DeepSeek-V4.1-Flash-1017 vs MiniMax M2
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 26.4. DeepSeek-V4.1-Flash-1017 and MiniMax M2 cost the same.
DeepSeek · MiniMax · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 26.4, ranking #15 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; this is a narrower head-to-head signal than the composite indexes.
DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash-1017
- overall performance matters — it scores 51.2 and ranks #15 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 process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose MiniMax M2
- you want predictable pricing at $0.30/M input and $1.20/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 16 for MiniMax M2
DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (Humanity's Last Exam), while MiniMax M2 is better at 0 benchmarks.
DeepSeek-V4.1-Flash-1017 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) costs the same as MiniMax M2 ($0.30/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) costs the same as MiniMax M2 ($1.20/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 and MiniMax M2 cost the same.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 533.2B more parameters than MiniMax M2, making it 231.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to MiniMax M2's 1,000,000 tokens. MiniMax M2 can generate longer responses up to 1,000,000 tokens, while DeepSeek-V4.1-Flash-1017 is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas MiniMax M2 does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
MiniMax M2
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while MiniMax M2 was released on 2025-10-27.
DeepSeek-V4.1-Flash-1017 is 12 months newer than MiniMax M2.
Oct 17, 2026
5 days ago
11mo newerOct 27, 2025
12 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. MiniMax M2 is available from MiniMax, Novita.
DeepSeek-V4.1-Flash-1017
MiniMax M2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and MiniMax M2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs MiniMax M2.