GLM-5.1 vs Kimi K2.7 Code vs Qwen3 235B-A22B
Kimi K2.7 Code comes out ahead, 64 to 57 and 51 on our weighted score, though Qwen3 235B-A22B is 28% cheaper per token.
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
- Our pick
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Alibaba (Qwen)Qwen3 235B-A22B
51/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
Kimi K2.7 Code is our pick
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and Qwen3 235B-A22B (51). It leads on inputs & features and context window. Qwen3 235B-A22B wins 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 235B-A22B 139.4
- Lowest priceQwen3 235B-A22BQwen3 235B-A22B $1.23 · Kimi K2.7 Code $1.71 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · GLM-5.1 200,000 · Qwen3 235B-A22B 131,072 tokens
- Widest inputsKimi K2.7 CodeGLM-5.1: Text · Kimi K2.7 Code: Text, Images, Video · Qwen3 235B-A22B: Text
- 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 235B-A22B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 78 | 65 |
| Price | 25% | 34 | 39 | 46 |
| Inputs & features | 15% | 45 | 80 | 35 |
| Context window | 10% | 32 | 37 | 24 |
| Overall | 100% | 57/100 | 64/100 | 51/100 |
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 235B-A22BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 150.0 (best) | 139.4 |
| ECI rank | #51 of 148 | #49 of 148 (best) | #103 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 87.9% | 70.7% |
| 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 | $0.70 (best) |
| Output | $4.40 | $4.00 | $2.80 (best) |
| Cached input | $0.26 | $0.19 (best) | — |
| Blended (3:1) | $2.15 | $1.71 | $1.23 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Moonshot AI API | Official Alibaba API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5.1 | kimi-k2.7-code | qwen3-235b-a22b |
| API providers | 40 | 51 (best) | 7 |
| Released | Apr 7, 2026 | Jun 12, 2026 | Apr 28, 2025 |
| Knowledge cutoff | — | Jan 2025 | Apr 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 235B-A22B$12.60
Which should you choose?
Which is better: GLM-5.1, Kimi K2.7 Code or Qwen3 235B-A22B?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and Qwen3 235B-A22B (51). It leads on inputs & features and context window. Qwen3 235B-A22B wins 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 235B-A22B?
Qwen3 235B-A22B is cheaper at $0.70 input / $2.80 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 $1.23 per million tokens for Qwen3 235B-A22B versus $1.71 for Kimi K2.7 Code (1.4× as much) and $2.15 for GLM-5.1 (1.8× 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 235B-A22B 139.4 (#103 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 235B-A22B 70.7%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code and Qwen3 235B-A22B 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 has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1 and 131,072 for Qwen3 235B-A22B. Maximum output per response: GLM-5.1 up to 131,072, Kimi K2.7 Code up to 262,144, Qwen3 235B-A22B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Kimi K2.7 Code accepts text, images and video; Qwen3 235B-A22B accepts text. 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?
Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; Qwen3 235B-A22B came out Apr 28, 2025. Knowledge cutoff: Kimi K2.7 Code Jan 2025, Qwen3 235B-A22B Apr 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.
