GLM-5.1 vs Kimi K2.7 Code vs Qwen3 Max
Kimi K2.7 Code comes out ahead, 64 to 57 and 50 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
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Alibaba (Qwen)Qwen3 Max
50/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
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 Max (50). 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 Max 142.4
- Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · GLM-5.1 $2.15 · Qwen3 Max $2.40 per 1M tokens (3:1 blend)
- Longest contextKimi K2.7 Code and Qwen3 MaxKimi K2.7 Code 262,144 · Qwen3 Max 262,144 · GLM-5.1 200,000 tokens
- Widest inputsKimi K2.7 CodeGLM-5.1: Text · Kimi K2.7 Code: Text, Images, Video · Qwen3 Max: Text
- Self-hostingGLM-5.1 and Kimi K2.7 CodePublishes downloadable weights
| Measure | Weight | GLM-5.1 | Kimi K2.7 Code | Qwen3 Max |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 78 | 78 | 68 |
| Price | 25% | 34 | 39 | 32 |
| Inputs & features | 15% | 45 | 80 | 25 |
| Context window | 10% | 32 | 37 | 37 |
| Overall | 100% | 57/100 | 64/100 | 50/100 |
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 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.9 | 150.0 (best) | 142.4 |
| ECI rank | #51 of 148 | #49 of 148 (best) | #91 of 148 |
| GPQA DiamondGraduate-level science questions | 89.9% (best) | 87.9% | 72.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 36.8% | 54.0% (best) | 19.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% | 95.6% (best) | 73.3% |
| SWE-bench VerifiedFixing real GitHub issues | 74.2% | — | — |
| SimpleQA VerifiedShort factual questions | 34.0% | 36.5% | 48.8% (best) |
| Price per million tokens | |||
| Input | $1.40 | $0.95 (best) | $1.20 |
| Output | $4.40 | $4.00 (best) | $6.00 |
| Cached input | $0.26 | $0.19 (best) | — |
| Blended (3:1) | $2.15 | $1.71 (best) | $2.40 |
| 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) | 262,144 tokens (best) |
| Max output | 131,072 tokens | 262,144 tokens (best) | 65,536 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 | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5.1 | kimi-k2.7-code | qwen3-max |
| API providers | 40 | 51 (best) | 16 |
| Released | Apr 7, 2026 | Jun 12, 2026 | Sep 23, 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 Max$24.00
Which should you choose?
Which is better: GLM-5.1, Kimi K2.7 Code or Qwen3 Max?
Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and Qwen3 Max (50). 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, Kimi K2.7 Code or Qwen3 Max?
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); Qwen3 Max costs $1.20 input / $6.00 output per million tokens (official Alibaba 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 $2.40 for Qwen3 Max (1.4× 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 Max 142.4 (#91 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 Max 72.6%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, GLM-5.1 36.8%, Qwen3 Max 19.0%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Qwen3 Max 73.3%; SimpleQA Verified — Qwen3 Max 48.8%, 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 Kimi K2.7 Code and Qwen3 Max 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 Max 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 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5.1 accepts text; Kimi K2.7 Code accepts text, images and video; Qwen3 Max accepts text. Kimi K2.7 Code handles the widest range of inputs.
Are any of these open source?
GLM-5.1 and Kimi K2.7 Code publishes its weights and can be self-hosted; Qwen3 Max is proprietary.
Which is newer?
Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; Qwen3 Max came out Sep 23, 2025. Knowledge cutoff: Kimi K2.7 Code Jan 2025, Qwen3 Max 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.
