ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Trinity Large Thinking vs Motif 3

Trinity Large Thinking comes out ahead, 55 to 44 on our weighted score, and it is the cheaper option too.

  1. Our pick

    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. Motif Technologies

    Motif 3

    Released Aug 7, 2026

    44/100
    • ECI—
    • Price$0.50 / $2.00
    • Context262K
    Full specs Remove Motif 3

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Trinity Large Thinking is our pick

Trinity Large Thinking is the better all-round choice, scoring 55/100 against Motif 3 (44). It leads on price and 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 priceTrinity Large ThinkingTrinity Large Thinking $0.388 · Motif 3 $0.875 per 1M tokens (3:1 blend)
  • Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Motif 3 262,144 tokens
  • Widest inputsSame inputsTrinity Large Thinking: Text · Motif 3: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightTrinity Large ThinkingMotif 3
Price50%6953
Inputs & features30%3535
Context window20%4937
Overall100%55/10044/100

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 Motif 3 specifications side by side
SpecificationTrinity Large ThinkingArcee AIMotif 3Motif Technologies
Capability
Capabilities Index (ECI)——
ECI rank——
Price per million tokens
Input$0.25 (best)$0.50
Output$0.80 (best)$2.00
Cached input$0.06—
Blended (3:1)$0.388 (best)$0.875
Long-context rateSame rateSame rate
Price sourceOfficial Arcee APIMedian of 1 providers
Limits
Context window524,288 tokens (best)262,144 tokens
Max output262,144 tokens262,144 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpenMDW-1.1OpenMIT
API model IDtrinity-large-thinking—
API providers6 (best)1
ReleasedApr 1, 2026Aug 7, 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
  • Motif 3$9.00
04 — Questions

Which should you choose?

Which is better: Trinity Large Thinking or Motif 3?

Trinity Large Thinking is the better all-round choice, scoring 55/100 against Motif 3 (44). It leads on price and 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 or Motif 3?

Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). Motif 3 costs $0.50 input / $2.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.388 per million tokens for Trinity Large Thinking versus $0.875 for Motif 3 (2.3× 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 Motif 3 has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Trinity Large Thinking and Motif 3 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 Motif 3. Maximum output per response: Trinity Large Thinking up to 262,144, Motif 3 up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Trinity Large Thinking accepts text; Motif 3 accepts text. They handle the same number of input types.

Are any of these open source?

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

Which is newer?

Motif 3 is the newest, released Aug 7, 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