GLM-5-Turbo (38) and GLM-5.1 (37). It leads on price, inputs & features and 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 priceKimi K2.7 CodeKimi K2.7 Code $1.71 · GLM-5-Turbo $1.90 · 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 · GLM-5-Turbo 200,000 tokens
- Widest inputsKimi K2.7 CodeGLM-5.1: Text · GLM-5-Turbo: Text · Kimi K2.7 Code: Text, Images, Video
- Self-hostingGLM-5.1 and Kimi K2.7 CodePublishes downloadable weights
| Measure | Weight | GLM-5.1 | GLM-5-Turbo | Kimi K2.7 Code |
|---|---|---|---|---|
| Price | 50% | 34 | 37 | 39 |
| Inputs & features | 30% | 45 | 45 | 80 |
| Context window | 20% | 32 | 32 | 37 |
| Overall | 100% | 37/100 | 38/100 | 51/100 |
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-5.1Z.ai (Zhipu) | GLM-5-TurboZ.ai (Zhipu) | Kimi K2.7 CodeMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | — | 150.0 (best) |
| ECI rank | #51 of 148 | — | #49 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | — | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | — | 54.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | — | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | — | 36.5% (best) |
| Price per million tokens | |||
| Input | $1.40 | $1.20 | $0.95 (best) |
| Output | $4.40 | $4.00 (best) | $4.00 (best) |
| Cached input | $0.26 | $0.24 | $0.19 (best) |
| Blended (3:1) | $2.15 | $1.90 | $1.71 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Z.AI API | Official Moonshot AI API |
| Limits | |||
| Context window | 200,000 tokens | 200,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | 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 | glm-5-turbo | kimi-k2.7-code |
| API providers | 40 | 17 | 51 (best) |
| Released | Apr 7, 2026 | Mar 16, 2026 | Jun 12, 2026 |
| Knowledge cutoff | — | — | 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
- GLM-5-Turbo$20.00
- Kimi K2.7 Code$17.50
Which should you choose?
Which is better: GLM-5.1, GLM-5-Turbo or Kimi K2.7 Code?
Kimi K2.7 Code is the better all-round choice, scoring 51/100 against GLM-5-Turbo (38) and GLM-5.1 (37). It leads on price, inputs & features and 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-5.1, GLM-5-Turbo 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). GLM-5-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI 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 $1.90 for GLM-5-Turbo (1.1× as much) and $2.15 for GLM-5.1 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5.1 has an ECI of 149.9, GLM-5-Turbo has not been scored yet and Kimi K2.7 Code has an ECI of 150.0.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo and Kimi K2.7 Code 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?
Kimi K2.7 Code has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1 and 200,000 for GLM-5-Turbo. Maximum output per response: GLM-5.1 up to 131,072, GLM-5-Turbo up to 131,072, Kimi K2.7 Code up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; GLM-5-Turbo accepts text; Kimi K2.7 Code accepts text, images and video. Kimi K2.7 Code 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; GLM-5-Turbo is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; GLM-5-Turbo came out Mar 16, 2026. Knowledge cutoff: 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.
