GLM-5 vs Qwen3.6 27B vs Qwen3.7 Max
Qwen3.6 27B comes out ahead, 65 to 58 and 55 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
- Our pick
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
- Alibaba (Qwen)
Qwen3.7 Max
58/100- ECI153.7
- Price$2.50 / $7.50
- Context1M
Qwen3.6 27B is our pick
Qwen3.6 27B is the better all-round choice, scoring 65/100 against Qwen3.7 Max (58) and GLM-5 (55). It leads on price and inputs & features. Qwen3.7 Max wins on capability and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.7 MaxCapabilities Index (ECI): Qwen3.7 Max 153.7 · Qwen3.6 27B 146.5 · GLM-5 145.8
- Lowest priceQwen3.6 27BQwen3.6 27B $1.35 · GLM-5 $1.55 · Qwen3.7 Max $3.75 per 1M tokens (3:1 blend)
- Longest contextQwen3.7 MaxQwen3.7 Max 1,000,000 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · Qwen3.6 27B: Text, Images, Audio, Video · Qwen3.7 Max: Text
- Self-hostingGLM-5 and Qwen3.6 27BPublishes downloadable weights
| Measure | Weight | GLM-5 | Qwen3.6 27B | Qwen3.7 Max |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 74 | 83 |
| Price | 25% | 41 | 44 | 23 |
| Inputs & features | 15% | 35 | 90 | 35 |
| Context window | 10% | 32 | 37 | 60 |
| Overall | 100% | 55/100 | 65/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | Qwen3.6 27BAlibaba (Qwen) | Qwen3.7 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 146.5 | 153.7 (best) |
| ECI rank | #74 of 148 | #68 of 148 | #37 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% | 85.9% | 90.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 35.1% | 64.6% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 91.1% | 95.6% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | 77.3% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 55.8% |
| Price per million tokens | |||
| Input | $1.00 | $0.60 (best) | $2.50 |
| Output | $3.20 (best) | $3.60 | $7.50 |
| Cached input | $0.20 (best) | — | $0.50 |
| Blended (3:1) | $1.55 | $1.35 (best) | $3.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 131,072 tokens (best) | 65,536 tokens | 65,536 tokens |
| 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 | Proprietary |
| API model ID | glm-5 | qwen3.6-27b | qwen3.7-max |
| API providers | 27 (best) | 27 (best) | 26 |
| Released | Feb 12, 2026 | Apr 22, 2026 | May 21, 2026 |
| Knowledge cutoff | — | — | — |
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
- Qwen3.6 27B$13.20
- Qwen3.7 Max$40.00
Which should you choose?
Which is better: GLM-5, Qwen3.6 27B or Qwen3.7 Max?
Qwen3.6 27B is the better all-round choice, scoring 65/100 against Qwen3.7 Max (58) and GLM-5 (55). It leads on price and inputs & features. Qwen3.7 Max wins on capability and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Qwen3.6 27B or Qwen3.7 Max?
Qwen3.6 27B is cheaper at $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); Qwen3.7 Max costs $2.50 input / $7.50 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $1.35 per million tokens for Qwen3.6 27B versus $1.55 for GLM-5 (1.1× as much) and $3.75 for Qwen3.7 Max (2.8× as much).
Which scores higher on benchmarks?
Qwen3.7 Max scores higher on the Capabilities Index (ECI): Qwen3.7 Max 153.7 (#37 of 148), Qwen3.6 27B 146.5 (#68 of 148) and GLM-5 145.8 (#74 of 148). Their confidence ranges do not overlap (151.9–156.0 vs 144.2–147.9), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.7 Max 90.9%, GLM-5 87.8%, Qwen3.6 27B 85.9%; OTIS Mock AIME 2024–2025 — Qwen3.7 Max 95.6%, 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, Qwen3.7 Max 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?
Qwen3.7 Max has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3.6 27B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Qwen3.6 27B up to 65,536, Qwen3.7 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Qwen3.6 27B accepts text, images, audio and video; Qwen3.7 Max accepts text. Qwen3.6 27B handles the widest range of inputs.
Are any of these open source?
GLM-5 and Qwen3.6 27B publishes its weights and can be self-hosted; Qwen3.7 Max is proprietary.
Which is newer?
Qwen3.7 Max is the newest, released May 21, 2026. Qwen3.6 27B came out Apr 22, 2026; GLM-5 came out Feb 12, 2026.
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.
