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Comparison · 3 models · Updated Nov 2, 2026

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

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. 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

  2. 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
  3. 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
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 InstructGLM-4.7: Text · Qwen3 235B-A22B Thinking 2507: Text · Qwen3 VL 30B A3B Instruct: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7Qwen3 235B-A22B Thinking 2507Qwen3 VL 30B A3B Instruct
Price50%484974
Inputs & features30%353560
Context window20%323737
Overall100%41/10042/10062/100

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-4.7 vs Qwen3 235B-A22B Thinking 2507 vs Qwen3 VL 30B A3B Instruct specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Qwen3 235B-A22B Thinking 2507Alibaba (Qwen)Qwen3 VL 30B A3B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)143.7143.8 (best)—
ECI rank#94 of 165#93 of 165 (best)—
GPQA DiamondGraduate-level science questions83.3% (best)80.1%—
OTIS Mock AIME 2024–2025Competition mathematics83.3%86.7% (best)—
SimpleQA VerifiedShort factual questions32.2%40.4% (best)—
Price per million tokens
Input$0.60$0.30$0.15 (best)
Output$2.20$2.90$0.60 (best)
Cached input$0.11——
Blended (3:1)$1.00$0.95$0.263 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 11 providersMedian of 13 providers
Limits
Context window204,800 tokens262,144 tokens (best)262,144 tokens (best)
Max output131,072 tokens (best)81,920 tokens32,768 tokens
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenApache 2.0OpenApache-2.0
API model IDglm-4.7——
API providers31 (best)1213
ReleasedDec 22, 2025Jul 25, 2025Oct 4, 2025
Knowledge cutoffApr 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-4.7$10.40
  • Qwen3 235B-A22B Thinking 2507$8.80
  • Qwen3 VL 30B A3B Instruct$2.70
04 — Questions

Which should you choose?

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

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

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. GLM-4.7 has an ECI of 143.7, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and Qwen3 VL 30B A3B Instruct has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Qwen3 235B-A22B Thinking 2507 and Qwen3 VL 30B 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 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: GLM-4.7 up to 131,072, Qwen3 235B-A22B Thinking 2507 up to 81,920, Qwen3 VL 30B A3B Instruct up to 32,768 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Qwen3 235B-A22B Thinking 2507 accepts text; Qwen3 VL 30B A3B Instruct accepts text, images and video. 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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