Qwen3 Coder Next vs GLM-4.6V
GLM-4.6V comes out ahead, 59 to 51 on our weighted score.
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
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Make it a three-way comparison.
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51). It leads on inputs & features. Qwen3 Coder Next wins on context window. 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 priceSame priceQwen3 Coder Next $0.45 · GLM-4.6V $0.45 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VQwen3 Coder Next: Text · 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 | GLM-4.6V |
|---|---|---|---|
| Price | 50% | 66 | 66 |
| Inputs & features | 30% | 35 | 70 |
| Context window | 20% | 37 | 24 |
| Overall | 100% | 51/100 | 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) | GLM-4.6VZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.20 (best) | $0.30 |
| Output | $1.20 | $0.90 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.45 | $0.45 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Z.AI API |
| Limits | ||
| Context window | 262,144 tokens (best) | 128,000 tokens |
| Max output | 65,536 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | glm-4.6v |
| API providers | 11 (best) | 10 |
| Released | Feb 3, 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
- GLM-4.6V$4.80
Which should you choose?
Which is better: Qwen3 Coder Next or GLM-4.6V?
GLM-4.6V is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51). It leads on inputs & features. Qwen3 Coder Next wins on context window. 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 or GLM-4.6V?
Qwen3 Coder Next and GLM-4.6V cost the same: $0.20 input / $1.20 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Qwen3 Coder Next has not been scored yet 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 and GLM-4.6V yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 Coder Next has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V. Maximum output per response: Qwen3 Coder Next 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; GLM-4.6V accepts text, images and video. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Qwen3 Coder Next is the newest, released 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.
