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

DeepSeek-V4-Flash-Max leads the LLM Stats Score 39.1 to 33.0.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V4-Flash-Max leads the overall LLM Stats Score 39.1 to 33.0, ranking #60 overall.

In the 6 individual benchmarks reported for both models, DeepSeek-V4-Flash-Max wins 5; 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-Flash-Max

  • overall performance matters — it scores 39.1 and ranks #60 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 5 of 6 exact shared results
  • you need open weights you can self-host or fine-tune

Choose MAI-Thinking-1

  • you want the most recent training data — it shipped Jun 2026

At a glance

The differences that matter most.

DeepSeek-V4-Flash-Max
DeepSeek
MAI-Thinking-1
Microsoft
Core performance indexes
LLM Stats Score
39.1
#60
33.0
#101
Reasoning
39.8
#55
33.8
#92
Coding
28.9
#55
19.4
#104
Agents
18.5
#67
12.8
#97
Cost, coverage & limits
Benchmark wins
5 of 6
1 of 6
Input price
$0.09 / M
— / M
Output price
$0.18 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4-Flash-Max
MAI-Thinking-1
Math
39.2#21
33.7#53
Tool use
17.3#73
10.7#119
Long context
11.5#65
20.4#30
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for DeepSeek-V4-Flash-Max · 23 for MAI-Thinking-1

6 shared

DeepSeek-V4-Flash-Max outperforms in 5 benchmarks (GPQA, HMMT Feb 26, MMLU-Pro, SWE-Bench Verified, Terminal-Bench 2.0), while MAI-Thinking-1 is better at 1 benchmark (SWE-Bench Pro).

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

Mon Sep 14 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

716.0B diff

MAI-Thinking-1 has 716.0B more parameters than DeepSeek-V4-Flash-Max, making it 252.1% larger.

DeepSeek
DeepSeek-V4-Flash-Max
284.0Bparameters
Microsoft
MAI-Thinking-1
1.0Tparameters
284.0B
DeepSeek-V4-Flash-Max
1000.0B
MAI-Thinking-1

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Flash-Max specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-Max specifies output context (1,048,576 tokens).

DeepSeek
DeepSeek-V4-Flash-Max
Input1,048,576 tokens
Output1,048,576 tokens
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Mon Sep 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V4-Flash-Max 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-Flash-Max

MIT

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Flash-Max was released on 2026-04-23, while MAI-Thinking-1 was released on 2026-06-02.

MAI-Thinking-1 is 1 month newer than DeepSeek-V4-Flash-Max.

DeepSeek-V4-Flash-Max

Apr 23, 2026

4 months ago

MAI-Thinking-1

Jun 2, 2026

3 months ago

1mo newer

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-Flash-Max and MAI-Thinking-1 side-by-side, then vote on the output you prefer.

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

FAQ

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

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

DeepSeek-V4-Flash-Max leads the LLM Stats Score 39.1 to 33.0. DeepSeek-V4-Flash-Max 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-Flash-Max compare to MAI-Thinking-1 in benchmarks?

DeepSeek-V4-Flash-Max scores CodeForces: 100.0%, HMMT Feb 26: 94.8%, LiveCodeBench: 91.6%, IMO-AnswerBench: 88.4%, GPQA: 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-Flash-Max and MAI-Thinking-1?

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

Key differences include LLM Stats Score (39.1 vs 33.0), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-Max and MAI-Thinking-1?

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

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