GLM-4.7 vs Qwen3.6 27B vs Kimi K2 Thinking
Qwen3.6 27B comes out ahead, 65 to 58 and 56 on our weighted score, though GLM-4.7 is 26% cheaper per token.
Z.ai (Zhipu)GLM-4.7
56/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
- Our pick
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
Moonshot AIKimi K2 Thinking
58/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Qwen3.6 27B is our pick
Qwen3.6 27B is the better all-round choice, scoring 65/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.6 27BCapabilities Index (ECI): Qwen3.6 27B 146.5 · Kimi K2 Thinking 146.0 · GLM-4.7 143.5
- Lowest priceGLM-4.7GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 · Qwen3.6 27B $1.35 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 27B and Kimi K2 ThinkingQwen3.6 27B 262,144 · Kimi K2 Thinking 262,144 · GLM-4.7 204,800 tokens
- Widest inputsQwen3.6 27BGLM-4.7: Text · Qwen3.6 27B: Text, Images, Audio, Video · Kimi K2 Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Qwen3.6 27B | Kimi K2 Thinking |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 70 | 74 | 73 |
| Price | 25% | 50 | 44 | 48 |
| Inputs & features | 15% | 35 | 90 | 35 |
| Context window | 10% | 32 | 37 | 37 |
| Overall | 100% | 56/100 | 65/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Qwen3.6 27BAlibaba (Qwen) | Kimi K2 ThinkingMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.5 | 146.5 (best) | 146.0 |
| ECI rank | #84 of 148 | #68 of 148 (best) | #72 of 148 |
| GPQA DiamondGraduate-level science questions | 83.3% | 85.9% (best) | 84.2% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 35.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | 91.1% (best) | 83.1% |
| SimpleQA VerifiedShort factual questions | 32.2% | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.60 | $0.60 |
| Output | $2.20 (best) | $3.60 | $2.50 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 (best) | $1.35 | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 10 providers |
| Limits | |||
| Context window | 204,800 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 | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7 | qwen3.6-27b | — |
| API providers | 20 | 27 (best) | 10 |
| Released | Dec 22, 2025 | Apr 22, 2026 | Nov 6, 2025 |
| Knowledge cutoff | Apr 2025 | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7$10.40
- Qwen3.6 27B$13.20
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GLM-4.7, Qwen3.6 27B or Kimi K2 Thinking?
Qwen3.6 27B is the better all-round choice, scoring 65/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-4.7, Qwen3.6 27B or Kimi K2 Thinking?
GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers); Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.7 versus $1.07 for Kimi K2 Thinking (1.1× as much) and $1.35 for Qwen3.6 27B (1.4× as much).
Which scores higher on benchmarks?
Qwen3.6 27B scores higher on the Capabilities Index (ECI): Qwen3.6 27B 146.5 (#68 of 148), Kimi K2 Thinking 146.0 (#72 of 148) and GLM-4.7 143.5 (#84 of 148). The confidence ranges of the top two overlap (144.2–147.9 vs 143.4–147.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.6 27B 85.9%, Kimi K2 Thinking 84.2%, GLM-4.7 83.3%; OTIS Mock AIME 2024–2025 — Qwen3.6 27B 91.1%, GLM-4.7 83.3%, Kimi K2 Thinking 83.1%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Qwen3.6 27B and Kimi K2 Thinking yet, so there is no like-for-like coding score. On overall capability, Qwen3.6 27B 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 27B and Kimi K2 Thinking have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Qwen3.6 27B up to 65,536, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen3.6 27B accepts text, images, audio and video; Kimi K2 Thinking accepts text. Qwen3.6 27B 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?
Qwen3.6 27B is the newest, released Apr 22, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Kimi K2 Thinking Aug 2024.
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.
