GLM-5.1 vs Kimi K2.7 Code vs Qwen3.8 27B
Too close to call on our weighted score (Qwen3.8 27B 67, Kimi K2.7 Code 64, GLM-5.1 57). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Alibaba (Qwen)Qwen3.8 27B
67/100- ECI149.4
- Price$0.40 / $2.50
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen3.8 27B 67/100, Kimi K2.7 Code 64/100, GLM-5.1 57/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Qwen3.8 27B on price. 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 · Qwen3.8 27B 149.4
- Lowest priceQwen3.8 27BQwen3.8 27B $0.925 · Kimi K2.7 Code $1.71 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 Code and Qwen3.8 27BKimi K2.7 Code 262,144 · Qwen3.8 27B 262,144 · GLM-5.1 200,000 tokens
- Widest inputsKimi K2.7 Code and Qwen3.8 27BGLM-5.1: Text · Kimi K2.7 Code: Text, Images, Video · Qwen3.8 27B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5.1 | Kimi K2.7 Code | Qwen3.8 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 78 | 77 |
| Price | 25% | 34 | 39 | 51 |
| Inputs & features | 15% | 45 | 80 | 80 |
| Context window | 10% | 32 | 37 | 37 |
| Overall | 100% | 57/100 | 64/100 | 67/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.1Z.ai (Zhipu) | Kimi K2.7 CodeMoonshot AI | Qwen3.8 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 150.0 (best) | 149.4 |
| ECI rank | #51 of 148 | #49 of 148 (best) | #53 of 148 |
| 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 | $0.95 | $0.40 (best) |
| Output | $4.40 | $4.00 | $2.50 (best) |
| Cached input | $0.26 | $0.19 (best) | — |
| Blended (3:1) | $2.15 | $1.71 | $0.925 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Moonshot AI API | Median of 39 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens | 262,144 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5.1 | kimi-k2.7-code | — |
| API providers | 40 | 51 (best) | 41 |
| Released | Apr 7, 2026 | Jun 12, 2026 | Aug 14, 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
- Kimi K2.7 Code$17.50
- Qwen3.8 27B$9.00
Which should you choose?
Which is better: GLM-5.1, Kimi K2.7 Code or Qwen3.8 27B?
It is close. Our weighted score puts them within 3 points (Qwen3.8 27B 67/100, Kimi K2.7 Code 64/100, GLM-5.1 57/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Qwen3.8 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Kimi K2.7 Code or Qwen3.8 27B?
Qwen3.8 27B is cheaper at $0.40 input / $2.50 output per million tokens (median across 39 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 $0.925 per million tokens for Qwen3.8 27B versus $1.71 for Kimi K2.7 Code (1.9× as much) and $2.15 for GLM-5.1 (2.3× 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 Qwen3.8 27B 149.4 (#53 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.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code and Qwen3.8 27B 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 and Qwen3.8 27B have the largest context windows (262,144 and 262,144 tokens), against 200,000 for GLM-5.1. Maximum output per response: GLM-5.1 up to 131,072, Kimi K2.7 Code up to 262,144, Qwen3.8 27B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Kimi K2.7 Code accepts text, images and video; Qwen3.8 27B accepts text, images and video. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 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.
