Qwen3 Coder Next vs Mistral Small 3.2 vs GLM-4.6V
Mistral Small 3.2 comes out ahead, 64 to 59 and 51 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
- Our pick
Mistral AIMistral Small 3.2
64/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
Mistral Small 3.2 is our pick
Mistral Small 3.2 is the better all-round choice, scoring 64/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads on price. Qwen3 Coder Next wins on context window. 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 priceMistral Small 3.2Mistral Small 3.2 $0.15 · Qwen3 Coder Next $0.45 · GLM-4.6V $0.45 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · Mistral Small 3.2 128,000 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VQwen3 Coder Next: Text · Mistral Small 3.2: Text, Images · 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 | Mistral Small 3.2 | GLM-4.6V |
|---|---|---|---|---|
| Price | 50% | 66 | 89 | 66 |
| Inputs & features | 30% | 35 | 50 | 70 |
| Context window | 20% | 37 | 24 | 24 |
| Overall | 100% | 51/100 | 64/100 | 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) | Mistral Small 3.2Mistral AI | GLM-4.6VZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 131.7 | — |
| ECI rank | — | #123 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 49.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 30.3% | — |
| Price per million tokens | |||
| Input | $0.20 | $0.10 (best) | $0.30 |
| Output | $1.20 | $0.30 (best) | $0.90 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | $0.15 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Mistral API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | mistral-small-2506 | glm-4.6v |
| API providers | 11 (best) | 6 | 10 |
| Released | Feb 3, 2026 | Jun 20, 2025 | Dec 8, 2025 |
| Knowledge cutoff | Sep 2025 | Mar 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
- Mistral Small 3.2$1.60
- GLM-4.6V$4.80
Which should you choose?
Which is better: Qwen3 Coder Next, Mistral Small 3.2 or GLM-4.6V?
Mistral Small 3.2 is the better all-round choice, scoring 64/100 against GLM-4.6V (59) and Qwen3 Coder Next (51). It leads on price. Qwen3 Coder Next wins on context window. 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, Qwen3 Coder Next, Mistral Small 3.2 or GLM-4.6V?
Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). Qwen3 Coder Next costs $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). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $0.45 for Qwen3 Coder Next (3× as much) and $0.45 for GLM-4.6V (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, Mistral Small 3.2 has an ECI of 131.7 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, Mistral Small 3.2 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 has the largest context window at 262,144 tokens, against 128,000 for Mistral Small 3.2 and 128,000 for GLM-4.6V. Maximum output per response: Qwen3 Coder Next up to 65,536, Mistral Small 3.2 up to 16,384, GLM-4.6V up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 Coder Next accepts text; Mistral Small 3.2 accepts text and images; GLM-4.6V accepts text, images and video. GLM-4.6V 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 Coder Next is the newest, released Feb 3, 2026. GLM-4.6V came out Dec 8, 2025; Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: Qwen3 Coder Next Sep 2025, Mistral Small 3.2 Mar 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.
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