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DeepSeek-V4.1-Flash vs Qwen2.5 VL 32B Instruct

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 9.7.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 9.7, 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 coding — 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 Qwen2.5 VL 32B Instruct

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

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Qwen2.5 VL 32B Instruct
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.8
#12
9.7
#267
Reasoning
48.9
#17
8.4
#271
Coding
44.4
#5
12.4
#147
Agents
41.3
#4
-3.1
#176
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
Qwen2.5 VL 32B Instruct
Math
35.2#43
11.5#239
Vision
29.5#31
6.3#146
Multimodal
34.3#13
9.1#119
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 28 for Qwen2.5 VL 32B Instruct

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Qwen2.5 VL 32B Instruct 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

729.7B diff

DeepSeek-V4.1-Flash has 729.7B more parameters than Qwen2.5 VL 32B Instruct, making it 2178.2% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 VL 32B Instruct
33.5Bparameters
763.2B
DeepSeek-V4.1-Flash
33.5B
Qwen2.5 VL 32B Instruct

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
Qwen2.5 VL 32B Instruct
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 Qwen2.5 VL 32B Instruct 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

Qwen2.5 VL 32B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Qwen2.5 VL 32B Instruct 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

Qwen2.5 VL 32B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Qwen2.5 VL 32B Instruct was released on 2025-02-28.

DeepSeek-V4.1-Flash is 19 months newer than Qwen2.5 VL 32B Instruct.

DeepSeek-V4.1-Flash

Sep 10, 2026

-1 days ago

1.5yr newer
Qwen2.5 VL 32B Instruct

Feb 28, 2025

1.5 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 DeepSeek-V4.1-Flash and Qwen2.5 VL 32B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Qwen2.5 VL 32B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Qwen2.5 VL 32B Instruct.

Which is better, DeepSeek-V4.1-Flash or Qwen2.5 VL 32B Instruct?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 9.7. DeepSeek-V4.1-Flash is made by DeepSeek and Qwen2.5 VL 32B Instruct 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 Qwen2.5 VL 32B Instruct 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%. Qwen2.5 VL 32B Instruct scores DocVQA: 94.8%, Android Control Low_EM: 93.3%, HumanEval: 91.5%, ScreenSpot: 88.5%, MBPP: 84.0%.

What are the context window sizes for DeepSeek-V4.1-Flash and Qwen2.5 VL 32B Instruct?

DeepSeek-V4.1-Flash supports 1.0M tokens and Qwen2.5 VL 32B Instruct 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 Qwen2.5 VL 32B Instruct?

Key differences include LLM Stats Score (51.8 vs 9.7), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Qwen2.5 VL 32B Instruct?

DeepSeek-V4.1-Flash is developed by DeepSeek and Qwen2.5 VL 32B Instruct is developed by Alibaba Cloud / Qwen Team.

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