Model Comparison

DeepSeek-V2.5 vs MiniMax M1 40K

MiniMax M1 40K significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V2.5 outperforms in 0 benchmarks, while MiniMax M1 40K is better at 1 benchmark (SWE-Bench Verified).

MiniMax M1 40K significantly outperforms across most benchmarks.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
MiniMax
MiniMax M1 40K
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

220.0B diff

MiniMax M1 40K has 220.0B more parameters than DeepSeek-V2.5, making it 93.2% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
MiniMax
MiniMax M1 40K
456.0Bparameters
236.0B
DeepSeek-V2.5
456.0B
MiniMax M1 40K

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
MiniMax
MiniMax M1 40K
Input- tokens
Output- tokens
Fri Apr 17 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while MiniMax M1 40K uses MIT.

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

DeepSeek-V2.5

deepseek

Open weights

MiniMax M1 40K

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while MiniMax M1 40K was released on 2025-06-16.

MiniMax M1 40K is 13 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

MiniMax M1 40K

Jun 16, 2025

10 months ago

1.1yr 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→

Key Takeaways

DeepSeek
Larger context window (8,192 tokens)
MiniMax
Higher SWE-Bench Verified score (55.6% vs 16.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
MiniMax
MiniMax M1 40K

FAQ

Common questions about DeepSeek-V2.5 vs MiniMax M1 40K

MiniMax M1 40K significantly outperforms across most benchmarks. DeepSeek-V2.5 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.
DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. 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%.
DeepSeek-V2.5 supports 8K 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.
Key differences include licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V2.5 is developed by DeepSeek and MiniMax M1 40K is developed by MiniMax.