Qwen3.5 397B-A17B vs Qwen3.7 Plus vs GLM-5
Qwen3.7 Plus comes out ahead, 70 to 65 and 55 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)
Qwen3.5 397B-A17B
65/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
- Our pick
Alibaba (Qwen)
Qwen3.7 Plus
70/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
Z.ai (Zhipu)
GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Qwen3.7 Plus is our pick
Qwen3.7 Plus is the better all-round choice, scoring 70/100 against Qwen3.5 397B-A17B (65) and GLM-5 (55). It leads on price and context window. Qwen3.5 397B-A17B 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 · Qwen3.5 397B-A17B 146.7 · GLM-5 145.8
- Lowest priceQwen3.7 PlusQwen3.7 Plus $0.70 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.7 PlusQwen3.7 Plus 1,000,000 · Qwen3.5 397B-A17B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.5 397B-A17BQwen3.5 397B-A17B: Text, Images, Audio, Video · Qwen3.7 Plus: Text, Images, Video · GLM-5: Text
- Self-hostingQwen3.5 397B-A17B and GLM-5Publishes downloadable weights
| Measure | Weight | Qwen3.5 397B-A17B | Qwen3.7 Plus | GLM-5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 75 | 73 |
| Price | 25% | 44 | 57 | 41 |
| Inputs & features | 15% | 90 | 80 | 35 |
| Context window | 10% | 37 | 60 | 32 |
| Overall | 100% | 65/100 | 70/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.7 | 147.4 (best) | 145.8 |
| ECI rank | #67 of 148 | #61 of 148 (best) | #74 of 148 |
| GPQA DiamondGraduate-level science questions | 86.4% | 87.9% (best) | 87.8% |
| FrontierMath Tiers 1–3Research-level mathematics | 31.2% | 34.4% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 88.9% | 93.3% (best) | 80.0% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 72.1% |
| Price per million tokens | |||
| Input | $0.60 | $0.40 (best) | $1.00 |
| Output | $3.60 | $1.60 (best) | $3.20 |
| Cached input | — | $0.04 (best) | $0.20 |
| Blended (3:1) | $1.35 | $0.70 (best) | $1.55 |
| Long-context rate | Same rate | Over 256K: $1.20 / $4.80 | Same rate |
| Price source | Official Alibaba API | Official Alibaba API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens (best) | 204,800 tokens |
| Max output | 65,536 tokens | 64,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | qwen3.5-397b-a17b | qwen3.7-plus | glm-5 |
| API providers | 23 | 25 | 27 (best) |
| Released | Feb 15, 2026 | Jun 2, 2026 | Feb 12, 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.
Qwen3.5 397B-A17B$13.20
Qwen3.7 Plus$7.20
GLM-5$16.40
Which should you choose?
Which is better: Qwen3.5 397B-A17B, Qwen3.7 Plus or GLM-5?
Qwen3.7 Plus is the better all-round choice, scoring 70/100 against Qwen3.5 397B-A17B (65) and GLM-5 (55). It leads on price and context window. Qwen3.5 397B-A17B wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.5 397B-A17B, Qwen3.7 Plus or GLM-5?
Qwen3.7 Plus is cheaper at $0.40 input / $1.60 output per million tokens (official Alibaba API price). Qwen3.5 397B-A17B 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 Qwen3.5 397B-A17B (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), Qwen3.5 397B-A17B 146.7 (#67 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (145.7–148.9 vs 144.8–148.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.7 Plus 87.9%, GLM-5 87.8%, Qwen3.5 397B-A17B 86.4%; OTIS Mock AIME 2024–2025 — Qwen3.7 Plus 93.3%, Qwen3.5 397B-A17B 88.9%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 397B-A17B 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 Qwen3.5 397B-A17B and 204,800 for GLM-5. Maximum output per response: Qwen3.5 397B-A17B up to 65,536, Qwen3.7 Plus up to 64,000, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 397B-A17B accepts text, images, audio and video; Qwen3.7 Plus accepts text, images and video; GLM-5 accepts text. Qwen3.5 397B-A17B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 397B-A17B and GLM-5 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. Qwen3.5 397B-A17B came out Feb 15, 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.