GLM-4.6V vs GLM-5 vs Qwen3.5 397B-A17B
Too close to call on our weighted score (GLM-4.6V 59, Qwen3.5 397B-A17B 56, GLM-5 37). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Z.ai (Zhipu)
GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Alibaba (Qwen)Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (GLM-4.6V 59/100, Qwen3.5 397B-A17B 56/100, GLM-5 37/100), so choose by what matters most for your work: GLM-4.6V on price and Qwen3.5 397B-A17B for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGLM-4.6VGLM-4.6V $0.45 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 397B-A17BQwen3.5 397B-A17B 262,144 · GLM-5 204,800 · GLM-4.6V 128,000 tokens
- Widest inputsQwen3.5 397B-A17BGLM-4.6V: Text, Images, Video · GLM-5: Text · Qwen3.5 397B-A17B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | GLM-5 | Qwen3.5 397B-A17B |
|---|---|---|---|---|
| Price | 50% | 66 | 41 | 44 |
| Inputs & features | 30% | 70 | 35 | 90 |
| Context window | 20% | 24 | 32 | 37 |
| Overall | 100% | 59/100 | 37/100 | 56/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.6VZ.ai (Zhipu) | GLM-5Z.ai (Zhipu) | Qwen3.5 397B-A17BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 145.8 | 146.7 (best) |
| ECI rank | — | #74 of 148 | #67 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 87.8% (best) | 86.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 31.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 80.0% | 88.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 72.1% | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $1.00 | $0.60 |
| Output | $0.90 (best) | $3.20 | $3.60 |
| Cached input | — | $0.20 | — |
| Blended (3:1) | $0.45 (best) | $1.55 | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Z.AI API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.6v | glm-5 | qwen3.5-397b-a17b |
| API providers | 10 | 27 (best) | 23 |
| Released | Dec 8, 2025 | Feb 12, 2026 | Feb 15, 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-4.6V$4.80
- GLM-5$16.40
- Qwen3.5 397B-A17B$13.20
Which should you choose?
Which is better: GLM-4.6V, GLM-5 or Qwen3.5 397B-A17B?
It is close. Our weighted score puts them within 3 points (GLM-4.6V 59/100, Qwen3.5 397B-A17B 56/100, GLM-5 37/100), so choose by what matters most for your work: GLM-4.6V on price and Qwen3.5 397B-A17B for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-4.6V, GLM-5 or Qwen3.5 397B-A17B?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI 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 $0.45 per million tokens for GLM-4.6V versus $1.35 for Qwen3.5 397B-A17B (3× as much) and $1.55 for GLM-5 (3.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, GLM-5 has an ECI of 145.8 and Qwen3.5 397B-A17B has an ECI of 146.7.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V and Qwen3.5 397B-A17B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.5 397B-A17B has the largest context window at 262,144 tokens, against 204,800 for GLM-5 and 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, GLM-5 up to 131,072, Qwen3.5 397B-A17B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; GLM-5 accepts text; Qwen3.5 397B-A17B accepts text, images, audio and video. Qwen3.5 397B-A17B 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?
Qwen3.5 397B-A17B is the newest, released Feb 15, 2026. GLM-5 came out Feb 12, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V 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.
