ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-4.7 vs Trinity Mini vs Qwen3 235B-A22B Thinking 2507

Trinity Mini comes out ahead, 65 to 42 and 41 on our weighted score, and it is the cheaper option too.

  1. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    41/100
    • ECI143.7
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7
  2. Our pick

    Arcee AI

    Trinity Mini

    Released Dec 1, 2025

    65/100
    • ECI—
    • Price$0.045 / $0.15
    • Context131K
    Full specs Remove Trinity Mini

  3. Alibaba (Qwen)

    Qwen3 235B-A22B Thinking 2507

    Released Jul 25, 2025

    42/100
    • ECI143.8
    • Price$0.30 / $2.90
    • Context262K
    Full specs Remove Qwen3 235B-A22B Thinking 2507
01 — Verdict

Trinity Mini is our pick

Trinity Mini is the better all-round choice, scoring 65/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on 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 MiniTrinity Mini $0.071 · Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Trinity Mini 131,072 tokens
  • Widest inputsSame inputsGLM-4.7: Text · Trinity Mini: Text · Qwen3 235B-A22B Thinking 2507: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7Trinity MiniQwen3 235B-A22B Thinking 2507
Price50%4810049
Inputs & features30%353535
Context window20%322537
Overall100%41/10065/10042/100

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-4.7 vs Trinity Mini vs Qwen3 235B-A22B Thinking 2507 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Trinity MiniArcee AIQwen3 235B-A22B Thinking 2507Alibaba (Qwen)
Capability
Capabilities Index (ECI)143.7—143.8 (best)
ECI rank#94 of 165—#93 of 165 (best)
GPQA DiamondGraduate-level science questions83.3% (best)—80.1%
OTIS Mock AIME 2024–2025Competition mathematics83.3%—86.7% (best)
SimpleQA VerifiedShort factual questions32.2%—40.4% (best)
Price per million tokens
Input$0.60$0.045 (best)$0.30
Output$2.20$0.15 (best)$2.90
Cached input$0.11——
Blended (3:1)$1.00$0.071 (best)$0.95
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 1 providersMedian of 11 providers
Limits
Context window204,800 tokens131,072 tokens262,144 tokens (best)
Max output131,072 tokens (best)131,072 tokens (best)81,920 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpenMDW-1.1OpenApache 2.0
API model IDglm-4.7——
API providers31 (best)112
ReleasedDec 22, 2025Dec 1, 2025Jul 25, 2025
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-4.7$10.40
  • Trinity Mini$0.75
  • Qwen3 235B-A22B Thinking 2507$8.80
04 — Questions

Which should you choose?

Which is better: GLM-4.7, Trinity Mini or Qwen3 235B-A22B Thinking 2507?

Trinity Mini is the better all-round choice, scoring 65/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on 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-4.7, Trinity Mini or Qwen3 235B-A22B Thinking 2507?

Trinity Mini is cheaper at $0.045 input / $0.15 output per million tokens (median across 1 API provider). Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.071 per million tokens for Trinity Mini versus $0.95 for Qwen3 235B-A22B Thinking 2507 (13× as much) and $1.00 for GLM-4.7 (14× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.7, Trinity Mini has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Trinity Mini and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for Trinity Mini. Maximum output per response: GLM-4.7 up to 131,072, Trinity Mini up to 131,072, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Trinity Mini accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. They handle the same number of input types.

Are any of these open source?

Yes, all three publish their weights (OpenMDW-1.1 and Apache 2.0), so you can self-host them.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Trinity Mini came out Dec 1, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: GLM-4.7 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 Nov 2, 2026.

05 — Keep comparing

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