GLM-5.1 vs Inkling vs Kimi K2.7 Code
Kimi K2.7 Code comes out ahead, 64 to 59 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
Thinking MachinesInkling
59/100- ECI148.6
- Price$3.74 / $9.36
- Context1.05M
- 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 Inkling (59) and GLM-5.1 (57). It leads on price and inputs & features. Inkling wins on 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 · Inkling 148.6
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · GLM-5.1 $2.15 · Inkling $5.14 per 1M tokens (3:1 blend)
- Longest contextInklingInkling 1,048,576 · Kimi K2.7 Code 262,144 · GLM-5.1 200,000 tokens
- Widest inputsInkling and Kimi K2.7 CodeGLM-5.1: Text · Inkling: Text, Images, Audio · Kimi K2.7 Code: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5.1 | Inkling | Kimi K2.7 Code |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 76 | 78 |
| Price | 25% | 34 | 16 | 39 |
| Inputs & features | 15% | 45 | 70 | 80 |
| Context window | 10% | 32 | 61 | 37 |
| Overall | 100% | 57/100 | 59/100 | 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) | InklingThinking Machines | Kimi K2.7 CodeMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 148.6 | 150.0 (best) |
| ECI rank | #51 of 148 | #57 of 148 | #49 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 88.3% | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | 33.3% | 54.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 88.9% | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | 40.3% (best) | 36.5% |
| Price per million tokens | |||
| Input | $1.40 | $3.74 | $0.95 (best) |
| Output | $4.40 | $9.36 | $4.00 (best) |
| Cached input | $0.26 | $0.748 | $0.19 (best) |
| Blended (3:1) | $2.15 | $5.14 | $1.71 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Thinking Machines API | Official Moonshot AI API |
| Limits | |||
| Context window | 200,000 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | Yes |
| 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 | glm-5.1 | thinkingmachines/Inkling:peft:262144 | kimi-k2.7-code |
| API providers | 40 | 23 | 51 (best) |
| Released | Apr 7, 2026 | Jul 15, 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
- Inkling$56.12
- Kimi K2.7 Code$17.50
Which should you choose?
Which is better: GLM-5.1, Inkling or Kimi K2.7 Code?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against Inkling (59) and GLM-5.1 (57). It leads on price and inputs & features. Inkling wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Inkling 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-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price); Inkling costs $3.74 input / $9.36 output per million tokens (official Thinking Machines 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 $2.15 for GLM-5.1 (1.3× as much) and $5.14 for Inkling (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 Inkling 148.6 (#57 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%, 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 Inkling 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?
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: GLM-5.1 up to 131,072, Inkling up to 1,048,576, Kimi K2.7 Code up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Inkling accepts text, images and audio; Kimi K2.7 Code accepts text, images and video. Inkling 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 Oct 4, 2026.
