Trinity Nano Preview vs GLM-4.6V vs Ministral 14B
GLM-4.6V comes out ahead, 52 to 45 and 25 on our weighted score, though Ministral 14B is 2.3× cheaper per token.
Arcee AITrinity Nano Preview
25/100- ECI—
- Price—
- Context131K
- Our pick
Z.ai (Zhipu)GLM-4.6V
52/100- ECI—
- Price$0.30 / $0.90
- Context128K
Mistral AIMinistral 14B
45/100- ECI—
- Price$0.20 / $0.20
- Context262K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 52/100 against Ministral 14B (45) and Trinity Nano Preview (25). It leads on inputs & features. Ministral 14B wins on context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Trinity Nano Preview unpriced
- Longest contextMinistral 14BMinistral 14B 262,144 · Trinity Nano Preview 131,072 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VTrinity Nano Preview: Text · GLM-4.6V: Text, Images, Video · Ministral 14B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Trinity Nano Preview | GLM-4.6V | Ministral 14B |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 70 | 50 |
| Context window | 40% | 24 | 24 | 37 |
| Overall | 100% | 25/100 | 52/100 | 45/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Trinity Nano PreviewArcee AI | GLM-4.6VZ.ai (Zhipu) | Ministral 14BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.30 | $0.20 (best) |
| Output | — | $0.90 | $0.20 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.45 | $0.20 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Z.AI API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenOpenMDW-1.1 | Open | OpenApache-2.0 |
| API model ID | — | glm-4.6v | — |
| API providers | — | 10 (best) | 1 |
| Released | Dec 1, 2025 | Dec 8, 2025 | Dec 2, 2025 |
| Knowledge cutoff | — | 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.
- Trinity Nano Preview—
- GLM-4.6V$4.80
- Ministral 14B$2.40
Which should you choose?
Which is better: Trinity Nano Preview, GLM-4.6V or Ministral 14B?
GLM-4.6V is the better all-round choice, scoring 52/100 against Ministral 14B (45) and Trinity Nano Preview (25). It leads on inputs & features. Ministral 14B wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Trinity Nano Preview, GLM-4.6V or Ministral 14B?
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). 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). Trinity Nano Preview has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Trinity Nano Preview has not been scored yet, GLM-4.6V has not been scored yet and Ministral 14B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Trinity Nano Preview, GLM-4.6V and Ministral 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 14B has the largest context window at 262,144 tokens, against 131,072 for Trinity Nano Preview and 128,000 for GLM-4.6V. Maximum output per response: Trinity Nano Preview up to 131,072, GLM-4.6V up to 32,768, Ministral 14B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Trinity Nano Preview accepts text; GLM-4.6V accepts text, images and video; Ministral 14B accepts text and images. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (OpenMDW-1.1 and Apache-2.0), so you can self-host them.
Which is newer?
GLM-4.6V is the newest, released Dec 8, 2025. Ministral 14B came out Dec 2, 2025; Trinity Nano Preview came out Dec 1, 2025. Knowledge cutoff: 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.
