Trinity Large Thinking vs Perceptron Mk1
Trinity Large Thinking comes out ahead, 53 to 45 on our weighted score, and it is the cheaper option too.
- Our pick
Arcee AITrinity Large Thinking
53/100- ECI—
- Price$0.25 / $0.80
- Context524K
Perceptron
Perceptron Mk1
45/100- ECI—
- Price$0.15 / $1.50
- Context33K
Add a model
Make it a three-way comparison.
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 53/100 against Perceptron Mk1 (45). It leads on price and context window. Perceptron Mk1 wins on inputs & features. 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 priceTrinity Large ThinkingTrinity Large Thinking $0.388 · Perceptron Mk1 $0.487 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Perceptron Mk1 32,768 tokens
- Widest inputsPerceptron Mk1Trinity Large Thinking: Text · Perceptron Mk1: Text, Images, Video
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Trinity Large Thinking | Perceptron Mk1 |
|---|---|---|---|
| Price | 50% | 66 | 62 |
| Inputs & features | 30% | 35 | 45 |
| Context window | 20% | 49 | 0 |
| Overall | 100% | 53/100 | 45/100 |
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 | Trinity Large ThinkingArcee AI | Perceptron Mk1Perceptron |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.25 | $0.15 (best) |
| Output | $0.80 (best) | $1.50 |
| Cached input | $0.06 | — |
| Blended (3:1) | $0.388 (best) | $0.487 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Arcee API | Median of 3 providers |
| Limits | ||
| Context window | 524,288 tokens (best) | 32,768 tokens |
| Max output | 262,144 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | Yes | Yes |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenOpenMDW-1.1 | Proprietary |
| API model ID | trinity-large-thinking | — |
| API providers | 3 | 3 |
| Released | Apr 1, 2026 | May 12, 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 Large Thinking$4.10
- Perceptron Mk1$4.50
Which should you choose?
Which is better: Trinity Large Thinking or Perceptron Mk1?
Trinity Large Thinking is the better all-round choice, scoring 53/100 against Perceptron Mk1 (45). It leads on price and context window. Perceptron Mk1 wins on inputs & features. 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, Trinity Large Thinking or Perceptron Mk1?
Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). Perceptron Mk1 costs $0.15 input / $1.50 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $0.487 for Perceptron Mk1 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Trinity Large Thinking has not been scored yet and Perceptron Mk1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Trinity Large Thinking and Perceptron Mk1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Perceptron Mk1 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 32,768 for Perceptron Mk1. Maximum output per response: Trinity Large Thinking up to 262,144, Perceptron Mk1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Trinity Large Thinking accepts text; Perceptron Mk1 accepts text, images and video. Perceptron Mk1 handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Perceptron Mk1 is proprietary.
Which is newer?
Perceptron Mk1 is the newest, released May 12, 2026. Trinity Large Thinking came out Apr 1, 2026.
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 Nov 2, 2026.
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