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DeepSeek-V4.1-Flash-1017 vs Qwen2.5 VL 7B Instruct

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 6.2.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 6.2, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Qwen2.5 VL 7B Instruct

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

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Qwen2.5 VL 7B Instruct
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.0
#15
6.2
#316
Reasoning
48.6
#18
2.7
#326
Agents
38.6
#9
-3.2
#203
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash-1017
Qwen2.5 VL 7B Instruct
Math
31.8#67
3.2#300
Vision
32.2#26
4.2#178
Multimodal
35.0#14
6.3#151
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 32 for Qwen2.5 VL 7B Instruct

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Qwen2.5 VL 7B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

754.9B diff

DeepSeek-V4.1-Flash-1017 has 754.9B more parameters than Qwen2.5 VL 7B Instruct, making it 9106.3% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 VL 7B Instruct
8.3Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
8.3B
Qwen2.5 VL 7B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 VL 7B Instruct
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Qwen2.5 VL 7B Instruct support multimodal inputs.

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

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Qwen2.5 VL 7B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen2.5 VL 7B 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-1017

MIT

Open weights

Qwen2.5 VL 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Qwen2.5 VL 7B Instruct was released on 2025-01-26.

DeepSeek-V4.1-Flash-1017 is 21 months newer than Qwen2.5 VL 7B Instruct.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.7yr newer
Qwen2.5 VL 7B Instruct

Jan 26, 2025

1.7 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?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Qwen2.5 VL 7B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Qwen2.5 VL 7B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen2.5 VL 7B Instruct.

Which is better, DeepSeek-V4.1-Flash-1017 or Qwen2.5 VL 7B Instruct?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 6.2. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Qwen2.5 VL 7B 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-1017 compare to Qwen2.5 VL 7B Instruct in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Qwen2.5 VL 7B Instruct scores DocVQA: 95.7%, Android Control Low_EM: 91.4%, MobileMiniWob++_SR: 91.4%, ChartQA: 87.3%, OCRBench: 86.4%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Qwen2.5 VL 7B Instruct?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Qwen2.5 VL 7B 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-1017 and Qwen2.5 VL 7B Instruct?

Key differences include LLM Stats Score (51.0 vs 6.2), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Qwen2.5 VL 7B Instruct?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Qwen2.5 VL 7B Instruct is developed by Alibaba Cloud / Qwen Team.

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