Mercury Edit 2 vs Perceptron Mk1 vs Trinity Large Thinking
Trinity Large Thinking comes out ahead, 53 to 45 and 34 on our weighted score.
InceptionMercury Edit 2
34/100- ECI—
- Price$0.25 / $0.75
- Context32K
Perceptron
Perceptron Mk1
45/100- ECI—
- Price$0.15 / $1.50
- Context33K
- Our pick
Arcee AITrinity Large Thinking
53/100- ECI—
- Price$0.25 / $0.80
- Context524K
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 53/100 against Perceptron Mk1 (45) and Mercury Edit 2 (34). It leads on 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 priceMercury Edit 2Mercury Edit 2 $0.375 · Trinity 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 · Mercury Edit 2 32,000 tokens
- Widest inputsPerceptron Mk1Mercury Edit 2: Text · Perceptron Mk1: Text, Images, Video · Trinity Large Thinking: Text
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Mercury Edit 2 | Perceptron Mk1 | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 67 | 62 | 66 |
| Inputs & features | 30% | 0 | 45 | 35 |
| Context window | 20% | 0 | 0 | 49 |
| Overall | 100% | 34/100 | 45/100 | 53/100 |
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 | Mercury Edit 2Inception | Perceptron Mk1Perceptron | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.15 (best) | $0.25 |
| Output | $0.75 (best) | $1.50 | $0.80 |
| Cached input | $0.025 (best) | — | $0.06 |
| Blended (3:1) | $0.375 (best) | $0.487 | $0.388 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Median of 3 providers | Official Arcee API |
| Limits | |||
| Context window | 32,000 tokens | 32,768 tokens | 524,288 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenOpenMDW-1.1 |
| API model ID | mercury-edit-2 | — | trinity-large-thinking |
| API providers | 1 | 3 (best) | 3 (best) |
| Released | Mar 30, 2026 | May 12, 2026 | Apr 1, 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.
- Mercury Edit 2$4.00
- Perceptron Mk1$4.50
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: Mercury Edit 2, Perceptron Mk1 or Trinity Large Thinking?
Trinity Large Thinking is the better all-round choice, scoring 53/100 against Perceptron Mk1 (45) and Mercury Edit 2 (34). It leads on 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, Mercury Edit 2, Perceptron Mk1 or Trinity Large Thinking?
Mercury Edit 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). Trinity Large Thinking costs $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 $0.375 per million tokens for Mercury Edit 2 versus $0.388 for Trinity Large Thinking (1× as much) and $0.487 for Perceptron Mk1 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mercury Edit 2 has not been scored yet, Perceptron Mk1 has not been scored yet and Trinity Large Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury Edit 2, Perceptron Mk1 and Trinity Large Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Mercury Edit 2 and 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 and 32,000 for Mercury Edit 2. Maximum output per response: Mercury Edit 2 up to 8,192, Perceptron Mk1 up to 8,192, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Mercury Edit 2 accepts text; Perceptron Mk1 accepts text, images and video; Trinity Large Thinking accepts text. 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; Mercury Edit 2 and Perceptron Mk1 is proprietary.
Which is newer?
Perceptron Mk1 is the newest, released May 12, 2026. Trinity Large Thinking came out Apr 1, 2026; Mercury Edit 2 came out Mar 30, 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.
