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Comparison · 3 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs Qwen3 VL 30B A3B Instruct vs GLM-4.7

Qwen3 VL 30B A3B Instruct comes out ahead, 62 to 42 and 41 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

    Alibaba (Qwen)

    Qwen3 VL 30B A3B Instruct

    Released Oct 4, 2025

    62/100
    • ECI—
    • Price$0.15 / $0.60
    • Context262K
    Full specs Remove Qwen3 VL 30B A3B Instruct

  3. 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
01 — Verdict

Qwen3 VL 30B A3B Instruct is our pick

Qwen3 VL 30B A3B Instruct is the better all-round choice, scoring 62/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price and 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 30B A3B InstructQwen3 VL 30B A3B Instruct $0.263 · Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507 and Qwen3 VL 30B A3B InstructQwen3 235B-A22B Thinking 2507 262,144 · Qwen3 VL 30B A3B Instruct 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsQwen3 VL 30B A3B InstructQwen3 235B-A22B Thinking 2507: Text · Qwen3 VL 30B A3B Instruct: Text, Images, Video · GLM-4.7: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightQwen3 235B-A22B Thinking 2507Qwen3 VL 30B A3B InstructGLM-4.7
Price50%497448
Inputs & features30%356035
Context window20%373732
Overall100%42/10062/10041/100

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 Qwen3 VL 30B A3B Instruct vs GLM-4.7 specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)Qwen3 VL 30B A3B InstructAlibaba (Qwen)GLM-4.7Z.ai (Zhipu)
Capability
Capabilities Index (ECI)143.8 (best)—143.7
ECI rank#93 of 165 (best)—#94 of 165
GPQA DiamondGraduate-level science questions80.1%—83.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics86.7% (best)—83.3%
SimpleQA VerifiedShort factual questions40.4% (best)—32.2%
Price per million tokens
Input$0.30$0.15 (best)$0.60
Output$2.90$0.60 (best)$2.20
Cached input——$0.11
Blended (3:1)$0.95$0.263 (best)$1.00
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 13 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)262,144 tokens (best)204,800 tokens
Max output81,920 tokens32,768 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningYesNoYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0OpenApache-2.0Open
API model ID——glm-4.7
API providers121331 (best)
ReleasedJul 25, 2025Oct 4, 2025Dec 22, 2025
Knowledge cutoff——Apr 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 235B-A22B Thinking 2507$8.80
  • Qwen3 VL 30B A3B Instruct$2.70
  • GLM-4.7$10.40
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507, Qwen3 VL 30B A3B Instruct or GLM-4.7?

Qwen3 VL 30B A3B Instruct is the better all-round choice, scoring 62/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price and 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, Qwen3 235B-A22B Thinking 2507, Qwen3 VL 30B A3B Instruct or GLM-4.7?

Qwen3 VL 30B A3B Instruct is cheaper at $0.15 input / $0.60 output per million tokens (median across 13 API providers). 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 $0.263 per million tokens for Qwen3 VL 30B A3B Instruct versus $0.95 for Qwen3 235B-A22B Thinking 2507 (3.6× as much) and $1.00 for GLM-4.7 (3.8× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8, Qwen3 VL 30B A3B Instruct has not been scored yet and GLM-4.7 has an ECI of 143.7.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, Qwen3 VL 30B A3B Instruct and GLM-4.7 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 and Qwen3 VL 30B A3B Instruct have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Qwen3 VL 30B A3B Instruct up to 32,768, GLM-4.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; Qwen3 VL 30B A3B Instruct accepts text, images and video; GLM-4.7 accepts text. Qwen3 VL 30B A3B Instruct handles the widest range of inputs.

Are any of these open source?

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

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 VL 30B A3B Instruct came out Oct 4, 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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