GLM-4.6V vs Ministral 14B vs Qwen3 Coder Next
Ministral 14B comes out ahead, 64 to 59 and 51 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Our pick
Mistral AIMinistral 14B
64/100- ECI—
- Price$0.20 / $0.20
- Context262K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Ministral 14B is our pick
Ministral 14B is the better all-round choice, scoring 64/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads on price. GLM-4.6V wins on inputs & features. 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 priceMinistral 14BMinistral 14B $0.20 · GLM-4.6V $0.45 · Qwen3 Coder Next $0.45 per 1M tokens (3:1 blend)
- Longest contextMinistral 14B and Qwen3 Coder NextMinistral 14B 262,144 · Qwen3 Coder Next 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Ministral 14B: Text, Images · Qwen3 Coder Next: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | Ministral 14B | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 66 | 83 | 66 |
| Inputs & features | 30% | 70 | 50 | 35 |
| Context window | 20% | 24 | 37 | 37 |
| Overall | 100% | 59/100 | 64/100 | 51/100 |
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) | Ministral 14BMistral AI | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.20 (best) | $0.20 (best) |
| Output | $0.90 | $0.20 (best) | $1.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | $0.20 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 32,768 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Open |
| API model ID | glm-4.6v | — | — |
| API providers | 10 | 1 | 11 (best) |
| Released | Dec 8, 2025 | Dec 2, 2025 | Feb 3, 2026 |
| Knowledge cutoff | Apr 2025 | — | Sep 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
- Ministral 14B$2.40
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: GLM-4.6V, Ministral 14B or Qwen3 Coder Next?
Ministral 14B is the better all-round choice, scoring 64/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads on price. GLM-4.6V wins on inputs & features. 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, Ministral 14B or Qwen3 Coder Next?
Ministral 14B is cheaper at $0.20 input / $0.20 output per million tokens (median across 1 API provider). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI API price); Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for Ministral 14B versus $0.45 for GLM-4.6V (2.3× as much) and $0.45 for Qwen3 Coder Next (2.3× 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, Ministral 14B has not been scored yet and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, Ministral 14B and Qwen3 Coder Next 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?
Ministral 14B and Qwen3 Coder Next have the largest context windows (262,144 and 262,144 tokens), against 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, Ministral 14B up to 262,144, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Ministral 14B accepts text and images; Qwen3 Coder Next accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), 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; Ministral 14B came out Dec 2, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, Qwen3 Coder Next Sep 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.
