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DeepSeek-V4.1-Flash-1017 vs Qwen3-235B-A22B-Thinking-2507

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 27.6. DeepSeek-V4.1-Flash-1017 is 1.9x cheaper per token.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 27.6, ranking #15 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, DeepSeek-V4.1-Flash-1017 is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

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 51.2 and ranks #15 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
  • cost matters — it's about 1.9x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Oct 2026

Choose Qwen3-235B-A22B-Thinking-2507

  • you want predictable pricing at $0.30/M input and $3.00/M output

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Qwen3-235B-A22B-Thinking-2507
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
51.2
#15
27.6
#161
Reasoning
48.8
#18
28.0
#153
Agents
38.7
#8
11.3
#132
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.30 / M
$0.30 / M
Output price
$1.20 / M
$3.00 / M
Context window
1,048,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash-1017
Qwen3-235B-A22B-Thinking-2507
Math
31.8#69
31.2#76
Vision
32.3#25
11.8#124
Tool use
31.3#6
10.2#142
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 25 for Qwen3-235B-A22B-Thinking-2507

1 shared

DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks.

DeepSeek-V4.1-Flash-1017 significantly outperforms across most benchmarks.

Sat Oct 24 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

DeepSeek-V4.1-Flash-1017 costs less

For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) costs the same as Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.5x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than DeepSeek-V4.1-Flash-1017.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sat Oct 24 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash-1017
Input tokens$0.30
Output tokens$1.20
Best providerDeepSeek
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input tokens$0.30
Output tokens$3.00
Best providerFireworks
Notice missing or incorrect data?

Model Size

Parameter count comparison

528.2B diff

DeepSeek-V4.1-Flash-1017 has 528.2B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 224.8% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
235.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
235.0B
Qwen3-235B-A22B-Thinking-2507

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Qwen3-235B-A22B-Thinking-2507 is limited to 131,072 tokens.

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Sat Oct 24 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 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

Qwen3-235B-A22B-Thinking-2507

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen3-235B-A22B-Thinking-2507 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

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

DeepSeek-V4.1-Flash-1017 is 15 months newer than Qwen3-235B-A22B-Thinking-2507.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

5 days ago

1.2yr newer
Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

1.2 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

Provider Availability

DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

DeepSeek-V4.1-Flash-1017

deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Qwen3-235B-A22B-Thinking-2507

fireworks logo
Fireworks
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Qwen3-235B-A22B-Thinking-2507
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen3-235B-A22B-Thinking-2507.

Which is better, DeepSeek-V4.1-Flash-1017 or Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 27.6. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Qwen3-235B-A22B-Thinking-2507 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 Qwen3-235B-A22B-Thinking-2507 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%. Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.

Is DeepSeek-V4.1-Flash-1017 cheaper than Qwen3-235B-A22B-Thinking-2507?

Both models cost $0.30 per million input tokens.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Qwen3-235B-A22B-Thinking-2507 supports 262K 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 Qwen3-235B-A22B-Thinking-2507?

Key differences include LLM Stats Score (51.2 vs 27.6), context window (1.0M vs 262K), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.

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