GLM-5.1 vs Inkling Small vs Kimi K2.7 Code
Inkling Small comes out ahead, 70 to 64 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
- Our pick
Thinking MachinesInkling Small
70/100- ECI150.2
- Price$0.50 / $1.20
- Context1.05M
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Inkling Small is our pick
Inkling Small is the better all-round choice, scoring 70/100 against Kimi K2.7 Code (64) and GLM-5.1 (57). It leads on price and context window. Kimi K2.7 Code wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityInkling SmallCapabilities Index (ECI): Inkling Small 150.2 · Kimi K2.7 Code 150.0 · GLM-5.1 149.9
- Lowest priceInkling SmallInkling Small $0.675 · Kimi K2.7 Code $1.71 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · Kimi K2.7 Code 262,144 · GLM-5.1 200,000 tokens
- Widest inputsInkling Small and Kimi K2.7 CodeGLM-5.1: Text · Inkling Small: 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 Small | Kimi K2.7 Code |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 78 | 78 |
| Price | 25% | 34 | 58 | 39 |
| Inputs & features | 15% | 45 | 70 | 80 |
| Context window | 10% | 32 | 61 | 37 |
| Overall | 100% | 57/100 | 70/100 | 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) | Inkling SmallThinking Machines | Kimi K2.7 CodeMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 150.2 (best) | 150.0 |
| ECI rank | #51 of 148 | #47 of 148 (best) | #49 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 88.5% | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | 46.3% | 54.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 90.0% | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | 19.1% | 36.5% (best) |
| Price per million tokens | |||
| Input | $1.40 | $0.50 (best) | $0.95 |
| Output | $4.40 | $1.20 (best) | $4.00 |
| Cached input | $0.26 | — | $0.19 (best) |
| Blended (3:1) | $2.15 | $0.675 (best) | $1.71 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 11 providers | 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 | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Open |
| API model ID | glm-5.1 | — | kimi-k2.7-code |
| API providers | 40 | 11 | 51 (best) |
| Released | Apr 7, 2026 | Jul 30, 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 Small$7.40
- Kimi K2.7 Code$17.50
Which should you choose?
Which is better: GLM-5.1, Inkling Small or Kimi K2.7 Code?
Inkling Small is the better all-round choice, scoring 70/100 against Kimi K2.7 Code (64) and GLM-5.1 (57). It leads on price and context window. Kimi K2.7 Code wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Inkling Small or Kimi K2.7 Code?
Inkling Small is cheaper at $0.50 input / $1.20 output per million tokens (median across 11 API providers). Kimi K2.7 Code costs $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). At a typical mix of three input tokens to one output token, that is $0.675 per million tokens for Inkling Small versus $1.71 for Kimi K2.7 Code (2.5× as much) and $2.15 for GLM-5.1 (3.2× as much).
Which scores higher on benchmarks?
Inkling Small scores higher on the Capabilities Index (ECI): Inkling Small 150.2 (#47 of 148), Kimi K2.7 Code 150.0 (#49 of 148) and GLM-5.1 149.9 (#51 of 148). The confidence ranges of the top two overlap (147.5–152.1 vs 148.1–151.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Inkling Small 88.5%, Kimi K2.7 Code 87.9%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, Inkling Small 46.3%, GLM-5.1 36.8%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Inkling Small 90.0%; SimpleQA Verified — Kimi K2.7 Code 36.5%, GLM-5.1 34.0%, Inkling Small 19.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Inkling Small and Kimi K2.7 Code yet, so there is no like-for-like coding score. On overall capability, Inkling Small 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 Small 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 Small 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 Small accepts text, images and audio; Kimi K2.7 Code accepts text, images and video. Inkling Small 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 Small is the newest, released Jul 30, 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.
