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Comparison · 3 models · Updated Nov 2, 2026

Qwen3 VL 30B A3B Instruct vs GLM-4.7 vs Grok 4.1 Fast

Grok 4.1 Fast comes out ahead, 66 to 62 and 41 on our weighted score.

  1. 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

  2. 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
  3. Our pick

    xAI

    Grok 4.1 Fast

    Released Nov 19, 2025

    66/100
    • ECI—
    • Price$0.20 / $0.50
    • Context2M
    Full specs Remove Grok 4.1 Fast
01 — Verdict

Grok 4.1 Fast is our pick

Grok 4.1 Fast is the better all-round choice, scoring 66/100 against Qwen3 VL 30B A3B Instruct (62) and GLM-4.7 (41). It leads on context window. Qwen3 VL 30B A3B Instruct 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 30B A3B InstructQwen3 VL 30B A3B Instruct $0.263 · Grok 4.1 Fast $0.275 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextGrok 4.1 FastGrok 4.1 Fast 2,000,000 · Qwen3 VL 30B A3B Instruct 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsQwen3 VL 30B A3B InstructQwen3 VL 30B A3B Instruct: Text, Images, Video · GLM-4.7: Text · Grok 4.1 Fast: Text, Images
  • Self-hostingQwen3 VL 30B A3B Instruct and GLM-4.7Publishes downloadable weights (Apache-2.0)
How the score is built
MeasureWeightQwen3 VL 30B A3B InstructGLM-4.7Grok 4.1 Fast
Price50%744873
Inputs & features30%603550
Context window20%373272
Overall100%62/10041/10066/100

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 VL 30B A3B Instruct vs GLM-4.7 vs Grok 4.1 Fast specifications side by side
SpecificationQwen3 VL 30B A3B InstructAlibaba (Qwen)GLM-4.7Z.ai (Zhipu)Grok 4.1 FastxAI
Capability
Capabilities Index (ECI)—143.7—
ECI rank—#94 of 165—
GPQA DiamondGraduate-level science questions—83.3%—
OTIS Mock AIME 2024–2025Competition mathematics—83.3%—
SimpleQA VerifiedShort factual questions—32.2%—
Price per million tokens
Input$0.15 (best)$0.60$0.20
Output$0.60$2.20$0.50 (best)
Cached input—$0.11—
Blended (3:1)$0.263 (best)$1.00$0.275
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 13 providersOfficial Z.AI APIMedian of 6 providers
Limits
Context window262,144 tokens204,800 tokens2,000,000 tokens (best)
Max output32,768 tokens131,072 tokens (best)30,000 tokens
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsNoNoNo
AudioNoNoNo
VideoYesNoNo
ReasoningNoYesNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenApache-2.0OpenProprietary
API model ID—glm-4.7—
API providers1331 (best)8
ReleasedOct 4, 2025Dec 22, 2025Nov 19, 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 VL 30B A3B Instruct$2.70
  • GLM-4.7$10.40
  • Grok 4.1 Fast$3.00
04 — Questions

Which should you choose?

Which is better: Qwen3 VL 30B A3B Instruct, GLM-4.7 or Grok 4.1 Fast?

Grok 4.1 Fast is the better all-round choice, scoring 66/100 against Qwen3 VL 30B A3B Instruct (62) and GLM-4.7 (41). It leads on context window. Qwen3 VL 30B A3B Instruct 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, Qwen3 VL 30B A3B Instruct, GLM-4.7 or Grok 4.1 Fast?

Qwen3 VL 30B A3B Instruct is cheaper at $0.15 input / $0.60 output per million tokens (median across 13 API providers). Grok 4.1 Fast costs $0.20 input / $0.50 output per million tokens (median across 6 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 $0.275 for Grok 4.1 Fast (1× 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 VL 30B A3B Instruct has not been scored yet, GLM-4.7 has an ECI of 143.7 and Grok 4.1 Fast has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 VL 30B A3B Instruct, GLM-4.7 and Grok 4.1 Fast 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?

Grok 4.1 Fast has the largest context window at 2,000,000 tokens, against 262,144 for Qwen3 VL 30B A3B Instruct and 204,800 for GLM-4.7. Maximum output per response: Qwen3 VL 30B A3B Instruct up to 32,768, GLM-4.7 up to 131,072, Grok 4.1 Fast up to 30,000 tokens.

Which can read images, PDFs, audio or video?

Qwen3 VL 30B A3B Instruct accepts text, images and video; GLM-4.7 accepts text; Grok 4.1 Fast accepts text and images. Qwen3 VL 30B A3B Instruct handles the widest range of inputs.

Are any of these open source?

Qwen3 VL 30B A3B Instruct and GLM-4.7 publishes its weights (Apache-2.0) and can be self-hosted; Grok 4.1 Fast is proprietary.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Grok 4.1 Fast came out Nov 19, 2025; Qwen3 VL 30B A3B Instruct came out Oct 4, 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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