Trinity Large Preview vs Trinity Nano Preview
Trinity Large Preview comes out ahead, 34 to 25 on our weighted score.
- Our pick
Arcee AITrinity Large Preview
34/100- ECI—
- Price—
- Context524K
- Arcee AI
Trinity Nano Preview
25/100- ECI—
- Price—
- Context131K
Add a model
Make it a three-way comparison.
Trinity Large Preview is our pick
Trinity Large Preview is the better all-round choice, scoring 34/100 against Trinity Nano Preview (25). It leads 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
- Longest contextTrinity Large PreviewTrinity Large Preview 524,288 · Trinity Nano Preview 131,072 tokens
- Widest inputsSame inputsTrinity Large Preview: Text · Trinity Nano Preview: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Trinity Large Preview | Trinity Nano Preview |
|---|---|---|---|
| Inputs & features | 60% | 25 | 25 |
| Context window | 40% | 49 | 24 |
| Overall | 100% | 34/100 | 25/100 |
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 Large PreviewArcee AI | Trinity Nano PreviewArcee AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 524,288 tokens (best) | 131,072 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenOpenMDW-1.1 | OpenOpenMDW-1.1 |
| API model ID | — | — |
| API providers | 1 | — |
| Released | Jan 27, 2026 | Dec 1, 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.
- Trinity Large Preview—
- Trinity Nano Preview—
Which should you choose?
Which is better: Trinity Large Preview or Trinity Nano Preview?
Trinity Large Preview is the better all-round choice, scoring 34/100 against Trinity Nano Preview (25). It leads 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 Large Preview or Trinity Nano Preview?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Trinity Large Preview has not been scored yet and Trinity Nano Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Trinity Large Preview and Trinity Nano Preview yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Trinity Large Preview has the largest context window at 524,288 tokens, against 131,072 for Trinity Nano Preview. Maximum output per response: Trinity Large Preview up to 262,144, Trinity Nano Preview up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Trinity Large Preview accepts text; Trinity Nano Preview accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (OpenMDW-1.1), so you can self-host them.
Which is newer?
Trinity Large Preview is the newest, released Jan 27, 2026. Trinity Nano Preview came out Dec 1, 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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