Qwen3.6 Max Preview vs Kimi K2.6 vs GLM-5.1
Kimi K2.6 comes out ahead, 65 to 57 and 54 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.6 Max Preview
54/100- ECI149.2
- Price$1.30 / $7.80
- Context262K
- Our pick
Moonshot AIKimi K2.6
65/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
Kimi K2.6 is our pick
Kimi K2.6 is the better all-round choice, scoring 65/100 against GLM-5.1 (57) and Qwen3.6 Max Preview (54). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.6Capabilities Index (ECI): Kimi K2.6 151.1 · GLM-5.1 149.9 · Qwen3.6 Max Preview 149.2
- Lowest priceKimi K2.6Kimi K2.6 $1.71 · GLM-5.1 $2.15 · Qwen3.6 Max Preview $2.92 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 Max Preview and Kimi K2.6Qwen3.6 Max Preview 262,144 · Kimi K2.6 262,144 · GLM-5.1 200,000 tokens
- Widest inputsKimi K2.6Qwen3.6 Max Preview: Text · Kimi K2.6: Text, Images, Video · GLM-5.1: Text
- Self-hostingKimi K2.6 and GLM-5.1Publishes downloadable weights
| Measure | Weight | Qwen3.6 Max Preview | Kimi K2.6 | GLM-5.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 77 | 79 | 78 |
| Price | 25% | 28 | 39 | 34 |
| Inputs & features | 15% | 35 | 80 | 45 |
| Context window | 10% | 37 | 37 | 32 |
| Overall | 100% | 54/100 | 65/100 | 57/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.6 Max PreviewAlibaba (Qwen) | Kimi K2.6Moonshot AI | GLM-5.1Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.2 | 151.1 (best) | 149.9 |
| ECI rank | #54 of 148 | #45 of 148 (best) | #51 of 148 |
| GPQA DiamondGraduate-level science questions | 87.4% | 90.8% (best) | 89.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 57.2% (best) | 36.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 96.1% (best) | 93.3% |
| SWE-bench VerifiedFixing real GitHub issues | 76.7% (best) | 76.7% (best) | 74.2% |
| SimpleQA VerifiedShort factual questions | 52.0% (best) | 34.9% | 34.0% |
| Price per million tokens | |||
| Input | $1.30 | $0.95 (best) | $1.40 |
| Output | $7.80 | $4.00 (best) | $4.40 |
| Cached input | $0.13 (best) | $0.16 | $0.26 |
| Blended (3:1) | $2.92 | $1.71 (best) | $2.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Moonshot AI API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 200,000 tokens |
| Max output | 65,536 tokens | 262,144 tokens (best) | 131,072 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 | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | qwen3.6-max-preview | kimi-k2.6 | glm-5.1 |
| API providers | 10 | 46 (best) | 40 |
| Released | Apr 20, 2026 | Apr 21, 2026 | Apr 7, 2026 |
| Knowledge cutoff | Apr 2025 | 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.
- Qwen3.6 Max Preview$28.60
- Kimi K2.6$17.50
- GLM-5.1$22.80
Which should you choose?
Which is better: Qwen3.6 Max Preview, Kimi K2.6 or GLM-5.1?
Kimi K2.6 is the better all-round choice, scoring 65/100 against GLM-5.1 (57) and Qwen3.6 Max Preview (54). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.6 Max Preview, Kimi K2.6 or GLM-5.1?
Kimi K2.6 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); Qwen3.6 Max Preview costs $1.30 input / $7.80 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 K2.6 versus $2.15 for GLM-5.1 (1.3× as much) and $2.92 for Qwen3.6 Max Preview (1.7× as much).
Which scores higher on benchmarks?
Kimi K2.6 scores higher on the Capabilities Index (ECI): Kimi K2.6 151.1 (#45 of 148), GLM-5.1 149.9 (#51 of 148) and Qwen3.6 Max Preview 149.2 (#54 of 148). The confidence ranges of the top two overlap (149.1–152.8 vs 148.0–151.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2.6 90.8%, GLM-5.1 89.9%, Qwen3.6 Max Preview 87.4%; OTIS Mock AIME 2024–2025 — Kimi K2.6 96.1%, GLM-5.1 93.3%, Qwen3.6 Max Preview 91.1%; SWE-bench Verified — Qwen3.6 Max Preview 76.7%, Kimi K2.6 76.7%, GLM-5.1 74.2%; SimpleQA Verified — Qwen3.6 Max Preview 52.0%, Kimi K2.6 34.9%, GLM-5.1 34.0%.
Which is better for coding?
Qwen3.6 Max Preview resolves more real GitHub issues on SWE-bench Verified: Qwen3.6 Max Preview 76.7%, Kimi K2.6 76.7% and GLM-5.1 74.2%. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.6 Max Preview and Kimi K2.6 have the largest context windows (262,144 and 262,144 tokens), against 200,000 for GLM-5.1. Maximum output per response: Qwen3.6 Max Preview up to 65,536, Kimi K2.6 up to 262,144, GLM-5.1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 Max Preview accepts text; Kimi K2.6 accepts text, images and video; GLM-5.1 accepts text. Kimi K2.6 handles the widest range of inputs.
Are any of these open source?
Kimi K2.6 and GLM-5.1 publishes its weights and can be self-hosted; Qwen3.6 Max Preview is proprietary.
Which is newer?
Kimi K2.6 is the newest, released Apr 21, 2026. Qwen3.6 Max Preview came out Apr 20, 2026; GLM-5.1 came out Apr 7, 2026. Knowledge cutoff: Qwen3.6 Max Preview Apr 2025, Kimi K2.6 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.
