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Comparison · 3 models · Updated Nov 2, 2026

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

Too close to call on our weighted score (Trinity Large Thinking 53, Mercury 2 52, Qwen3.5 9B Base 50). The right pick depends on what you value most.

  1. 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

  2. Inception

    Mercury 2

    Released Feb 24, 2026

    52/100
    • ECI—
    • Price$0.25 / $0.75
    • Context128K
    Full specs Remove Mercury 2

  3. 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

Too close to call

It is close. Our weighted score puts them within 2 points (Trinity Large Thinking 53/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 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 2Mercury 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 2 128,000 tokens
  • Widest inputsQwen3.5 9B BaseQwen3.5 9B Base: Text, Images, Video · Mercury 2: Text · 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
MeasureWeightQwen3.5 9B BaseMercury 2Trinity Large Thinking
Price50%656766
Inputs & features30%354535
Context window20%372449
Overall100%50/10052/10053/100

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.

Qwen3.5 9B Base vs Mercury 2 vs Trinity Large Thinking specifications side by side
SpecificationQwen3.5 9B BaseAlibaba (Qwen)Mercury 2InceptionTrinity Large ThinkingArcee AI
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.30$0.25 (best)$0.25 (best)
Output$0.807$0.75 (best)$0.80
Cached input—$0.025 (best)$0.06
Blended (3:1)$0.427$0.375 (best)$0.388
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 1 providersOfficial Inception APIOfficial Arcee API
Limits
Context window262,144 tokens128,000 tokens524,288 tokens (best)
Max output65,536 tokens50,000 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsNoNoNo
AudioNoNoNo
VideoYesNoNo
ReasoningNoYeslow · medium · highYes
Tool callingNoYesYes
Structured outputNoYesNo
Availability
WeightsOpenApache-2.0ProprietaryOpenOpenMDW-1.1
API model ID—mercury-2trinity-large-thinking
API providers17 (best)3
ReleasedFeb 26, 2026Feb 24, 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.

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

Which should you choose?

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

It is close. Our weighted score puts them within 2 points (Trinity Large Thinking 53/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 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, Qwen3.5 9B Base, Mercury 2 or Trinity Large Thinking?

Mercury 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 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. Qwen3.5 9B Base has not been scored yet, Mercury 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 Qwen3.5 9B Base, Mercury 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 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 128,000 for Mercury 2. Maximum output per response: Qwen3.5 9B Base up to 65,536, Mercury 2 up to 50,000, Trinity Large Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Qwen3.5 9B Base accepts text, images and video; Mercury 2 accepts text; 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 2 is proprietary.

Which is newer?

Trinity Large Thinking is the newest, released Apr 1, 2026. Qwen3.5 9B Base came out Feb 26, 2026; Mercury 2 came out Feb 24, 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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