Model Comparison

Qwen2.5 32B Instruct vs Qwen3 VL 8B Thinking

Qwen3 VL 8B Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Qwen2.5 32B Instruct outperforms in 0 benchmarks, while Qwen3 VL 8B Thinking is better at 4 benchmarks (GPQA, MMLU, MMLU-Pro, MMLU-Redux).

Qwen3 VL 8B Thinking significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

23.5B diff

Qwen2.5 32B Instruct has 23.5B more parameters than Qwen3 VL 8B Thinking, making it 261.1% larger.

Qwen2.5 32B Instruct

32.5Bparameters

Qwen3 VL 8B Thinking

9.0Bparameters

32.5B

Qwen2.5 32B Instruct

9.0B

Qwen3 VL 8B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 8B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 8B Thinking specifies output context (262,144 tokens).

Qwen2.5 32B Instruct

Input- tokens

Output- tokens

Qwen3 VL 8B Thinking

Input262,144 tokens

Output262,144 tokens

Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 8B Thinking supports multimodal inputs, whereas Qwen2.5 32B Instruct does not.

Qwen3 VL 8B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen2.5 32B Instruct

Text

Images

Audio

Video

Qwen3 VL 8B 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.

Qwen2.5 32B Instruct

Apache 2.0

Open weights

Qwen3 VL 8B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen2.5 32B Instruct was released on 2024-09-19, while Qwen3 VL 8B Thinking was released on 2025-09-22.

Qwen3 VL 8B Thinking is 12 months newer than Qwen2.5 32B Instruct.

Qwen2.5 32B Instruct

Sep 19, 2024

1.6 years ago

Qwen3 VL 8B Thinking

Sep 22, 2025

7 months ago

1.0yr newer

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→

Key Takeaways

Qwen2.5 32B Instruct

View details

Alibaba Cloud / Qwen Team

No standout differentiators in the data we have for this pair.

Qwen3 VL 8B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Higher GPQA score (69.9% vs 49.5%)

Higher MMLU score (85.2% vs 83.3%)

Higher MMLU-Pro score (77.3% vs 69.0%)

Higher MMLU-Redux score (88.8% vs 83.9%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen2.5 32B Instruct	Qwen3 VL 8B Thinking

FAQ

Common questions about Qwen2.5 32B Instruct vs Qwen3 VL 8B Thinking.

Which is better, Qwen2.5 32B Instruct or Qwen3 VL 8B Thinking?

Qwen3 VL 8B Thinking significantly outperforms across most benchmarks. Qwen2.5 32B Instruct is made by Alibaba Cloud / Qwen Team and Qwen3 VL 8B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Qwen2.5 32B Instruct compare to Qwen3 VL 8B Thinking in benchmarks?

Qwen2.5 32B Instruct scores GSM8k: 95.9%, HumanEval: 88.4%, HellaSwag: 85.2%, BBH: 84.5%, MBPP: 84.0%. Qwen3 VL 8B Thinking scores DocVQAtest: 95.3%, ScreenSpot: 93.6%, MMLU-Redux: 88.8%, MMBench-V1.1: 87.5%, InfoVQAtest: 86.0%.

What are the context window sizes for Qwen2.5 32B Instruct and Qwen3 VL 8B Thinking?

Qwen2.5 32B Instruct supports an unknown number of tokens and Qwen3 VL 8B 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 Qwen2.5 32B Instruct and Qwen3 VL 8B Thinking?

Key differences include multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.