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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 33.0.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 33.0, ranking #12 overall.

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

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 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
DeepSeek
MAI-Thinking-1
Microsoft
Core performance indexes
LLM Stats Score
51.8
#12
33.0
#101
Reasoning
48.9
#17
33.8
#92
Coding
44.4
#5
19.4
#104
Agents
41.3
#4
12.8
#97
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V4.1-Flash
MAI-Thinking-1
Math
35.2#43
33.7#53
Tool use
35.1#2
10.7#119
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

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

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while MAI-Thinking-1 is better at 0 benchmarks.

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

Sat Sep 12 2026 • llm-stats.com

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, making it 31.0% larger.

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

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Sat Sep 12 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas MAI-Thinking-1 does not.

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

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

MAI-Thinking-1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash 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

MIT

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while MAI-Thinking-1 was released on 2026-06-02.

DeepSeek-V4.1-Flash is 3 months newer than MAI-Thinking-1.

DeepSeek-V4.1-Flash

Sep 10, 2026

2 days ago

3mo newer
MAI-Thinking-1

Jun 2, 2026

3 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?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash and MAI-Thinking-1 side-by-side, then vote on the output you prefer.

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

FAQ

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

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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 33.0. DeepSeek-V4.1-Flash 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 compare to MAI-Thinking-1 in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. 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 and MAI-Thinking-1?

DeepSeek-V4.1-Flash 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 and MAI-Thinking-1?

Key differences include LLM Stats Score (51.8 vs 33.0), 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 and MAI-Thinking-1?

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

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