GLM-5 vs Kimi K2.5 vs Qwen3.6 Plus
Too close to call on our weighted score (Qwen3.6 Plus 66, Kimi K2.5 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 Plus
66/100- ECI147.6
- Price$0.50 / $3.00
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (Qwen3.6 Plus 66/100, Kimi K2.5 65/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.5 for raw capability, Qwen3.6 Plus on price and Qwen3.6 Plus for long inputs. 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 Plus 147.6 · GLM-5 145.8
- Lowest priceQwen3.6 PlusQwen3.6 Plus $1.13 · Kimi K2.5 $1.20 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 PlusQwen3.6 Plus 1,000,000 · Kimi K2.5 262,144 · GLM-5 204,800 tokens
- Widest inputsKimi K2.5 and Qwen3.6 PlusGLM-5: Text · Kimi K2.5: Text, Images, Video · Qwen3.6 Plus: Text, Images, Video
- Self-hostingGLM-5 and Kimi K2.5Publishes downloadable weights
| Measure | Weight | GLM-5 | Kimi K2.5 | Qwen3.6 Plus |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 76 | 75 |
| Price | 25% | 41 | 46 | 47 |
| Inputs & features | 15% | 35 | 80 | 70 |
| Context window | 10% | 32 | 37 | 60 |
| Overall | 100% | 55/100 | 65/100 | 66/100 |
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 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 148.0 (best) | 147.6 |
| ECI rank | #74 of 148 | #58 of 148 (best) | #59 of 148 |
| GPQA DiamondGraduate-level science questions | 87.8% | 87.6% | 88.4% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 38.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 92.2% | 93.3% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | 73.8% (best) | 57.9% |
| SimpleQA VerifiedShort factual questions | — | 34.3% | 44.1% (best) |
| Price per million tokens | |||
| Input | $1.00 | $0.60 | $0.50 (best) |
| Output | $3.20 | $3.00 (best) | $3.00 (best) |
| Cached input | $0.20 | — | $0.05 (best) |
| Blended (3:1) | $1.55 | $1.20 | $1.13 (best) |
| Long-context rate | Same rate | Same rate | Over 256K: $2.00 / $6.00 |
| Price source | Official Z.AI API | Median of 21 providers | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens | 1,000,000 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 | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5 | — | qwen3.6-plus |
| API providers | 27 (best) | 21 | 18 |
| Released | Feb 12, 2026 | Jan 27, 2026 | Apr 2, 2026 |
| 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$16.40
- Kimi K2.5$12.00
- Qwen3.6 Plus$11.00
Which should you choose?
Which is better: GLM-5, Kimi K2.5 or Qwen3.6 Plus?
It is close. Our weighted score puts them within a point (Qwen3.6 Plus 66/100, Kimi K2.5 65/100, GLM-5 55/100), so choose by what matters most for your work: Kimi K2.5 for raw capability, Qwen3.6 Plus on price and Qwen3.6 Plus for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Kimi K2.5 or Qwen3.6 Plus?
Qwen3.6 Plus is cheaper at $0.50 input / $3.00 output per million tokens (official Alibaba API price). Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 21 API providers); 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 $1.13 per million tokens for Qwen3.6 Plus versus $1.20 for Kimi K2.5 (1.1× as much) and $1.55 for GLM-5 (1.4× 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 Plus 147.6 (#59 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (146.5–149.3 vs 145.4–149.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.6 Plus 88.4%, GLM-5 87.8%, Kimi K2.5 87.6%; OTIS Mock AIME 2024–2025 — Qwen3.6 Plus 93.3%, Kimi K2.5 92.2%, GLM-5 80.0%; SWE-bench Verified — Kimi K2.5 73.8%, GLM-5 72.1%, Qwen3.6 Plus 57.9%.
Which is better for coding?
Kimi K2.5 resolves more real GitHub issues on SWE-bench Verified: Kimi K2.5 73.8%, GLM-5 72.1% and Qwen3.6 Plus 57.9%. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.6 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.5 and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Kimi K2.5 up to 262,144, Qwen3.6 Plus 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 Plus accepts text, images and video. Kimi K2.5 handles the widest range of inputs.
Are any of these open source?
GLM-5 and Kimi K2.5 publishes its weights and can be self-hosted; Qwen3.6 Plus is proprietary.
Which is newer?
Qwen3.6 Plus is the newest, released Apr 2, 2026. GLM-5 came out Feb 12, 2026; Kimi K2.5 came out Jan 27, 2026. Knowledge cutoff: Kimi K2.5 Jan 2025, Qwen3.6 Plus 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.
