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Comparison · 3 models · Updated Oct 4, 2026

GLM-4.5V vs Qwen3 VL 235B A22B Instruct vs Qwen3-Next 80B-A3B Instruct

Qwen3 VL 235B A22B Instruct comes out ahead, 53 to 49 and 39 on our weighted score, and it is the cheaper option too.

  1. Z.ai (Zhipu)

    GLM-4.5V

    Released Aug 11, 2025

    49/100
    • ECI—
    • Price$0.60 / $1.80
    • Context64K
  2. Our pick

    Alibaba (Qwen)

    Qwen3 VL 235B A22B Instruct

    Released Sep 23, 2025

    53/100
    • ECI—
    • Price$0.30 / $1.55
    • Context131K
  3. Alibaba (Qwen)

    Qwen3-Next 80B-A3B Instruct

    Released Sep 2025

    39/100
    • ECI—
    • Price$0.50 / $2.00
    • Context131K
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 Qwen3-Next 80B-A3B Instruct (39). It leads on price. GLM-4.5V wins on inputs & features. 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 priceQwen3 VL 235B A22B InstructQwen3 VL 235B A22B Instruct $0.613 · Qwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
  • Longest contextQwen3 VL 235B A22B Instruct and Qwen3-Next 80B-A3B InstructQwen3 VL 235B A22B Instruct 131,072 · Qwen3-Next 80B-A3B Instruct 131,072 · GLM-4.5V 64,000 tokens
  • Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen3 VL 235B A22B Instruct: Text, Images · Qwen3-Next 80B-A3B Instruct: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.5VQwen3 VL 235B A22B InstructQwen3-Next 80B-A3B Instruct
Price50%526053
Inputs & features30%706025
Context window20%122424
Overall100%49/10053/10039/100

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-4.5V vs Qwen3 VL 235B A22B Instruct vs Qwen3-Next 80B-A3B Instruct specifications side by side
SpecificationGLM-4.5VZ.ai (Zhipu)Qwen3 VL 235B A22B InstructAlibaba (Qwen)Qwen3-Next 80B-A3B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.60$0.30 (best)$0.50
Output$1.80$1.55 (best)$2.00
Cached input———
Blended (3:1)$0.90$0.613 (best)$0.875
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 12 providersOfficial Alibaba API
Limits
Context window64,000 tokens131,072 tokens (best)131,072 tokens (best)
Max output16,384 tokens32,768 tokens (best)32,768 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsNoNoNo
AudioNoNoNo
VideoYesNoNo
ReasoningYesNoNo
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenOpenOpen
API model IDglm-4.5v—qwen3-next-80b-a3b-instruct
API providers111213 (best)
ReleasedAug 11, 2025Sep 23, 2025Sep 2025
Knowledge cutoffApr 2025Mar 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.

  • GLM-4.5V$9.60
  • Qwen3 VL 235B A22B Instruct$6.10
  • Qwen3-Next 80B-A3B Instruct$9.00
04 — Questions

Which should you choose?

Which is better: GLM-4.5V, Qwen3 VL 235B A22B Instruct or Qwen3-Next 80B-A3B Instruct?

Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against GLM-4.5V (49) and Qwen3-Next 80B-A3B Instruct (39). It leads on price. GLM-4.5V wins on inputs & features. 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-4.5V, Qwen3 VL 235B A22B Instruct or Qwen3-Next 80B-A3B Instruct?

Qwen3 VL 235B A22B Instruct is cheaper at $0.30 input / $1.55 output per million tokens (median across 12 API providers). Qwen3-Next 80B-A3B Instruct costs $0.50 input / $2.00 output per million tokens (official Alibaba API price); 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 $0.613 per million tokens for Qwen3 VL 235B A22B Instruct versus $0.875 for Qwen3-Next 80B-A3B Instruct (1.4× as much) and $0.90 for GLM-4.5V (1.5× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Qwen3 VL 235B A22B Instruct has not been scored yet and Qwen3-Next 80B-A3B Instruct has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.5V, Qwen3 VL 235B A22B Instruct and Qwen3-Next 80B-A3B Instruct 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 VL 235B A22B Instruct and Qwen3-Next 80B-A3B Instruct have the largest context windows (131,072 and 131,072 tokens), against 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Qwen3 VL 235B A22B Instruct up to 32,768, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.

Which can read images, PDFs, audio or video?

GLM-4.5V accepts text, images and video; Qwen3 VL 235B A22B Instruct accepts text and images; Qwen3-Next 80B-A3B Instruct 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?

Qwen3 VL 235B A22B Instruct is the newest, released Sep 23, 2025. Qwen3-Next 80B-A3B Instruct came out Sep 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3 VL 235B A22B Instruct Mar 31, 2025, Qwen3-Next 80B-A3B Instruct 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.