Trinity Nano Preview vs Trinity Large Preview
Trinity Large Preview comes out ahead, 34 to 25 on our weighted score.
Arcee AITrinity Nano Preview
25/100- ECI—
- Price—
- Context131K
- Our pickArcee AI
Trinity Large Preview
34/100- ECI—
- Price—
- Context524K
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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 Nano Preview: Text · Trinity Large Preview: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Trinity Nano Preview | Trinity Large Preview |
|---|---|---|---|
| Inputs & features | 60% | 25 | 25 |
| Context window | 40% | 24 | 49 |
| Overall | 100% | 25/100 | 34/100 |
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 | Trinity Large 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 | 131,072 tokens | 524,288 tokens (best) |
| Max output | 131,072 tokens | 262,144 tokens (best) |
| 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 | Dec 1, 2025 | Jan 27, 2026 |
| 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 Nano Preview—
- Trinity Large Preview—
Which should you choose?
Which is better: Trinity Nano Preview or Trinity Large 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 Nano Preview or Trinity Large 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 Nano Preview has not been scored yet and Trinity Large Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Trinity Nano Preview and Trinity Large 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 Nano Preview up to 131,072, Trinity Large Preview up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Trinity Nano Preview accepts text; Trinity Large 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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