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DeepSeek-V3.2-Speciale vs MAI-Thinking-1

DeepSeek-V3.2-Speciale and MAI-Thinking-1 are closely matched at 33.5 and 32.6 on the LLM Stats Score.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek-V3.2-Speciale and MAI-Thinking-1 are closely matched on the overall LLM Stats Score at 33.5 and 32.6.

In the 3 individual benchmarks reported for both models, MAI-Thinking-1 wins 2; 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-V3.2-Speciale

  • you need open weights you can self-host or fine-tune

Choose MAI-Thinking-1

  • you value its reported benchmark strengths — it wins 2 of 3 exact shared results
  • you want the most recent training data — it shipped Jun 2026

At a glance

The differences that matter most.

DeepSeek-V3.2-Speciale
DeepSeek
MAI-Thinking-1
Microsoft
Core performance indexes
LLM Stats Score
33.5
#118
32.6
#124
Reasoning
32.1
#125
33.5
#114
Coding
18.5
#131
19.2
#123
Agents
10.8
#136
12.8
#120
Cost, coverage & limits
Benchmark wins
1 of 3
2 of 3
Input price
$0.28 / M
— / M
Output price
$0.42 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V3.2-Speciale
MAI-Thinking-1
Math
34.1#51
33.6#55
Tool use
10.2#141
10.6#139
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

8 reported for DeepSeek-V3.2-Speciale · 23 for MAI-Thinking-1

3 shared

DeepSeek-V3.2-Speciale outperforms in 1 benchmarks (Terminal-Bench 2.0), while MAI-Thinking-1 is better at 2 benchmarks (AIME 2025, SWE-Bench Verified).

MAI-Thinking-1 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

315.0B diff

MAI-Thinking-1 has 315.0B more parameters than DeepSeek-V3.2-Speciale, making it 46.0% larger.

DeepSeek
DeepSeek-V3.2-Speciale
685.0Bparameters
Microsoft
MAI-Thinking-1
1.0Tparameters
685.0B
DeepSeek-V3.2-Speciale
1000.0B
MAI-Thinking-1

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Speciale specifies input context (131,072 tokens). Only DeepSeek-V3.2-Speciale specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Fri Oct 23 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Speciale 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-V3.2-Speciale

MIT

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2-Speciale was released on 2025-12-01, while MAI-Thinking-1 was released on 2026-06-02.

MAI-Thinking-1 is 6 months newer than DeepSeek-V3.2-Speciale.

DeepSeek-V3.2-Speciale

Dec 1, 2025

10 months ago

MAI-Thinking-1

Jun 2, 2026

4 months ago

6mo 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?

Judge for yourself.

Run your own prompts against DeepSeek-V3.2-Speciale and MAI-Thinking-1 side-by-side, then vote on the output you prefer.

DeepSeek-V3.2-Speciale
✓ Preferred
MAI-Thinking-1
Open in Playground

FAQ

Common questions about DeepSeek-V3.2-Speciale vs MAI-Thinking-1.

Which is better, DeepSeek-V3.2-Speciale or MAI-Thinking-1?

DeepSeek-V3.2-Speciale and MAI-Thinking-1 are closely matched on the LLM Stats Score at 33.5 and 32.6. DeepSeek-V3.2-Speciale 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-V3.2-Speciale compare to MAI-Thinking-1 in benchmarks?

DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%. 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-V3.2-Speciale and MAI-Thinking-1?

DeepSeek-V3.2-Speciale supports 131K 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-V3.2-Speciale and MAI-Thinking-1?

Key differences include LLM Stats Score (33.5 vs 32.6), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2-Speciale and MAI-Thinking-1?

DeepSeek-V3.2-Speciale is developed by DeepSeek and MAI-Thinking-1 is developed by Microsoft.

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