Mercury Edit 2 vs Hy3 preview vs Trinity Large Thinking
Too close to call on our weighted score (Hy3 preview 56, Trinity Large Thinking 55, Mercury Edit 2 35). The right pick depends on what you value most.
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
TencentHy3 preview
56/100- ECI—
- Price$0.176 / $0.586
- Context256K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Too close to call
It is close. Our weighted score puts them within a point (Hy3 preview 56/100, Trinity Large Thinking 55/100, Mercury Edit 2 35/100), so choose by what matters most for your work: Hy3 preview on price and Trinity Large Thinking for long inputs. 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 priceHy3 previewHy3 preview $0.279 · Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Hy3 preview 256,000 · Mercury Edit 2 32,000 tokens
- Widest inputsSame inputsMercury Edit 2: Text · Hy3 preview: Text · Trinity Large Thinking: Text
- Self-hostingHy3 preview and Trinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Mercury Edit 2 | Hy3 preview | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 70 | 76 | 69 |
| Inputs & features | 30% | 0 | 35 | 35 |
| Context window | 20% | 0 | 36 | 49 |
| Overall | 100% | 35/100 | 56/100 | 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 | Hy3 previewTencent | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.176 (best) | $0.25 |
| Output | $0.75 | $0.586 (best) | $0.80 |
| Cached input | $0.025 (best) | — | $0.06 |
| Blended (3:1) | $0.375 | $0.279 (best) | $0.388 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Median of 4 providers | Official Arcee API |
| Limits | |||
| Context window | 32,000 tokens | 256,000 tokens | 524,288 tokens (best) |
| Max output | 8,192 tokens | 64,000 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 | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenOpenMDW-1.1 |
| API model ID | mercury-edit-2 | hy3-preview | trinity-large-thinking |
| API providers | 1 | 5 | 6 (best) |
| Released | Mar 30, 2026 | Apr 20, 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
- Hy3 preview$2.93
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: Mercury Edit 2, Hy3 preview or Trinity Large Thinking?
It is close. Our weighted score puts them within a point (Hy3 preview 56/100, Trinity Large Thinking 55/100, Mercury Edit 2 35/100), so choose by what matters most for your work: Hy3 preview on price and Trinity Large Thinking for long inputs. 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, Hy3 preview or Trinity Large Thinking?
Hy3 preview is cheaper at $0.176 input / $0.586 output per million tokens (median across 4 API providers; free on Tencent TokenHub). 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 $0.279 per million tokens for Hy3 preview versus $0.375 for Mercury Edit 2 (1.3× as much) and $0.388 for Trinity Large Thinking (1.4× 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, Hy3 preview 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, Hy3 preview 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 256,000 for Hy3 preview and 32,000 for Mercury Edit 2. Maximum output per response: Mercury Edit 2 up to 8,192, Hy3 preview up to 64,000, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Mercury Edit 2 accepts text; Hy3 preview accepts text; Trinity Large Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Hy3 preview and Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury Edit 2 is proprietary.
Which is newer?
Hy3 preview is the newest, released Apr 20, 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 Oct 4, 2026.
