GLM-4.6V-Flash vs Mistral Small 3.2 vs Ministral 3 14B
Too close to call on our weighted score (GLM-4.6V-Flash 65, Mistral Small 3.2 64, Ministral 3 14B 63). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.6V-Flash
65/100- ECI—
- Price$0.161 / $0.559
- Context128K
Mistral AIMistral Small 3.2
64/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
- Mistral AI
Ministral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.6V-Flash 65/100, Mistral Small 3.2 64/100, Ministral 3 14B 63/100), so choose by what matters most for your work: Mistral Small 3.2 on price and Ministral 3 14B 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 priceMistral Small 3.2Mistral Small 3.2 $0.15 · GLM-4.6V-Flash $0.261 · Ministral 3 14B $0.282 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 14BMinistral 3 14B 262,144 · GLM-4.6V-Flash 128,000 · Mistral Small 3.2 128,000 tokens
- Widest inputsGLM-4.6V-FlashGLM-4.6V-Flash: Text, Images, Video · Mistral Small 3.2: Text, Images · Ministral 3 14B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V-Flash | Mistral Small 3.2 | Ministral 3 14B |
|---|---|---|---|---|
| Price | 50% | 78 | 89 | 76 |
| Inputs & features | 30% | 70 | 50 | 60 |
| Context window | 20% | 24 | 24 | 37 |
| Overall | 100% | 65/100 | 64/100 | 63/100 |
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-4.6V-FlashZ.ai (Zhipu) | Mistral Small 3.2Mistral AI | Ministral 3 14BMistral AI |
|---|---|---|---|
| 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 | $0.161 | $0.10 (best) | $0.268 |
| Output | $0.559 | $0.30 (best) | $0.325 |
| Cached input | — | — | — |
| Blended (3:1) | $0.261 | $0.15 (best) | $0.282 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Mistral API | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 16,384 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| 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 | Open | OpenApache 2.0 |
| API model ID | glm-4.6v-flash | mistral-small-2506 | — |
| API providers | 6 (best) | 6 (best) | 2 |
| Released | Dec 8, 2025 | Jun 20, 2025 | Dec 2, 2025 |
| Knowledge cutoff | — | Mar 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-Flash$2.73
- Mistral Small 3.2$1.60
- Ministral 3 14B$3.33
Which should you choose?
Which is better: GLM-4.6V-Flash, Mistral Small 3.2 or Ministral 3 14B?
It is close. Our weighted score puts them within a point (GLM-4.6V-Flash 65/100, Mistral Small 3.2 64/100, Ministral 3 14B 63/100), so choose by what matters most for your work: Mistral Small 3.2 on price and Ministral 3 14B 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-Flash, Mistral Small 3.2 or Ministral 3 14B?
Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). GLM-4.6V-Flash costs $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI); Ministral 3 14B costs $0.268 input / $0.325 output per million tokens (median across 2 API providers). 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 $0.261 for GLM-4.6V-Flash (1.7× as much) and $0.282 for Ministral 3 14B (1.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V-Flash has not been scored yet, Mistral Small 3.2 has an ECI of 131.7 and Ministral 3 14B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V-Flash, Mistral Small 3.2 and Ministral 3 14B 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 3 14B has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V-Flash and 128,000 for Mistral Small 3.2. Maximum output per response: GLM-4.6V-Flash up to 32,768, Mistral Small 3.2 up to 16,384, Ministral 3 14B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V-Flash accepts text, images and video; Mistral Small 3.2 accepts text and images; Ministral 3 14B accepts text and images. GLM-4.6V-Flash 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?
GLM-4.6V-Flash is the newest, released Dec 8, 2025. Ministral 3 14B came out Dec 2, 2025; Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: Mistral Small 3.2 Mar 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.
