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Comparison · 3 models · Updated Nov 2, 2026

Morph V3 Fast vs Qwen2.5-VL 72B Instruct vs GLM-4.5V

GLM-4.5V comes out ahead, 48 to 44 and 32 on our weighted score.

  1. Morph

    Morph V3 Fast

    Released Jul 7, 2025

    32/100
    • ECI—
    • Price$0.80 / $1.20
    • Context262K
  2. Alibaba (Qwen)

    Qwen2.5-VL 72B Instruct

    Released Jan 26, 2025

    44/100
    • ECI—
    • Price$0.905 / $1.00
    • Context131K
  3. Our pick

    Z.ai (Zhipu)

    GLM-4.5V

    Released Aug 11, 2025

    48/100
    • ECI—
    • Price$0.60 / $1.80
    • Context64K
01 — Verdict

GLM-4.5V is our pick

GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen2.5-VL 72B Instruct (44) and Morph V3 Fast (32). It leads on inputs & features. Morph V3 Fast 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 priceMorph V3 Fast and GLM-4.5VMorph V3 Fast $0.90 · GLM-4.5V $0.90 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend)
  • Longest contextMorph V3 FastMorph V3 Fast 262,144 · Qwen2.5-VL 72B Instruct 131,072 · GLM-4.5V 64,000 tokens
  • Widest inputsGLM-4.5VMorph V3 Fast: Text · Qwen2.5-VL 72B Instruct: Text, Images · GLM-4.5V: Text, Images, Video
  • Self-hostingQwen2.5-VL 72B Instruct and GLM-4.5VPublishes downloadable weights
How the score is built
MeasureWeightMorph V3 FastQwen2.5-VL 72B InstructGLM-4.5V
Price50%504950
Inputs & features30%05070
Context window20%372512
Overall100%32/10044/10048/100

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.

Morph V3 Fast vs Qwen2.5-VL 72B Instruct vs GLM-4.5V specifications side by side
SpecificationMorph V3 FastMorphQwen2.5-VL 72B InstructAlibaba (Qwen)GLM-4.5VZ.ai (Zhipu)
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.80$0.905$0.60 (best)
Output$1.20$1.00 (best)$1.80
Cached input——$0.11
Blended (3:1)$0.90 (best)$0.93$0.90 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 5 providersMedian of 6 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)131,072 tokens64,000 tokens
Max output131,072 tokens (best)8,192 tokens16,384 tokens
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningNoNoYes
Tool callingNoYesYes
Structured outputNoNoNo
Availability
WeightsProprietaryOpenOpen
API model ID——glm-4.5v
API providers5617 (best)
ReleasedJul 7, 2025Jan 26, 2025Aug 11, 2025
Knowledge cutoff—Apr 2024Apr 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.

  • Morph V3 Fast$10.40
  • Qwen2.5-VL 72B Instruct$11.06
  • GLM-4.5V$9.60
04 — Questions

Which should you choose?

Which is better: Morph V3 Fast, Qwen2.5-VL 72B Instruct or GLM-4.5V?

GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen2.5-VL 72B Instruct (44) and Morph V3 Fast (32). It leads on inputs & features. Morph V3 Fast 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, Morph V3 Fast, Qwen2.5-VL 72B Instruct or GLM-4.5V?

Morph V3 Fast is cheaper at $0.80 input / $1.20 output per million tokens (median across 5 API providers). GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price); Qwen2.5-VL 72B Instruct costs $0.905 input / $1.00 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for Morph V3 Fast versus $0.90 for GLM-4.5V (1× as much) and $0.93 for Qwen2.5-VL 72B Instruct (1× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Morph V3 Fast has not been scored yet, Qwen2.5-VL 72B Instruct has not been scored yet and GLM-4.5V has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Morph V3 Fast, Qwen2.5-VL 72B Instruct and GLM-4.5V yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Morph V3 Fast does not support tool calling, which most coding agents need.

Which has the bigger context window?

Morph V3 Fast has the largest context window at 262,144 tokens, against 131,072 for Qwen2.5-VL 72B Instruct and 64,000 for GLM-4.5V. Maximum output per response: Morph V3 Fast up to 131,072, Qwen2.5-VL 72B Instruct up to 8,192, GLM-4.5V up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Morph V3 Fast accepts text; Qwen2.5-VL 72B Instruct accepts text and images; GLM-4.5V accepts text, images and video. GLM-4.5V handles the widest range of inputs.

Are any of these open source?

Qwen2.5-VL 72B Instruct and GLM-4.5V publishes its weights and can be self-hosted; Morph V3 Fast is proprietary.

Which is newer?

GLM-4.5V is the newest, released Aug 11, 2025. Morph V3 Fast came out Jul 7, 2025; Qwen2.5-VL 72B Instruct came out Jan 26, 2025. Knowledge cutoff: Qwen2.5-VL 72B Instruct Apr 2024, 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 Nov 2, 2026.