GLM-5.1 vs Qwen3.6 35B-A3B vs Kimi K2.7 Code
Qwen3.6 35B-A3B comes out ahead, 68 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
Alibaba (Qwen)Qwen3.6 35B-A3B
68/100- ECI143.9
- Price$0.248 / $1.49
- Context262K
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Qwen3.6 35B-A3B is our pick
Qwen3.6 35B-A3B is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64) and GLM-5.1 (57). It leads on price and inputs & features. 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 · Qwen3.6 35B-A3B 143.9
- Lowest priceQwen3.6 35B-A3BQwen3.6 35B-A3B $0.557 · Kimi K2.7 Code $1.71 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 35B-A3B and Kimi K2.7 CodeQwen3.6 35B-A3B 262,144 · Kimi K2.7 Code 262,144 · GLM-5.1 200,000 tokens
- Widest inputsQwen3.6 35B-A3BGLM-5.1: Text · Qwen3.6 35B-A3B: Text, Images, Audio, Video · 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 | Qwen3.6 35B-A3B | Kimi K2.7 Code |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 70 | 78 |
| Price | 25% | 34 | 62 | 39 |
| Inputs & features | 15% | 45 | 90 | 80 |
| Context window | 10% | 32 | 37 | 37 |
| Overall | 100% | 57/100 | 68/100 | 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) | Qwen3.6 35B-A3BAlibaba (Qwen) | Kimi K2.7 CodeMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 143.9 | 150.0 (best) |
| ECI rank | #51 of 148 | #83 of 148 | #49 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 84.9% | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | 20.4% | 54.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 86.7% | 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 | $0.248 (best) | $0.95 |
| Output | $4.40 | $1.49 (best) | $4.00 |
| Cached input | $0.26 | — | $0.19 (best) |
| Blended (3:1) | $2.15 | $0.557 (best) | $1.71 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Moonshot AI API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens | 65,536 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | 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 | qwen3.6-35b-a3b | kimi-k2.7-code |
| API providers | 40 | 34 | 51 (best) |
| Released | Apr 7, 2026 | Apr 17, 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
- Qwen3.6 35B-A3B$5.45
- Kimi K2.7 Code$17.50
Which should you choose?
Which is better: GLM-5.1, Qwen3.6 35B-A3B or Kimi K2.7 Code?
Qwen3.6 35B-A3B is the better all-round choice, scoring 68/100 against Kimi K2.7 Code (64) and GLM-5.1 (57). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.1, Qwen3.6 35B-A3B or Kimi K2.7 Code?
Qwen3.6 35B-A3B is cheaper at $0.248 input / $1.49 output per million tokens (official Alibaba API price). 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 $0.557 per million tokens for Qwen3.6 35B-A3B versus $1.71 for Kimi K2.7 Code (3.1× as much) and $2.15 for GLM-5.1 (3.9× 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 Qwen3.6 35B-A3B 143.9 (#83 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%, Kimi K2.7 Code 87.9%, Qwen3.6 35B-A3B 84.9%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, GLM-5.1 36.8%, Qwen3.6 35B-A3B 20.4%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Qwen3.6 35B-A3B 86.7%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.6 35B-A3B 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?
Qwen3.6 35B-A3B and Kimi K2.7 Code 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, Qwen3.6 35B-A3B up to 65,536, Kimi K2.7 Code up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Qwen3.6 35B-A3B accepts text, images, audio and video; Kimi K2.7 Code accepts text, images and video. Qwen3.6 35B-A3B 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?
Kimi K2.7 Code is the newest, released Jun 12, 2026. Qwen3.6 35B-A3B came out Apr 17, 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.
