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DeepSeek-V4.1-Flash-1017 vs Qwen2 72B Instruct

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 5.5.

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 5.5, 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 coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Qwen2 72B 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 72B Instruct
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.0
#15
5.5
#318
Reasoning
48.6
#18
5.3
#314
Coding
39.1
#15
9.7
#189
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

1 shared
Index
DeepSeek-V4.1-Flash-1017
Qwen2 72B Instruct
Math
31.8#67
11.0#258
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 17 for Qwen2 72B Instruct

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Qwen2 72B 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

691.2B diff

DeepSeek-V4.1-Flash-1017 has 691.2B more parameters than Qwen2 72B Instruct, making it 960.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen2 72B Instruct
72.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
72.0B
Qwen2 72B 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 72B Instruct
Input- tokens
Output- tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Qwen2 72B Instruct does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Qwen2 72B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen2 72B Instruct uses tongyi-qianwen.

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 72B Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Qwen2 72B Instruct was released on 2024-07-23.

DeepSeek-V4.1-Flash-1017 is 27 months newer than Qwen2 72B Instruct.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

2.2yr newer
Qwen2 72B Instruct

Jul 23, 2024

2.2 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 72B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Qwen2 72B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen2 72B Instruct.

Which is better, DeepSeek-V4.1-Flash-1017 or Qwen2 72B Instruct?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 5.5. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Qwen2 72B 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 72B 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 72B Instruct scores GSM8k: 91.1%, CMMLU: 90.1%, HellaSwag: 87.6%, HumanEval: 86.0%, Winogrande: 85.1%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Qwen2 72B Instruct?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Qwen2 72B 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 72B Instruct?

Key differences include LLM Stats Score (51.0 vs 5.5), multimodal support (yes vs no), licensing (MIT vs tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Qwen2 72B Instruct?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Qwen2 72B Instruct is developed by Alibaba Cloud / Qwen Team.

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