The AI arena is free today

Open Superagent

Qwen3 VL 32B Instruct vs Qwen3 VL 4B Thinking

Qwen3 VL 32B Instruct leads the LLM Stats Score 20.3 to 12.9.

Alibaba Cloud / Qwen Team · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 32B Instruct leads the overall LLM Stats Score 20.3 to 12.9, ranking #201 overall.

In the 45 individual benchmarks reported for both models, Qwen3 VL 32B Instruct wins 38; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Qwen3 VL 32B Instruct

  • overall performance matters — it scores 20.3 and ranks #201 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 38 of 45 exact shared results

Choose Qwen3 VL 4B Thinking

  • you want predictable pricing at $0.10/M input and $1.00/M output

At a glance

The differences that matter most.

Qwen3 VL 32B Instruct
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
20.3
#201
12.9
#255
Reasoning
19.2
#207
14.0
#237
Agents
9.5
#128
6.6
#147
Cost, coverage & limits
Benchmark wins
38 of 45
7 of 45
Input price
— / M
$0.10 / M
Output price
— / M
$1.00 / M
Context window
—
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

6 shared
Index
Qwen3 VL 32B Instruct
Qwen3 VL 4B Thinking
Math
19.5#168
15.6#217
Vision
14.9#95
7.7#144
Writing
11.4#67
11.0#72
Multimodal
17.4#82
10.8#118
Finance
21.7#75
16.5#112
1 more shared indexes
Legal
20.7#66
15.9#99
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

45 reported for Qwen3 VL 32B Instruct · 48 for Qwen3 VL 4B Thinking

45 shared

Qwen3 VL 32B Instruct outperforms in 38 benchmarks (AI2D, Arena-Hard v2, BFCL-v3, BLINK, CC-OCR, CharadesSTA, CharXiv-D, CharXiv-R, Creative Writing v3, DocVQAtest, ERQA, GPQA, IFEval, Include, InfoVQAtest, LiveBench 20241125, LVBench, MathVision, MathVista-Mini, MLVU-M, MMLU, MMLU-Pro, MMLU-ProX, MMLU-Redux, MM-MT-Bench, MMMU-Pro, MMMU (val), MMStar, MVBench, OCRBench, OCRBench-V2 (en), OCRBench-V2 (zh), ODinW, OSWorld, RealWorldQA, ScreenSpot, ScreenSpot Pro, SuperGPQA), while Qwen3 VL 4B Thinking is better at 7 benchmarks (AIME 2025, Hallusion Bench, LiveCodeBench v6, MuirBench, Multi-IF, PolyMATH, WritingBench).

Qwen3 VL 32B Instruct significantly outperforms across most benchmarks.

Wed Sep 23 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

29.0B diff

Qwen3 VL 32B Instruct has 29.0B more parameters than Qwen3 VL 4B Thinking, making it 725.0% larger.

Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct
33.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
33.0B
Qwen3 VL 32B Instruct
4.0B
Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).

Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Wed Sep 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Qwen3 VL 32B Instruct and Qwen3 VL 4B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3 VL 32B Instruct

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3 VL 32B Instruct

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Both models were released on 2025-09-22.

They likely represent similar generations of model development.

Qwen3 VL 32B Instruct

Sep 22, 2025

1.0 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

1.0 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Qwen3 VL 32B Instruct and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.

Qwen3 VL 32B Instruct
✓ Preferred
Qwen3 VL 4B Thinking
Open in Playground

FAQ

Common questions about Qwen3 VL 32B Instruct vs Qwen3 VL 4B Thinking.

Which is better, Qwen3 VL 32B Instruct or Qwen3 VL 4B Thinking?

Qwen3 VL 32B Instruct leads the LLM Stats Score 20.3 to 12.9. Qwen3 VL 32B Instruct is made by Alibaba Cloud / Qwen Team and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Qwen3 VL 32B Instruct compare to Qwen3 VL 4B Thinking in benchmarks?

Qwen3 VL 32B Instruct scores DocVQAtest: 96.9%, ScreenSpot: 95.8%, CharXiv-D: 90.5%, MMLU-Redux: 89.8%, AI2D: 89.5%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

What are the context window sizes for Qwen3 VL 32B Instruct and Qwen3 VL 4B Thinking?

Qwen3 VL 32B Instruct supports an unknown number of tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Qwen3 VL 32B Instruct and Qwen3 VL 4B Thinking?

Key differences include LLM Stats Score (20.3 vs 12.9). See the full comparison above for benchmark-by-benchmark results.

Related comparisons