GPT-5.6 Sol vs Mercury Edit 2 vs Trinity Large Thinking
Trinity Large Thinking comes out ahead, 55 to 40 and 35 on our weighted score.
OpenAIGPT-5.6 Sol
40/100- ECI161.8
- Price$4.00 / $20.00
- Context1.05M
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
- Our pick
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
Trinity Large Thinking is our pick
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GPT-5.6 Sol (40) and Mercury Edit 2 (35). GPT-5.6 Sol wins on 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 priceMercury Edit 2Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 SolGPT-5.6 Sol 1,050,000 · Trinity Large Thinking 524,288 · Mercury Edit 2 32,000 tokens
- Widest inputsGPT-5.6 SolGPT-5.6 Sol: Text, Images, PDFs · Mercury Edit 2: Text · Trinity Large Thinking: Text
- Self-hostingTrinity Large ThinkingPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | GPT-5.6 Sol | Mercury Edit 2 | Trinity Large Thinking |
|---|---|---|---|---|
| Price | 50% | 7 | 70 | 69 |
| Inputs & features | 30% | 80 | 0 | 35 |
| Context window | 20% | 61 | 0 | 49 |
| Overall | 100% | 40/100 | 35/100 | 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 | GPT-5.6 SolOpenAI | Mercury Edit 2Inception | Trinity Large ThinkingArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 161.8 | — | — |
| ECI rank | #8 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 93.5% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 89.1% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | — |
| SimpleQA VerifiedShort factual questions | 69.7% | — | — |
| Price per million tokens | |||
| Input | $4.00 | $0.25 (best) | $0.25 (best) |
| Output | $20.00 | $0.75 (best) | $0.80 |
| Cached input | $0.40 | $0.025 (best) | $0.06 |
| Blended (3:1) | $8.00 | $0.375 (best) | $0.388 |
| Long-context rate | Over 272K: $8.00 / $30.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Inception API | Official Arcee API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 32,000 tokens | 524,288 tokens |
| Max output | 128,000 tokens | 8,192 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenOpenMDW-1.1 |
| API model ID | gpt-5.6-sol | mercury-edit-2 | trinity-large-thinking |
| API providers | 40 (best) | 1 | 6 |
| Released | Jul 9, 2026 | Mar 30, 2026 | Apr 1, 2026 |
| Knowledge cutoff | Feb 16, 2026 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.6 Sol$80.00
- Mercury Edit 2$4.00
- Trinity Large Thinking$4.10
Which should you choose?
Which is better: GPT-5.6 Sol, Mercury Edit 2 or Trinity Large Thinking?
Trinity Large Thinking is the better all-round choice, scoring 55/100 against GPT-5.6 Sol (40) and Mercury Edit 2 (35). GPT-5.6 Sol wins on 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, GPT-5.6 Sol, Mercury Edit 2 or Trinity Large Thinking?
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); GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI 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 $8.00 for GPT-5.6 Sol (21× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.6 Sol has an ECI of 161.8, Mercury Edit 2 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 GPT-5.6 Sol, Mercury Edit 2 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?
GPT-5.6 Sol has the largest context window at 1,050,000 tokens, against 524,288 for Trinity Large Thinking and 32,000 for Mercury Edit 2. Maximum output per response: GPT-5.6 Sol up to 128,000, Mercury Edit 2 up to 8,192, Trinity Large Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GPT-5.6 Sol accepts text, images and PDFs; Mercury Edit 2 accepts text; Trinity Large Thinking accepts text. GPT-5.6 Sol handles the widest range of inputs.
Are any of these open source?
Trinity Large Thinking publishes its weights (OpenMDW-1.1) and can be self-hosted; GPT-5.6 Sol and Mercury Edit 2 is proprietary.
Which is newer?
GPT-5.6 Sol is the newest, released Jul 9, 2026. Trinity Large Thinking came out Apr 1, 2026; Mercury Edit 2 came out Mar 30, 2026. Knowledge cutoff: GPT-5.6 Sol Feb 16, 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.
