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DeepSeek-V4.1-Flash-1017 vs MAI-Thinking-1

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 32.8.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 32.8, 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
  • you need open weights you can self-host or fine-tune

Choose MAI-Thinking-1

  • you are already invested in the Microsoft ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
MAI-Thinking-1
Microsoft
Core performance indexes
LLM Stats Score
51.0
#15
32.8
#122
Reasoning
48.6
#18
33.6
#112
Coding
39.1
#15
19.4
#121
Agents
38.6
#9
12.9
#117
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

2 shared
Index
DeepSeek-V4.1-Flash-1017
MAI-Thinking-1
Math
31.8#67
33.7#54
Tool use
30.9#7
10.7#136
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 23 for MAI-Thinking-1

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and MAI-Thinking-1don'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

236.8B diff

MAI-Thinking-1 has 236.8B more parameters than DeepSeek-V4.1-Flash-1017, making it 31.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Microsoft
MAI-Thinking-1
1.0Tparameters
763.2B
DeepSeek-V4.1-Flash-1017
1000.0B
MAI-Thinking-1

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
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas MAI-Thinking-1 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

MAI-Thinking-1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while MAI-Thinking-1 uses a proprietary license.

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

DeepSeek-V4.1-Flash-1017

MIT

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while MAI-Thinking-1 was released on 2026-06-02.

DeepSeek-V4.1-Flash-1017 is 5 months newer than MAI-Thinking-1.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

0 days ago

4mo newer
MAI-Thinking-1

Jun 2, 2026

4 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 MAI-Thinking-1 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
MAI-Thinking-1
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs MAI-Thinking-1.

Which is better, DeepSeek-V4.1-Flash-1017 or MAI-Thinking-1?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 32.8. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and MAI-Thinking-1 is made by Microsoft. 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 MAI-Thinking-1 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%. MAI-Thinking-1 scores LongFact: 98.0%, AIME 2025: 97.0%, AIME 2026: 94.5%, GraphWalks: 90.0%, AIR-Bench: 88.0%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and MAI-Thinking-1?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and MAI-Thinking-1 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 MAI-Thinking-1?

Key differences include LLM Stats Score (51.0 vs 32.8), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and MAI-Thinking-1?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and MAI-Thinking-1 is developed by Microsoft.

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