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Comparison · 3 models · Updated Oct 4, 2026

Qwen3 VL 235B A22B Instruct vs GLM-4.5V vs MiniMax-M2

Qwen3 VL 235B A22B Instruct comes out ahead, 53 to 49 and 48 on our weighted score, though MiniMax-M2 is 14% cheaper per token.

  1. Our pick

    Alibaba (Qwen)

    Qwen3 VL 235B A22B Instruct

    Released Sep 23, 2025

    53/100
    • ECI—
    • Price$0.30 / $1.55
    • Context131K
    Full specs Remove Qwen3 VL 235B A22B Instruct

  2. Z.ai (Zhipu)

    GLM-4.5V

    Released Aug 11, 2025

    49/100
    • ECI—
    • Price$0.60 / $1.80
    • Context64K
    Full specs Remove GLM-4.5V

  3. MiniMax

    MiniMax-M2

    Released Oct 27, 2025

    48/100
    • ECI—
    • Price$0.30 / $1.20
    • Context205K
    Full specs Remove MiniMax-M2
01 — Verdict

Qwen3 VL 235B A22B Instruct is our pick

Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against GLM-4.5V (49) and MiniMax-M2 (48). GLM-4.5V wins on inputs & features. MiniMax-M2 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 priceMiniMax-M2MiniMax-M2 $0.525 · Qwen3 VL 235B A22B Instruct $0.613 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
  • Longest contextMiniMax-M2MiniMax-M2 204,800 · Qwen3 VL 235B A22B Instruct 131,072 · GLM-4.5V 64,000 tokens
  • Widest inputsGLM-4.5VQwen3 VL 235B A22B Instruct: Text, Images · GLM-4.5V: Text, Images, Video · MiniMax-M2: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightQwen3 VL 235B A22B InstructGLM-4.5VMiniMax-M2
Price50%605263
Inputs & features30%607035
Context window20%241232
Overall100%53/10049/10048/100

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.

Qwen3 VL 235B A22B Instruct vs GLM-4.5V vs MiniMax-M2 specifications side by side
SpecificationQwen3 VL 235B A22B InstructAlibaba (Qwen)GLM-4.5VZ.ai (Zhipu)MiniMax-M2MiniMax
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.30 (best)$0.60$0.30 (best)
Output$1.55$1.80$1.20 (best)
Cached input———
Blended (3:1)$0.613$0.90$0.525 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 12 providersOfficial Z.AI APIOfficial MiniMax (minimax.io) API
Limits
Context window131,072 tokens64,000 tokens204,800 tokens (best)
Max output32,768 tokens16,384 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningNoYesYes
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsOpenOpenOpen
API model ID—glm-4.5vMiniMax-M2
API providers121113 (best)
ReleasedSep 23, 2025Aug 11, 2025Oct 27, 2025
Knowledge cutoffMar 31, 2025Apr 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.

  • Qwen3 VL 235B A22B Instruct$6.10
  • GLM-4.5V$9.60
  • MiniMax-M2$5.40
04 — Questions

Which should you choose?

Which is better: Qwen3 VL 235B A22B Instruct, GLM-4.5V or MiniMax-M2?

Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against GLM-4.5V (49) and MiniMax-M2 (48). GLM-4.5V wins on inputs & features. MiniMax-M2 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, Qwen3 VL 235B A22B Instruct, GLM-4.5V or MiniMax-M2?

MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3 VL 235B A22B Instruct costs $0.30 input / $1.55 output per million tokens (median across 12 API providers); GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2 versus $0.613 for Qwen3 VL 235B A22B Instruct (1.2× as much) and $0.90 for GLM-4.5V (1.7× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Qwen3 VL 235B A22B Instruct has not been scored yet, GLM-4.5V has not been scored yet and MiniMax-M2 has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 VL 235B A22B Instruct, GLM-4.5V and MiniMax-M2 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?

MiniMax-M2 has the largest context window at 204,800 tokens, against 131,072 for Qwen3 VL 235B A22B Instruct and 64,000 for GLM-4.5V. Maximum output per response: Qwen3 VL 235B A22B Instruct up to 32,768, GLM-4.5V up to 16,384, MiniMax-M2 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 VL 235B A22B Instruct accepts text and images; GLM-4.5V accepts text, images and video; MiniMax-M2 accepts text. GLM-4.5V handles the widest range of inputs.

Are any of these open source?

Yes, all three publish their weights, so you can self-host them.

Which is newer?

MiniMax-M2 is the newest, released Oct 27, 2025. Qwen3 VL 235B A22B Instruct came out Sep 23, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: Qwen3 VL 235B A22B Instruct Mar 31, 2025, GLM-4.5V 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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