GLM-5 vs Kimi K2.5 vs Qwen3.6 27B
Too close to call on our weighted score (Kimi K2.5 65, Qwen3.6 27B 65, GLM-5 55). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Moonshot AIKimi K2.5
65/100- ECI148.0
- Price$0.60 / $3.00
- Context262K
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (Kimi K2.5 65/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.5 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.5Capabilities Index (ECI): Kimi K2.5 148.0 · Qwen3.6 27B 146.5 · GLM-5 145.8
- Lowest priceKimi K2.5Kimi K2.5 $1.20 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextKimi K2.5 and Qwen3.6 27BKimi K2.5 262,144 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · Kimi K2.5: Text, Images, Video · Qwen3.6 27B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | Kimi K2.5 | Qwen3.6 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 76 | 74 |
| Price | 25% | 41 | 46 | 44 |
| Inputs & features | 15% | 35 | 80 | 90 |
| Context window | 10% | 32 | 37 | 37 |
| Overall | 100% | 55/100 | 65/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | Kimi K2.5Moonshot AI | Qwen3.6 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 148.0 (best) | 146.5 |
| ECI rank | #74 of 148 | #58 of 148 (best) | #68 of 148 |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | 87.6% | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 35.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 92.2% (best) | 91.1% |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | 73.8% (best) | — |
| SimpleQA VerifiedShort factual questions | — | 34.3% | — |
| Price per million tokens | |||
| Input | $1.00 | $0.60 (best) | $0.60 (best) |
| Output | $3.20 | $3.00 (best) | $3.60 |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $1.20 (best) | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 21 providers | Official Alibaba API |
| Limits | |||
| Context window | 204,800 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 | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | — | qwen3.6-27b |
| API providers | 27 (best) | 21 | 27 (best) |
| Released | Feb 12, 2026 | Jan 27, 2026 | Apr 22, 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$16.40
- Kimi K2.5$12.00
- Qwen3.6 27B$13.20
Which should you choose?
Which is better: GLM-5, Kimi K2.5 or Qwen3.6 27B?
It is close. Our weighted score puts them within a point (Kimi K2.5 65/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.5 for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Kimi K2.5 or Qwen3.6 27B?
Kimi K2.5 is cheaper at $0.60 input / $3.00 output per million tokens (median across 21 API providers). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price); GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.20 per million tokens for Kimi K2.5 versus $1.35 for Qwen3.6 27B (1.1× as much) and $1.55 for GLM-5 (1.3× as much).
Which scores higher on benchmarks?
Kimi K2.5 scores higher on the Capabilities Index (ECI): Kimi K2.5 148.0 (#58 of 148), Qwen3.6 27B 146.5 (#68 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (146.5–149.3 vs 144.2–147.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5 87.8%, Kimi K2.5 87.6%, Qwen3.6 27B 85.9%; OTIS Mock AIME 2024–2025 — Kimi K2.5 92.2%, Qwen3.6 27B 91.1%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.6 27B yet, so there is no like-for-like coding score. On overall capability, Kimi K2.5 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 K2.5 and Qwen3.6 27B have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Kimi K2.5 up to 262,144, Qwen3.6 27B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Kimi K2.5 accepts text, images and video; Qwen3.6 27B accepts text, images, audio and video. 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-5 came out Feb 12, 2026; Kimi K2.5 came out Jan 27, 2026. Knowledge cutoff: Kimi K2.5 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.
