Trinity Large Thinking vs Sarvam 105B vs Mercury Edit 2
Sarvam 105B comes out ahead, 65 to 55 and 35 on our weighted score, and it is the cheaper option too.
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
- Our pick
Sarvam AI
Sarvam 105B
65/100- ECI—
- Price$0.047 / $0.186
- Context131K
InceptionMercury Edit 2
35/100- ECI—
- Price$0.25 / $0.75
- Context32K
Sarvam 105B is our pick
Sarvam 105B is the better all-round choice, scoring 65/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on price. 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 priceSarvam 105BSarvam 105B $0.082 · Mercury Edit 2 $0.375 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Sarvam 105B 131,072 · Mercury Edit 2 32,000 tokens
- Widest inputsSame inputsTrinity Large Thinking: Text · Sarvam 105B: Text · Mercury Edit 2: Text
- Self-hostingTrinity Large Thinking and Sarvam 105BPublishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Trinity Large Thinking | Sarvam 105B | Mercury Edit 2 |
|---|---|---|---|---|
| Price | 50% | 69 | 100 | 70 |
| Inputs & features | 30% | 35 | 35 | 0 |
| Context window | 20% | 49 | 24 | 0 |
| Overall | 100% | 55/100 | 65/100 | 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 | Sarvam 105BSarvam AI | Mercury Edit 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.047 (best) | $0.25 |
| Output | $0.80 | $0.186 (best) | $0.75 |
| Cached input | $0.06 | — | $0.025 (best) |
| Blended (3:1) | $0.388 | $0.082 (best) | $0.375 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Arcee API | Median of 2 providers | Official Inception API |
| Limits | |||
| Context window | 524,288 tokens (best) | 131,072 tokens | 32,000 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens | 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 · low · medium · high | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenOpenMDW-1.1 | Open | Proprietary |
| API model ID | trinity-large-thinking | sarvam-105b | mercury-edit-2 |
| API providers | 6 (best) | 3 | 1 |
| Released | Apr 1, 2026 | Sep 1, 2025 | 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
- Sarvam 105B$0.842
- Mercury Edit 2$4.00
Which should you choose?
Which is better: Trinity Large Thinking, Sarvam 105B or Mercury Edit 2?
Sarvam 105B is the better all-round choice, scoring 65/100 against Trinity Large Thinking (55) and Mercury Edit 2 (35). It leads on price. 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, Sarvam 105B or Mercury Edit 2?
Sarvam 105B is cheaper at $0.047 input / $0.186 output per million tokens (median across 2 API providers). 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 $0.082 per million tokens for Sarvam 105B versus $0.375 for Mercury Edit 2 (4.6× as much) and $0.388 for Trinity Large Thinking (4.7× 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, Sarvam 105B 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, Sarvam 105B 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 131,072 for Sarvam 105B and 32,000 for Mercury Edit 2. Maximum output per response: Trinity Large Thinking up to 262,144, Sarvam 105B 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; Sarvam 105B 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 Sarvam 105B 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; Sarvam 105B came out Sep 1, 2025.
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.
