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Comparison · 3 models · Updated Nov 2, 2026

Mercury Edit 2 vs Qwen3.5 9B Base vs Trinity Large Thinking

Trinity Large Thinking comes out ahead, 53 to 50 and 34 on our weighted score.

  1. Inception

    Mercury Edit 2

    Released Mar 30, 2026

    34/100
    • ECI—
    • Price$0.25 / $0.75
    • Context32K
    Full specs Remove Mercury Edit 2

  2. Alibaba (Qwen)

    Qwen3.5 9B Base

    Released Feb 26, 2026

    50/100
    • ECI—
    • Price$0.30 / $0.807
    • Context262K
    Full specs Remove Qwen3.5 9B Base
  3. Our pick

    Arcee AI

    Trinity Large Thinking

    Released Apr 1, 2026Beta

    53/100
    • ECI—
    • Price$0.25 / $0.80
    • Context524K
    Full specs Remove Trinity Large Thinking
01 — Verdict

Trinity Large Thinking is our pick

Trinity Large Thinking is the better all-round choice, scoring 53/100 against Qwen3.5 9B Base (50) and Mercury Edit 2 (34). It leads 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 9B Base $0.427 per 1M tokens (3:1 blend)
  • Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Qwen3.5 9B Base 262,144 · Mercury Edit 2 32,000 tokens
  • Widest inputsQwen3.5 9B BaseMercury Edit 2: Text · Qwen3.5 9B Base: Text, Images, Video · Trinity Large Thinking: Text
  • Self-hostingQwen3.5 9B Base and Trinity Large ThinkingPublishes downloadable weights (Apache-2.0 and OpenMDW-1.1)
How the score is built
MeasureWeightMercury Edit 2Qwen3.5 9B BaseTrinity Large Thinking
Price50%676566
Inputs & features30%03535
Context window20%03749
Overall100%34/10050/10053/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Mercury Edit 2 vs Qwen3.5 9B Base vs Trinity Large Thinking specifications side by side
SpecificationMercury Edit 2InceptionQwen3.5 9B BaseAlibaba (Qwen)Trinity Large ThinkingArcee AI
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.25 (best)$0.30$0.25 (best)
Output$0.75 (best)$0.807$0.80
Cached input$0.025 (best)—$0.06
Blended (3:1)$0.375 (best)$0.427$0.388
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Inception APIMedian of 1 providersOfficial Arcee API
Limits
Context window32,000 tokens262,144 tokens524,288 tokens (best)
Max output8,192 tokens65,536 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningNoNoYes
Tool callingNoNoYes
Structured outputNoNoNo
Availability
WeightsProprietaryOpenApache-2.0OpenOpenMDW-1.1
API model IDmercury-edit-2—trinity-large-thinking
API providers113 (best)
ReleasedMar 30, 2026Feb 26, 2026Apr 1, 2026
Knowledge cutoff———
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Mercury Edit 2$4.00
  • Qwen3.5 9B Base$4.61
  • Trinity Large Thinking$4.10
04 — Questions

Which should you choose?

Which is better: Mercury Edit 2, Qwen3.5 9B Base or Trinity Large Thinking?

Trinity Large Thinking is the better all-round choice, scoring 53/100 against Qwen3.5 9B Base (50) and Mercury Edit 2 (34). It leads 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, Mercury Edit 2, Qwen3.5 9B Base 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); Qwen3.5 9B Base costs $0.30 input / $0.807 output per million tokens (median across 1 API provider). 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 $0.427 for Qwen3.5 9B Base (1.1× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Mercury Edit 2 has not been scored yet, Qwen3.5 9B Base 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 Mercury Edit 2, Qwen3.5 9B Base 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 and Qwen3.5 9B Base 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 9B Base and 32,000 for Mercury Edit 2. Maximum output per response: Mercury Edit 2 up to 8,192, Qwen3.5 9B Base up to 65,536, Trinity Large Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Mercury Edit 2 accepts text; Qwen3.5 9B Base accepts text, images and video; Trinity Large Thinking accepts text. Qwen3.5 9B Base handles the widest range of inputs.

Are any of these open source?

Qwen3.5 9B Base and Trinity Large Thinking publishes its weights (Apache-2.0 and 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 9B Base came out Feb 26, 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 Nov 2, 2026.

05 — Keep comparing

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