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DeepSeek-V4.1-Flash-1017 vs Qwen3.8-Flash-Next

DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next are closely matched at 51.2 and 48.1 on the LLM Stats Score.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next are closely matched on the overall LLM Stats Score at 51.2 and 48.1.

In the 4 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 3; 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-1017

  • your work emphasizes agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 4 exact shared results
  • you want the most recent training data — it shipped Oct 2026

Choose Qwen3.8-Flash-Next

  • 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
Qwen3.8-Flash-Next
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.2
#15
48.1
#31
Reasoning
48.8
#18
48.5
#20
Coding
39.3
#14
33.2
#42
Agents
38.7
#8
32.8
#35
Cost, coverage & limits
Benchmark wins
3 of 4
1 of 4
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

4 shared
Index
DeepSeek-V4.1-Flash-1017
Qwen3.8-Flash-Next
Math
31.8#69
32.8#62
Vision
32.3#25
34.0#18
Tool use
31.3#6
30.0#11
Multimodal
35.0#14
35.3#12
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 22 for Qwen3.8-Flash-Next

4 shared

DeepSeek-V4.1-Flash-1017 outperforms in 3 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo), while Qwen3.8-Flash-Next is better at 1 benchmark (Agents' Last Exam).

DeepSeek-V4.1-Flash-1017 shows notably better performance in the majority of benchmarks.

Fri Oct 23 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

638.2B diff

DeepSeek-V4.1-Flash-1017 has 638.2B more parameters than Qwen3.8-Flash-Next, making it 510.6% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
125.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
125.0B
Qwen3.8-Flash-Next

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
Qwen3.8-Flash-Next
Input- tokens
Output- tokens
Fri Oct 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next 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

Qwen3.8-Flash-Next

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.

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

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Qwen3.8-Flash-Next

Qwen Community License 1.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Qwen3.8-Flash-Next was released on 2026-08-26.

DeepSeek-V4.1-Flash-1017 is 2 months newer than Qwen3.8-Flash-Next.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1mo newer
Qwen3.8-Flash-Next

Aug 26, 2026

1 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?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Qwen3.8-Flash-Next
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen3.8-Flash-Next.

Which is better, DeepSeek-V4.1-Flash-1017 or Qwen3.8-Flash-Next?

DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next are closely matched on the LLM Stats Score at 51.2 and 48.1. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Qwen3.8-Flash-Next 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 Qwen3.8-Flash-Next 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%. Qwen3.8-Flash-Next scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Qwen3.8-Flash-Next 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 Qwen3.8-Flash-Next?

Key differences include LLM Stats Score (51.2 vs 48.1), licensing (MIT vs Qwen Community License 1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Qwen3.8-Flash-Next?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Qwen3.8-Flash-Next is developed by Alibaba Cloud / Qwen Team.

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