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Comparison · 2 models · Updated Oct 4, 2026

GLM-4.6V vs Trinity Large Thinking

GLM-4.6V comes out ahead, 59 to 55 on our weighted score, though Trinity Large Thinking is 14% cheaper per token.

  1. Our pick

    Z.ai (Zhipu)

    GLM-4.6V

    Released Dec 8, 2025

    59/100
    • ECI—
    • Price$0.30 / $0.90
    • Context128K
    Full specs Remove GLM-4.6V

  2. 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

GLM-4.6V is our pick

GLM-4.6V is the better all-round choice, scoring 59/100 against Trinity Large Thinking (55). It leads on inputs & features. Trinity Large Thinking wins 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 · GLM-4.6V $0.45 per 1M tokens (3:1 blend)
  • Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · GLM-4.6V 128,000 tokens
  • Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · Trinity Large Thinking: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.6VTrinity Large Thinking
Price50%6669
Inputs & features30%7035
Context window20%2449
Overall100%59/10055/100

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.

GLM-4.6V vs Trinity Large Thinking specifications side by side
SpecificationGLM-4.6VZ.ai (Zhipu)Trinity Large ThinkingArcee AI
Capability
Capabilities Index (ECI)——
ECI rank——
Price per million tokens
Input$0.30$0.25 (best)
Output$0.90$0.80 (best)
Cached input—$0.06
Blended (3:1)$0.45$0.388 (best)
Long-context rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial Arcee API
Limits
Context window128,000 tokens524,288 tokens (best)
Max output32,768 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpenOpenMDW-1.1
API model IDglm-4.6vtrinity-large-thinking
API providers10 (best)6
ReleasedDec 8, 2025Apr 1, 2026
Knowledge cutoffApr 2025—
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.

  • GLM-4.6V$4.80
  • Trinity Large Thinking$4.10
04 — Questions

Which should you choose?

Which is better: GLM-4.6V or Trinity Large Thinking?

GLM-4.6V is the better all-round choice, scoring 59/100 against Trinity Large Thinking (55). It leads on inputs & features. Trinity Large Thinking wins 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, GLM-4.6V or Trinity Large Thinking?

Trinity Large Thinking is cheaper at $0.25 input / $0.80 output per million tokens (official Arcee API price). GLM-4.6V costs $0.30 input / $0.90 output per million tokens (official Z.AI 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 $0.45 for GLM-4.6V (1.2× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. GLM-4.6V 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 GLM-4.6V 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 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, Trinity Large Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

GLM-4.6V accepts text, images and video; Trinity Large Thinking accepts text. GLM-4.6V 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?

Trinity Large Thinking is the newest, released Apr 1, 2026. GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V Apr 2025.

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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