Qwen3 Coder Next vs Qwen3.5 397B-A17B vs GLM-4.6V
Too close to call on our weighted score (GLM-4.6V 59, Qwen3.5 397B-A17B 56, Qwen3 Coder Next 51). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
- Alibaba (Qwen)
Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
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, Qwen3 Coder Next 51/100), so choose by what matters most for your work: Qwen3 Coder Next on price. 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 priceQwen3 Coder Next and GLM-4.6VQwen3 Coder Next $0.45 · GLM-4.6V $0.45 · Qwen3.5 397B-A17B $1.35 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder Next and Qwen3.5 397B-A17BQwen3 Coder Next 262,144 · Qwen3.5 397B-A17B 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsQwen3.5 397B-A17BQwen3 Coder Next: Text · Qwen3.5 397B-A17B: Text, Images, Audio, Video · GLM-4.6V: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 Coder Next | Qwen3.5 397B-A17B | GLM-4.6V |
|---|---|---|---|---|
| Price | 50% | 66 | 44 | 66 |
| Inputs & features | 30% | 35 | 90 | 70 |
| Context window | 20% | 37 | 37 | 24 |
| Overall | 100% | 51/100 | 56/100 | 59/100 |
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 | Qwen3 Coder NextAlibaba (Qwen) | Qwen3.5 397B-A17BAlibaba (Qwen) | GLM-4.6VZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 146.7 | — |
| ECI rank | — | #67 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 86.4% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 31.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 88.9% | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.60 | $0.30 |
| Output | $1.20 | $3.60 | $0.90 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 (best) | $1.35 | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Alibaba API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 128,000 tokens |
| Max output | 65,536 tokens (best) | 65,536 tokens (best) | 32,768 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 | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | qwen3.5-397b-a17b | glm-4.6v |
| API providers | 11 | 23 (best) | 10 |
| Released | Feb 3, 2026 | Feb 15, 2026 | Dec 8, 2025 |
| Knowledge cutoff | Sep 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.
- Qwen3 Coder Next$4.40
- Qwen3.5 397B-A17B$13.20
- GLM-4.6V$4.80
Which should you choose?
Which is better: Qwen3 Coder Next, Qwen3.5 397B-A17B or GLM-4.6V?
It is close. Our weighted score puts them within 3 points (GLM-4.6V 59/100, Qwen3.5 397B-A17B 56/100, Qwen3 Coder Next 51/100), so choose by what matters most for your work: Qwen3 Coder Next on price. 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, Qwen3 Coder Next, Qwen3.5 397B-A17B or GLM-4.6V?
Qwen3 Coder Next is cheaper at $0.20 input / $1.20 output per million tokens (median across 11 API providers). GLM-4.6V costs $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). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Qwen3 Coder Next versus $0.45 for GLM-4.6V (1× as much) and $1.35 for Qwen3.5 397B-A17B (3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 Coder Next has not been scored yet, Qwen3.5 397B-A17B has an ECI of 146.7 and GLM-4.6V has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 Coder Next, Qwen3.5 397B-A17B and GLM-4.6V 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 Coder Next and Qwen3.5 397B-A17B have the largest context windows (262,144 and 262,144 tokens), against 128,000 for GLM-4.6V. Maximum output per response: Qwen3 Coder Next up to 65,536, Qwen3.5 397B-A17B up to 65,536, GLM-4.6V up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 Coder Next accepts text; Qwen3.5 397B-A17B accepts text, images, audio and video; GLM-4.6V accepts text, images 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. Qwen3 Coder Next came out Feb 3, 2026; GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: Qwen3 Coder Next Sep 2025, 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.
