GLM-5 vs Qwen3.6 27B vs Qwen3.7 Plus
Qwen3.7 Plus comes out ahead, 70 to 65 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
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
- Our pickAlibaba (Qwen)
Qwen3.7 Plus
70/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
Qwen3.7 Plus is our pick
Qwen3.7 Plus is the better all-round choice, scoring 70/100 against Qwen3.6 27B (65) and GLM-5 (55). It leads on price and context window. Qwen3.6 27B wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.7 PlusCapabilities Index (ECI): Qwen3.7 Plus 147.4 · Qwen3.6 27B 146.5 · GLM-5 145.8
- Lowest priceQwen3.7 PlusQwen3.7 Plus $0.70 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.7 PlusQwen3.7 Plus 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 Plus: Text, Images, Video
- Self-hostingGLM-5 and Qwen3.6 27BPublishes downloadable weights
| Measure | Weight | GLM-5 | Qwen3.6 27B | Qwen3.7 Plus |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 74 | 75 |
| Price | 25% | 41 | 44 | 57 |
| Inputs & features | 15% | 35 | 90 | 80 |
| Context window | 10% | 32 | 37 | 60 |
| Overall | 100% | 55/100 | 65/100 | 70/100 |
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 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 146.5 | 147.4 (best) |
| ECI rank | #74 of 148 | #68 of 148 | #61 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% | 85.9% | 87.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 35.1% (best) | 34.4% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 91.1% | 93.3% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.60 | $0.40 (best) |
| Output | $3.20 | $3.60 | $1.60 (best) |
| Cached input | $0.20 | — | $0.04 (best) |
| Blended (3:1) | $1.55 | $1.35 | $0.70 (best) |
| Long-context rate | Same rate | Same rate | Over 256K: $1.20 / $4.80 |
| 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 | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-5 | qwen3.6-27b | qwen3.7-plus |
| API providers | 27 (best) | 27 (best) | 25 |
| Released | Feb 12, 2026 | Apr 22, 2026 | Jun 2, 2026 |
| Knowledge cutoff | — | — | 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
- Qwen3.6 27B$13.20
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: GLM-5, Qwen3.6 27B or Qwen3.7 Plus?
Qwen3.7 Plus is the better all-round choice, scoring 70/100 against Qwen3.6 27B (65) and GLM-5 (55). It leads on price and context window. Qwen3.6 27B wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Qwen3.6 27B or Qwen3.7 Plus?
Qwen3.7 Plus is cheaper at $0.40 input / $1.60 output per million tokens (official Alibaba API price). 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 $0.70 per million tokens for Qwen3.7 Plus versus $1.35 for Qwen3.6 27B (1.9× as much) and $1.55 for GLM-5 (2.2× as much).
Which scores higher on benchmarks?
Qwen3.7 Plus scores higher on the Capabilities Index (ECI): Qwen3.7 Plus 147.4 (#61 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 (145.7–148.9 vs 144.2–147.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.7 Plus 87.9%, GLM-5 87.8%, Qwen3.6 27B 85.9%; OTIS Mock AIME 2024–2025 — Qwen3.7 Plus 93.3%, 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 and Qwen3.7 Plus yet, so there is no like-for-like coding score. On overall capability, Qwen3.7 Plus 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 Plus 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 Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Qwen3.6 27B accepts text, images, audio and video; Qwen3.7 Plus accepts text, images and video. 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 Plus is proprietary.
Which is newer?
Qwen3.7 Plus is the newest, released Jun 2, 2026. Qwen3.6 27B came out Apr 22, 2026; GLM-5 came out Feb 12, 2026. Knowledge cutoff: Qwen3.7 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.
