ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Trinity Large Thinking vs Qwen3.6 27B

Too close to call on our weighted score (Qwen3.6 27B 56, Trinity Large Thinking 55). The right pick depends on what you value most.

  1. Arcee AI

    Trinity Large Thinking

    Released Apr 1, 2026Beta

    55/100
    • ECI—
    • Price$0.25 / $0.80
    • Context524K
    Full specs Remove Trinity Large Thinking

  2. Alibaba (Qwen)

    Qwen3.6 27B

    Released Apr 22, 2026

    56/100
    • ECI146.5
    • Price$0.60 / $3.60
    • Context262K
    Full specs Remove Qwen3.6 27B

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (Qwen3.6 27B 56/100, Trinity Large Thinking 55/100), so choose by what matters most for your work: Trinity Large Thinking 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 priceTrinity Large ThinkingTrinity Large Thinking $0.388 · Qwen3.6 27B $1.35 per 1M tokens (3:1 blend)
  • Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Qwen3.6 27B 262,144 tokens
  • Widest inputsQwen3.6 27BTrinity Large Thinking: Text · Qwen3.6 27B: Text, Images, Audio, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightTrinity Large ThinkingQwen3.6 27B
Price50%6944
Inputs & features30%3590
Context window20%4937
Overall100%55/10056/100

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.

Trinity Large Thinking vs Qwen3.6 27B specifications side by side
SpecificationTrinity Large ThinkingArcee AIQwen3.6 27BAlibaba (Qwen)
Capability
Capabilities Index (ECI)—146.5
ECI rank—#68 of 148
GPQA DiamondGraduate-level science questions—85.9%
FrontierMath Tiers 1–3Research-level mathematics—35.1%
OTIS Mock AIME 2024–2025Competition mathematics—91.1%
Price per million tokens
Input$0.25 (best)$0.60
Output$0.80 (best)$3.60
Cached input$0.06—
Blended (3:1)$0.388 (best)$1.35
Long-context rateSame rateSame rate
Price sourceOfficial Arcee APIOfficial Alibaba API
Limits
Context window524,288 tokens (best)262,144 tokens
Max output262,144 tokens (best)65,536 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoYes
VideoNoYes
ReasoningYesYes
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenOpenMDW-1.1Open
API model IDtrinity-large-thinkingqwen3.6-27b
API providers627 (best)
ReleasedApr 1, 2026Apr 22, 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.

  • Trinity Large Thinking$4.10
  • Qwen3.6 27B$13.20
04 — Questions

Which should you choose?

Which is better: Trinity Large Thinking or Qwen3.6 27B?

It is close. Our weighted score puts them within 1 points (Qwen3.6 27B 56/100, Trinity Large Thinking 55/100), so choose by what matters most for your work: Trinity Large Thinking 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, Trinity Large Thinking or Qwen3.6 27B?

Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $1.35 for Qwen3.6 27B (3.5× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. Trinity Large Thinking has not been scored yet and Qwen3.6 27B has an ECI of 146.5.

Which is better for coding?

There are no published SWE-bench Verified results for Trinity Large Thinking and Qwen3.6 27B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both 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 Qwen3.6 27B. Maximum output per response: Trinity Large Thinking up to 262,144, Qwen3.6 27B up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Trinity Large Thinking accepts text; Qwen3.6 27B accepts text, images, audio and video. Qwen3.6 27B handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights (OpenMDW-1.1), so you can self-host them.

Which is newer?

Qwen3.6 27B is the newest, released Apr 22, 2026. Trinity Large Thinking came out Apr 1, 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.

05 — Keep comparing

Related comparisons