Trinity Large Thinking vs Qwen3.5 122B-A10B vs Mercury Edit 2
Qwen3.5 122B-A10B comes out ahead, 58 to 55 and 35 on our weighted score, though Mercury Edit 2 is 2.9× cheaper per token.
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
- Our pick
Alibaba (Qwen)Qwen3.5 122B-A10B
58/100- ECI—
- Price$0.40 / $3.20
- Context262K
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
Qwen3.5 122B-A10B is our pick
Qwen3.5 122B-A10B is the better all-round choice, scoring 58/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on 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 Edit 2Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 · Qwen3.5 122B-A10B $1.10 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Qwen3.5 122B-A10B 262,144 · Mercury Edit 2 32,000 tokens
- Widest inputsQwen3.5 122B-A10BTrinity Large Thinking: Text · Qwen3.5 122B-A10B: Text, Images, Audio, Video · Mercury Edit 2: Text
- Self-hostingTrinity Large Thinking and Qwen3.5 122B-A10BPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Trinity Large Thinking | Qwen3.5 122B-A10B | Mercury Edit 2 |
|---|---|---|---|---|
| Price | 50% | 69 | 48 | 70 |
| Inputs & features | 30% | 35 | 90 | 0 |
| Context window | 20% | 49 | 37 | 0 |
| Overall | 100% | 55/100 | 58/100 | 35/100 |
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 | Qwen3.5 122B-A10BAlibaba (Qwen) | Mercury Edit 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.40 | $0.25 (best) |
| Output | $0.80 | $3.20 | $0.75 (best) |
| Cached input | $0.06 | — | $0.025 (best) |
| Blended (3:1) | $0.388 | $1.10 | $0.375 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Arcee API | Official Alibaba API | Official Inception API |
| Limits | |||
| Context window | 524,288 tokens (best) | 262,144 tokens | 32,000 tokens |
| Max output | 262,144 tokens (best) | 65,536 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 | qwen3.5-122b-a10b | mercury-edit-2 |
| API providers | 6 | 19 (best) | 1 |
| Released | Apr 1, 2026 | Feb 23, 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
- Qwen3.5 122B-A10B$10.40
- Mercury Edit 2$4.00
Which should you choose?
Which is better: Trinity Large Thinking, Qwen3.5 122B-A10B or Mercury Edit 2?
Qwen3.5 122B-A10B is the better all-round choice, scoring 58/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on 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, Trinity Large Thinking, Qwen3.5 122B-A10B or Mercury Edit 2?
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); Qwen3.5 122B-A10B costs $0.40 input / $3.20 output per million tokens (official Alibaba API price). 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 $1.10 for Qwen3.5 122B-A10B (2.9× 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, Qwen3.5 122B-A10B 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, Qwen3.5 122B-A10B 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?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 262,144 for Qwen3.5 122B-A10B and 32,000 for Mercury Edit 2. Maximum output per response: Trinity Large Thinking up to 262,144, Qwen3.5 122B-A10B up to 65,536, Mercury Edit 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Trinity Large Thinking accepts text; Qwen3.5 122B-A10B accepts text, images, audio and video; Mercury Edit 2 accepts text. Qwen3.5 122B-A10B handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking and Qwen3.5 122B-A10B publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury Edit 2 is proprietary.
Which is newer?
Trinity Large Thinking is the newest, released Apr 1, 2026. Mercury Edit 2 came out Mar 30, 2026; Qwen3.5 122B-A10B came out Feb 23, 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.
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