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Comparison · 2 models · Updated Oct 4, 2026

MiniMax-M2.7-highspeed vs Trinity Large Thinking

Trinity Large Thinking comes out ahead, 55 to 41 on our weighted score, and it is the cheaper option too.

  1. MiniMax

    MiniMax-M2.7-highspeed

    Released Mar 18, 2026

    41/100
    • ECI—
    • Price$0.60 / $2.40
    • Context205K
    Full specs Remove MiniMax-M2.7-highspeed
  2. 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

  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 MiniMax-M2.7-highspeed (41). 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 · MiniMax-M2.7-highspeed $1.05 per 1M tokens (3:1 blend)
  • Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · MiniMax-M2.7-highspeed 204,800 tokens
  • Widest inputsSame inputsMiniMax-M2.7-highspeed: Text · Trinity Large Thinking: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightMiniMax-M2.7-highspeedTrinity Large Thinking
Price50%4969
Inputs & features30%3535
Context window20%3249
Overall100%41/10055/100

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.

MiniMax-M2.7-highspeed vs Trinity Large Thinking specifications side by side
SpecificationMiniMax-M2.7-highspeedMiniMaxTrinity Large ThinkingArcee AI
Capability
Capabilities Index (ECI)——
ECI rank——
Price per million tokens
Input$0.60$0.25 (best)
Output$2.40$0.80 (best)
Cached input$0.06$0.06
Blended (3:1)$1.05$0.388 (best)
Long-context rateSame rateSame rate
Price sourceOfficial MiniMax (minimax.io) APIOfficial Arcee API
Limits
Context window204,800 tokens524,288 tokens (best)
Max output131,072 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpenOpenMDW-1.1
API model IDMiniMax-M2.7-highspeedtrinity-large-thinking
API providers14 (best)6
ReleasedMar 18, 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.

  • MiniMax-M2.7-highspeed$10.80
  • Trinity Large Thinking$4.10
04 — Questions

Which should you choose?

Which is better: MiniMax-M2.7-highspeed or Trinity Large Thinking?

Trinity Large Thinking is the better all-round choice, scoring 55/100 against MiniMax-M2.7-highspeed (41). 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, MiniMax-M2.7-highspeed or Trinity Large Thinking?

Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). MiniMax-M2.7-highspeed costs $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) 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 $1.05 for MiniMax-M2.7-highspeed (2.7× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. MiniMax-M2.7-highspeed 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 MiniMax-M2.7-highspeed and Trinity Large Thinking 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 204,800 for MiniMax-M2.7-highspeed. Maximum output per response: MiniMax-M2.7-highspeed up to 131,072, Trinity Large Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

MiniMax-M2.7-highspeed accepts text; Trinity Large Thinking accepts text. They handle the same number of input types.

Are any of these open source?

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

Which is newer?

Trinity Large Thinking is the newest, released Apr 1, 2026. MiniMax-M2.7-highspeed came out Mar 18, 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

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