Kimi K2.7 Code vs Inkling vs GLM-5.1
Too close to call on our weighted score (Kimi K2.7 Code 63, Inkling 63, GLM-5.1 56). The right pick depends on what you value most.
Moonshot AIKimi K2.7 Code
63/100- ECI150.2
- Price$0.95 / $4.00
- Context262K
Thinking MachinesInkling
63/100- ECI148.5
- Price$1.00 / $4.05
- Context1.05M
Z.ai (Zhipu)GLM-5.1
56/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
Too close to call
It is close. Our weighted score puts them within a point (Kimi K2.7 Code 63/100, Inkling 63/100, GLM-5.1 56/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Inkling for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.2 · GLM-5.1 149.9 · Inkling 148.5
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · Inkling $1.76 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextInklingInkling 1,048,576 · Kimi K2.7 Code 262,144 · GLM-5.1 200,000 tokens
- Widest inputsKimi K2.7 Code and InklingKimi K2.7 Code: Text, Images, Video · Inkling: Text, Images, Audio · GLM-5.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Kimi K2.7 Code | Inkling | GLM-5.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 77 | 75 | 76 |
| Price | 25% | 37 | 37 | 33 |
| Inputs & features | 15% | 80 | 70 | 45 |
| Context window | 10% | 37 | 61 | 32 |
| Overall | 100% | 63/100 | 63/100 | 56/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2.7 CodeMoonshot AI | InklingThinking Machines | GLM-5.1Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 150.2 (best) | 148.5 | 149.9 |
| ECI rank | #59 of 165 (best) | #67 of 165 | #63 of 165 |
| GPQA DiamondGraduate-level science questions | 87.9% | 88.3% | 89.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 54.0% (best) | 33.3% | 36.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 95.6% (best) | 88.9% | 93.3% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 74.2% |
| SimpleQA VerifiedShort factual questions | 36.5% | 40.3% (best) | 34.0% |
| Price per million tokens | |||
| Input | $0.95 (best) | $1.00 | $1.40 |
| Output | $4.00 (best) | $4.05 | $4.40 |
| Cached input | $0.19 | $0.17 (best) | $0.26 |
| Blended (3:1) | $1.71 (best) | $1.76 | $2.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Moonshot AI API | Official Thinking Machines API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 200,000 tokens |
| Max output | 262,144 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Open |
| API model ID | kimi-k2.7-code | thinkingmachines/Inkling:peft:262144:sampling-nvfp4 | glm-5.1 |
| API providers | 49 (best) | 20 | 46 |
| Released | Jun 12, 2026 | Jul 15, 2026 | Apr 7, 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.
- Kimi K2.7 Code$17.50
- Inkling$18.10
- GLM-5.1$22.80
Which should you choose?
Which is better: Kimi K2.7 Code, Inkling or GLM-5.1?
It is close. Our weighted score puts them within a point (Kimi K2.7 Code 63/100, Inkling 63/100, GLM-5.1 56/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Inkling for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2.7 Code, Inkling or GLM-5.1?
Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Inkling costs $1.00 input / $4.05 output per million tokens (official Thinking Machines 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 $1.76 for Inkling (1× 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 150.2 (#59 of 165), GLM-5.1 149.9 (#63 of 165) and Inkling 148.5 (#67 of 165). The confidence ranges of the top two overlap (148.1–151.9 vs 148.1–151.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Inkling 88.3%, Kimi K2.7 Code 87.9%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, GLM-5.1 36.8%, Inkling 33.3%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Inkling 88.9%; SimpleQA Verified — Inkling 40.3%, Kimi K2.7 Code 36.5%, GLM-5.1 34.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code and Inkling 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?
Inkling has the largest context window at 1,048,576 tokens, against 262,144 for Kimi K2.7 Code and 200,000 for GLM-5.1. Maximum output per response: Kimi K2.7 Code up to 262,144, Inkling up to 1,048,576, GLM-5.1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.7 Code accepts text, images and video; Inkling accepts text, images and audio; GLM-5.1 accepts text. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Inkling is the newest, released Jul 15, 2026. Kimi K2.7 Code came out Jun 12, 2026; GLM-5.1 came out Apr 7, 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 Nov 2, 2026.
