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DeepSeek-V4.1-Flash vs Qwen3 VL 32B Thinking

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 23.6.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 23.6, ranking #12 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; 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 DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 2026

Choose Qwen3 VL 32B Thinking

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.8
#12
23.6
#173
Reasoning
48.9
#17
24.7
#153
Agents
41.3
#4
13.0
#96
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash
Qwen3 VL 32B Thinking
Math
35.2#43
25.4#111
Vision
29.5#31
16.9#85
Multimodal
34.3#13
19.5#72
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 47 for Qwen3 VL 32B Thinking

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Qwen3 VL 32B Thinking is better at 0 benchmarks.

DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.

Fri Sep 11 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

730.2B diff

DeepSeek-V4.1-Flash has 730.2B more parameters than Qwen3 VL 32B Thinking, making it 2212.7% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
763.2B
DeepSeek-V4.1-Flash
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash and Qwen3 VL 32B Thinking support multimodal inputs.

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

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Qwen3 VL 32B Thinking uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Qwen3 VL 32B Thinking was released on 2025-09-22.

DeepSeek-V4.1-Flash is 12 months newer than Qwen3 VL 32B Thinking.

DeepSeek-V4.1-Flash

Sep 10, 2026

-1 days ago

11mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

11 months 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 DeepSeek-V4.1-Flash and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Qwen3 VL 32B Thinking.

Which is better, DeepSeek-V4.1-Flash or Qwen3 VL 32B Thinking?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 23.6. DeepSeek-V4.1-Flash is made by DeepSeek and Qwen3 VL 32B 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 DeepSeek-V4.1-Flash compare to Qwen3 VL 32B Thinking in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for DeepSeek-V4.1-Flash and Qwen3 VL 32B Thinking?

DeepSeek-V4.1-Flash supports 1.0M tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash and Qwen3 VL 32B Thinking?

Key differences include LLM Stats Score (51.8 vs 23.6), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Qwen3 VL 32B Thinking?

DeepSeek-V4.1-Flash is developed by DeepSeek and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

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