Trinity Large Thinking vs MiMo-V2.6-Flash vs Mercury Edit 2
MiMo-V2.6-Flash comes out ahead, 80 to 53 and 34 on our weighted score, and it is the cheaper option too.
Arcee AITrinity Large Thinking
53/100- ECI—
- Price$0.25 / $0.80
- Context524K
- Our pick
XiaomiMiMo-V2.6-Flash
80/100- ECI—
- Price$0.14 / $0.28
- Context1.05M
InceptionMercury Edit 2
34/100- ECI—
- Price$0.25 / $0.75
- Context32K
MiMo-V2.6-Flash is our pick
MiMo-V2.6-Flash is the better all-round choice, scoring 80/100 against Trinity Large Thinking (53) and Mercury Edit 2 (34). It leads on price, inputs & features and 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 priceMiMo-V2.6-FlashMiMo-V2.6-Flash $0.175 · Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextMiMo-V2.6-FlashMiMo-V2.6-Flash 1,048,576 · Trinity Large Thinking 524,288 · Mercury Edit 2 32,000 tokens
- Widest inputsMiMo-V2.6-FlashTrinity Large Thinking: Text · MiMo-V2.6-Flash: Text, Images, Audio, Video · Mercury Edit 2: Text
- Self-hostingTrinity Large Thinking and MiMo-V2.6-FlashPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Trinity Large Thinking | MiMo-V2.6-Flash | Mercury Edit 2 |
|---|---|---|---|---|
| Price | 50% | 66 | 82 | 67 |
| Inputs & features | 30% | 35 | 90 | 0 |
| Context window | 20% | 49 | 61 | 0 |
| Overall | 100% | 53/100 | 80/100 | 34/100 |
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 | MiMo-V2.6-FlashXiaomi | Mercury Edit 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.14 (best) | $0.25 |
| Output | $0.80 | $0.28 (best) | $0.75 |
| Cached input | $0.06 | $0.0028 (best) | $0.025 |
| Blended (3:1) | $0.388 | $0.175 (best) | $0.375 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Arcee API | Official Xiaomi API | Official Inception API |
| Limits | |||
| Context window | 524,288 tokens | 1,048,576 tokens (best) | 32,000 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenOpenMDW-1.1 | Open | Proprietary |
| API model ID | trinity-large-thinking | mimo-v2.6-flash | mercury-edit-2 |
| API providers | 3 | 23 (best) | 1 |
| Released | Apr 1, 2026 | Sep 22, 2026 | Mar 30, 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
- MiMo-V2.6-Flash$1.96
- Mercury Edit 2$4.00
Which should you choose?
Which is better: Trinity Large Thinking, MiMo-V2.6-Flash or Mercury Edit 2?
MiMo-V2.6-Flash is the better all-round choice, scoring 80/100 against Trinity Large Thinking (53) and Mercury Edit 2 (34). It leads on price, inputs & features and 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, Trinity Large Thinking, MiMo-V2.6-Flash or Mercury Edit 2?
MiMo-V2.6-Flash is cheaper at $0.14 input / $0.28 output per million tokens (official Xiaomi 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 $0.175 per million tokens for MiMo-V2.6-Flash versus $0.375 for Mercury Edit 2 (2.1× as much) and $0.388 for Trinity Large Thinking (2.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Trinity Large Thinking has not been scored yet, MiMo-V2.6-Flash has not been scored yet and Mercury Edit 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Trinity Large Thinking, MiMo-V2.6-Flash and Mercury Edit 2 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?
MiMo-V2.6-Flash has the largest context window at 1,048,576 tokens, against 524,288 for Trinity Large Thinking and 32,000 for Mercury Edit 2. Maximum output per response: Trinity Large Thinking up to 262,144, MiMo-V2.6-Flash up to 131,072, Mercury Edit 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Trinity Large Thinking accepts text; MiMo-V2.6-Flash accepts text, images, audio and video; Mercury Edit 2 accepts text. MiMo-V2.6-Flash handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking and MiMo-V2.6-Flash publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury Edit 2 is proprietary.
Which is newer?
MiMo-V2.6-Flash is the newest, released Sep 22, 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.
