GLM-4.6V-Flash vs Ministral 3 14B vs Nemotron Nano 12B v2 VL
Too close to call on our weighted score (GLM-4.6V-Flash 65, Ministral 3 14B 63, Nemotron Nano 12B v2 VL 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 AIMinistral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
NVIDIANemotron Nano 12B v2 VL
63/100- ECI—
- Price$0.20 / $0.60
- Context128K
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-4.6V-Flash 65/100, Ministral 3 14B 63/100, Nemotron Nano 12B v2 VL 63/100), so choose by what matters most for your work: GLM-4.6V-Flash 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Ministral 3 14B $0.282 · Nemotron Nano 12B v2 VL $0.30 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 14BMinistral 3 14B 262,144 · GLM-4.6V-Flash 128,000 · Nemotron Nano 12B v2 VL 128,000 tokens
- Widest inputsGLM-4.6V-Flash and Nemotron Nano 12B v2 VLGLM-4.6V-Flash: Text, Images, Video · Ministral 3 14B: Text, Images · Nemotron Nano 12B v2 VL: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V-Flash | Ministral 3 14B | Nemotron Nano 12B v2 VL |
|---|---|---|---|---|
| Price | 50% | 78 | 76 | 75 |
| Inputs & features | 30% | 70 | 60 | 70 |
| Context window | 20% | 24 | 37 | 24 |
| Overall | 100% | 65/100 | 63/100 | 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) | Ministral 3 14BMistral AI | Nemotron Nano 12B v2 VLNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.161 (best) | $0.268 | $0.20 |
| Output | $0.559 | $0.325 (best) | $0.60 |
| Cached input | — | — | — |
| Blended (3:1) | $0.261 (best) | $0.282 | $0.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 2 providers | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 32,768 tokens | 262,144 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Open |
| API model ID | glm-4.6v-flash | — | nvidia/nemotron-nano-12b-v2-vl |
| API providers | 6 (best) | 2 | 4 |
| Released | Dec 8, 2025 | Dec 2, 2025 | Oct 28, 2025 |
| Knowledge cutoff | — | — | — |
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
- Ministral 3 14B$3.33
- Nemotron Nano 12B v2 VL$3.20
Which should you choose?
Which is better: GLM-4.6V-Flash, Ministral 3 14B or Nemotron Nano 12B v2 VL?
It is close. Our weighted score puts them within 1 points (GLM-4.6V-Flash 65/100, Ministral 3 14B 63/100, Nemotron Nano 12B v2 VL 63/100), so choose by what matters most for your work: GLM-4.6V-Flash 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, Ministral 3 14B or Nemotron Nano 12B v2 VL?
GLM-4.6V-Flash is cheaper at $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); Nemotron Nano 12B v2 VL costs $0.20 input / $0.60 output per million tokens (median across 3 API providers; free on Nvidia). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $0.282 for Ministral 3 14B (1.1× as much) and $0.30 for Nemotron Nano 12B v2 VL (1.2× 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, Ministral 3 14B has not been scored yet and Nemotron Nano 12B v2 VL has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V-Flash, Ministral 3 14B and Nemotron Nano 12B v2 VL 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 Nemotron Nano 12B v2 VL. Maximum output per response: GLM-4.6V-Flash up to 32,768, Ministral 3 14B up to 262,144, Nemotron Nano 12B v2 VL up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V-Flash accepts text, images and video; Ministral 3 14B accepts text and images; Nemotron Nano 12B v2 VL accepts text, images and video. 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; Nemotron Nano 12B v2 VL came out Oct 28, 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.
