ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs GLM-4.6V-Flash

GLM-4.6V-Flash comes out ahead, 63 to 42 on our weighted score, and it is the cheaper option too.

  1. 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
  2. Our pick

    Z.ai (Zhipu)

    GLM-4.6V-Flash

    Released Dec 8, 2025

    63/100
    • ECI—
    • Price$0.161 / $0.559
    • Context128K
    Full specs Remove GLM-4.6V-Flash

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

GLM-4.6V-Flash is our pick

GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 235B-A22B Thinking 2507 (42). It leads on price and inputs & features. 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Qwen3 235B-A22B Thinking 2507 $0.95 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.6V-Flash 128,000 tokens
  • Widest inputsGLM-4.6V-FlashQwen3 235B-A22B Thinking 2507: Text · GLM-4.6V-Flash: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightQwen3 235B-A22B Thinking 2507GLM-4.6V-Flash
Price50%4974
Inputs & features30%3570
Context window20%3724
Overall100%42/10063/100

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 235B-A22B Thinking 2507 vs GLM-4.6V-Flash specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)GLM-4.6V-FlashZ.ai (Zhipu)
Capability
Capabilities Index (ECI)143.8—
ECI rank#93 of 165—
GPQA DiamondGraduate-level science questions80.1%—
OTIS Mock AIME 2024–2025Competition mathematics86.7%—
SimpleQA VerifiedShort factual questions40.4%—
Price per million tokens
Input$0.30$0.161 (best)
Output$2.90$0.559 (best)
Cached input——
Blended (3:1)$0.95$0.261 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 2 providers
Limits
Context window262,144 tokens (best)128,000 tokens
Max output81,920 tokens (best)32,768 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoNo
VideoNoYes
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenApache 2.0Open
API model ID—glm-4.6v-flash
API providers12 (best)6
ReleasedJul 25, 2025Dec 8, 2025
Knowledge cutoff——
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 235B-A22B Thinking 2507$8.80
  • GLM-4.6V-Flash$2.73
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507 or GLM-4.6V-Flash?

GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 235B-A22B Thinking 2507 (42). It leads on price and inputs & features. 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, Qwen3 235B-A22B Thinking 2507 or GLM-4.6V-Flash?

GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $0.95 for Qwen3 235B-A22B Thinking 2507 (3.6× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and GLM-4.6V-Flash has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507 and GLM-4.6V-Flash 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?

Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V-Flash. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, GLM-4.6V-Flash up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; GLM-4.6V-Flash accepts text, images and video. GLM-4.6V-Flash handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights (Apache 2.0), so you can self-host them.

Which is newer?

GLM-4.6V-Flash is the newest, released Dec 8, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 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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