Mercury 2.5 vs Trinity Large Thinking vs Laguna XS 2.1
Too close to call on our weighted score (Mercury 2.5 71, Laguna XS 2.1 68, Trinity Large Thinking 55). The right pick depends on what you value most.
InceptionMercury 2.5
71/100- ECI—
- Price$0.04 / $0.15
- Context260K
Arcee AITrinity Large Thinking
55/100- ECI—
- Price$0.25 / $0.80
- Context524K
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (Mercury 2.5 71/100, Laguna XS 2.1 68/100, Trinity Large Thinking 55/100), so choose by what matters most for your work: Mercury 2.5 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 priceMercury 2.5Mercury 2.5 $0.068 · Laguna XS 2.1 $0.075 · Trinity Large Thinking $0.388 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Laguna XS 2.1 262,144 · Mercury 2.5 260,000 tokens
- Widest inputsSame inputsMercury 2.5: Text · Trinity Large Thinking: Text · Laguna XS 2.1: Text
- Self-hostingTrinity Large Thinking and Laguna XS 2.1Publishes downloadable weights (OpenMDW-1.1)
| Measure | Weight | Mercury 2.5 | Trinity Large Thinking | Laguna XS 2.1 |
|---|---|---|---|---|
| Price | 50% | 100 | 69 | 100 |
| Inputs & features | 30% | 45 | 35 | 35 |
| Context window | 20% | 36 | 49 | 37 |
| Overall | 100% | 71/100 | 55/100 | 68/100 |
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 | Mercury 2.5Inception | Trinity Large ThinkingArcee AI | Laguna XS 2.1Poolside |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.04 (best) | $0.25 | $0.06 |
| Output | $0.15 | $0.80 | $0.12 (best) |
| Cached input | $0.004 (best) | $0.06 | — |
| Blended (3:1) | $0.068 (best) | $0.388 | $0.075 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Arcee API | Median of 1 providers |
| Limits | |||
| Context window | 260,000 tokens | 524,288 tokens (best) | 262,144 tokens |
| Max output | 65,536 tokens | 262,144 tokens (best) | 32,768 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 | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenOpenMDW-1.1 | Open |
| API model ID | mercury-2.5 | trinity-large-thinking | poolside/laguna-xs-2.1 |
| API providers | 1 | 6 (best) | 4 |
| Released | Sep 8, 2026 | Apr 1, 2026 | Jul 2, 2026 |
| Knowledge cutoff | Nov 1, 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mercury 2.5$0.70
- Trinity Large Thinking$4.10
- Laguna XS 2.1$0.84
Which should you choose?
Which is better: Mercury 2.5, Trinity Large Thinking or Laguna XS 2.1?
It is close. Our weighted score puts them within 3 points (Mercury 2.5 71/100, Laguna XS 2.1 68/100, Trinity Large Thinking 55/100), so choose by what matters most for your work: Mercury 2.5 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, Mercury 2.5, Trinity Large Thinking or Laguna XS 2.1?
Mercury 2.5 is cheaper at $0.04 input / $0.15 output per million tokens (official Inception API price). Laguna XS 2.1 costs $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside); 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 $0.068 per million tokens for Mercury 2.5 versus $0.075 for Laguna XS 2.1 (1.1× as much) and $0.388 for Trinity Large Thinking (5.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mercury 2.5 has not been scored yet, Trinity Large Thinking has not been scored yet and Laguna XS 2.1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 2.5, Trinity Large Thinking and Laguna XS 2.1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 262,144 for Laguna XS 2.1 and 260,000 for Mercury 2.5. Maximum output per response: Mercury 2.5 up to 65,536, Trinity Large Thinking up to 262,144, Laguna XS 2.1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Mercury 2.5 accepts text; Trinity Large Thinking accepts text; Laguna XS 2.1 accepts text. They handle the same number of input types.
Are any of these open source?
Trinity Large Thinking and Laguna XS 2.1 publishes its weights (OpenMDW-1.1) and can be self-hosted; Mercury 2.5 is proprietary.
Which is newer?
Mercury 2.5 is the newest, released Sep 8, 2026. Laguna XS 2.1 came out Jul 2, 2026; Trinity Large Thinking came out Apr 1, 2026. Knowledge cutoff: Mercury 2.5 Nov 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.
