Trinity Large Thinking vs Nemotron 3 Super 120B A12B vs Mercury Edit 2
Too close to call on our weighted score (Trinity Large Thinking 55, Nemotron 3 Super 120B A12B 54, Mercury Edit 2 35). The right pick depends on what you value most.
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
NVIDIANemotron 3 Super 120B A12B
54/100- ECI—
- Price$0.20 / $0.80
- Context262K
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
Too close to call
It is close. Our weighted score puts them within 1 points (Trinity Large Thinking 55/100, Nemotron 3 Super 120B A12B 54/100, Mercury Edit 2 35/100), so choose by what matters most for your work: Nemotron 3 Super 120B A12B 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 priceNemotron 3 Super 120B A12BNemotron 3 Super 120B A12B $0.35 · Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Nemotron 3 Super 120B A12B 262,144 · Mercury Edit 2 32,000 tokens
- Widest inputsSame inputsTrinity Large Thinking: Text · Nemotron 3 Super 120B A12B: Text · Mercury Edit 2: Text
- Self-hostingTrinity Large Thinking and Nemotron 3 Super 120B A12BPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Trinity Large Thinking | Nemotron 3 Super 120B A12B | Mercury Edit 2 |
|---|---|---|---|---|
| Price | 50% | 69 | 72 | 70 |
| Inputs & features | 30% | 35 | 35 | 0 |
| Context window | 20% | 49 | 37 | 0 |
| Overall | 100% | 55/100 | 54/100 | 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 | Nemotron 3 Super 120B A12BNVIDIA | Mercury Edit 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.20 (best) | $0.25 |
| Output | $0.80 | $0.80 | $0.75 (best) |
| Cached input | $0.06 | — | $0.025 (best) |
| Blended (3:1) | $0.388 | $0.35 (best) | $0.375 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Arcee API | Official Nvidia API | Official Inception API |
| Limits | |||
| Context window | 524,288 tokens (best) | 262,144 tokens | 32,000 tokens |
| Max output | 262,144 tokens (best) | 262,144 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenOpenMDW-1.1 | Open | Proprietary |
| API model ID | trinity-large-thinking | nvidia/nemotron-3-super-120b-a12b | mercury-edit-2 |
| API providers | 6 | 19 (best) | 1 |
| Released | Apr 1, 2026 | Mar 11, 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
- Nemotron 3 Super 120B A12B$3.60
- Mercury Edit 2$4.00
Which should you choose?
Which is better: Trinity Large Thinking, Nemotron 3 Super 120B A12B or Mercury Edit 2?
It is close. Our weighted score puts them within 1 points (Trinity Large Thinking 55/100, Nemotron 3 Super 120B A12B 54/100, Mercury Edit 2 35/100), so choose by what matters most for your work: Nemotron 3 Super 120B A12B 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, Trinity Large Thinking, Nemotron 3 Super 120B A12B or Mercury Edit 2?
Nemotron 3 Super 120B A12B is cheaper at $0.20 input / $0.80 output per million tokens (official Nvidia 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 $0.35 per million tokens for Nemotron 3 Super 120B A12B versus $0.375 for Mercury Edit 2 (1.1× as much) and $0.388 for Trinity Large Thinking (1.1× 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, Nemotron 3 Super 120B A12B 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, Nemotron 3 Super 120B A12B 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 Nemotron 3 Super 120B A12B and 32,000 for Mercury Edit 2. Maximum output per response: Trinity Large Thinking up to 262,144, Nemotron 3 Super 120B A12B up to 262,144, Mercury Edit 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Trinity Large Thinking accepts text; Nemotron 3 Super 120B A12B accepts text; Mercury Edit 2 accepts text. They handle the same number of input types.
Are any of these open source?
Trinity Large Thinking and Nemotron 3 Super 120B A12B 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; Nemotron 3 Super 120B A12B came out Mar 11, 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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