Model Comparison

DeepSeek-R1-0528 vs MiniMax M1 40K

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

8 benchmarks

DeepSeek-R1-0528 outperforms in 7 benchmarks (AIME 2024, AIME 2025, GPQA, Humanity's Last Exam, LiveCodeBench, MMLU-Pro, SimpleQA), while MiniMax M1 40K is better at 1 benchmark (SWE-Bench Verified).

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Fri May 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

215.0B diff

DeepSeek-R1-0528 has 215.0B more parameters than MiniMax M1 40K, making it 47.1% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
MiniMax
MiniMax M1 40K
456.0Bparameters
671.0B
DeepSeek-R1-0528
456.0B
MiniMax M1 40K

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
MiniMax
MiniMax M1 40K
Input- tokens
Output- tokens
Fri May 15 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-R1-0528

MIT

Open weights

MiniMax M1 40K

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while MiniMax M1 40K was released on 2025-06-16.

MiniMax M1 40K is 1 month newer than DeepSeek-R1-0528.

DeepSeek-R1-0528

May 28, 2025

11 months ago

MiniMax M1 40K

Jun 16, 2025

11 months ago

2w 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

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher AIME 2024 score (91.4% vs 83.3%)
Higher AIME 2025 score (87.5% vs 74.6%)
Higher GPQA score (81.0% vs 69.2%)
Higher Humanity's Last Exam score (17.7% vs 7.2%)
Higher LiveCodeBench score (73.3% vs 62.3%)
Higher MMLU-Pro score (85.0% vs 80.6%)
Higher SimpleQA score (92.3% vs 17.9%)
MiniMax
Higher SWE-Bench Verified score (55.6% vs 44.6%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
MiniMax
MiniMax M1 40K

FAQ

Common questions about DeepSeek-R1-0528 vs MiniMax M1 40K.

Which is better, DeepSeek-R1-0528 or MiniMax M1 40K?

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and MiniMax M1 40K is made by MiniMax. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-R1-0528 compare to MiniMax M1 40K in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. MiniMax M1 40K scores MATH-500: 96.0%, AIME 2024: 83.3%, MMLU-Pro: 80.6%, ZebraLogic: 80.1%, OpenAI-MRCR: 2 needle 128k: 76.1%.

What are the context window sizes for DeepSeek-R1-0528 and MiniMax M1 40K?

DeepSeek-R1-0528 supports 131K tokens and MiniMax M1 40K supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes DeepSeek-R1-0528 and MiniMax M1 40K?

DeepSeek-R1-0528 is developed by DeepSeek and MiniMax M1 40K is developed by MiniMax.