Mercury Edit 2 vs Mercury 2.5 vs Trinity Large Thinking
Mercury 2.5 comes out ahead, 71 to 55 and 35 on our weighted score, and it is the cheaper option too.
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
- Our pickInception
Mercury 2.5
71/100- ECI—
- Price$0.04 / $0.15
- Context260K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Mercury 2.5 is our pick
Mercury 2.5 is the better all-round choice, scoring 71/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on price and inputs & features. Trinity Large Thinking wins on context window. 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 2.5Mercury 2.5 $0.068 · Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Mercury 2.5 260,000 · Mercury Edit 2 32,000 tokens
- Widest inputsSame inputsMercury Edit 2: Text · Mercury 2.5: Text · Trinity Large Thinking: Text
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Mercury Edit 2 | Mercury 2.5 | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 70 | 100 | 69 |
| Inputs & features | 30% | 0 | 45 | 35 |
| Context window | 20% | 0 | 36 | 49 |
| Overall | 100% | 35/100 | 71/100 | 55/100 |
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 | Mercury 2.5Inception | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.04 (best) | $0.25 |
| Output | $0.75 | $0.15 (best) | $0.80 |
| Cached input | $0.025 | $0.004 (best) | $0.06 |
| Blended (3:1) | $0.375 | $0.068 (best) | $0.388 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Inception API | Official Arcee API |
| Limits | |||
| Context window | 32,000 tokens | 260,000 tokens | 524,288 tokens (best) |
| Max output | 8,192 tokens | 65,536 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yeslow · medium · high | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenOpenMDW-1.1 |
| API model ID | mercury-edit-2 | mercury-2.5 | trinity-large-thinking |
| API providers | 1 | 1 | 6 (best) |
| Released | Mar 30, 2026 | Sep 8, 2026 | Apr 1, 2026 |
| Knowledge cutoff | — | Nov 1, 2025 | — |
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
- Mercury 2.5$0.70
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: Mercury Edit 2, Mercury 2.5 or Trinity Large Thinking?
Mercury 2.5 is the better all-round choice, scoring 71/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on price and inputs & features. Trinity Large Thinking wins on context window. 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, Mercury 2.5 or Trinity Large Thinking?
Mercury 2.5 is cheaper at $0.04 input / $0.15 output per million tokens (official Inception API price). Mercury Edit 2 costs $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). At a typical mix of three input tokens to one output token, that is $0.068 per million tokens for Mercury 2.5 versus $0.375 for Mercury Edit 2 (5.6× as much) and $0.388 for Trinity Large Thinking (5.7× 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, Mercury 2.5 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, Mercury 2.5 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 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 260,000 for Mercury 2.5 and 32,000 for Mercury Edit 2. Maximum output per response: Mercury Edit 2 up to 8,192, Mercury 2.5 up to 65,536, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Mercury Edit 2 accepts text; Mercury 2.5 accepts text; Trinity Large Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury Edit 2 and Mercury 2.5 is proprietary.
Which is newer?
Mercury 2.5 is the newest, released Sep 8, 2026. Trinity Large Thinking came out Apr 1, 2026; Mercury Edit 2 came out Mar 30, 2026. Knowledge cutoff: Mercury 2.5 Nov 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.
