DeepSeek-V2.5 vs MiniMax M3.1 Flash
MiniMax M3.1 Flash leads the LLM Stats Score 48.5 to 7.8. DeepSeek-V2.5 is 1.5x cheaper per token.
DeepSeek · MiniMax · Updated for 2026
Which is better?
MiniMax M3.1 Flash leads the overall LLM Stats Score 48.5 to 7.8, ranking #29 overall.
On price, DeepSeek-V2.5 is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M3.1 Flash also accepts a larger context window (1,000,000 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-V2.5
- cost matters — it's about 1.5x cheaper per token
- you need open weights you can self-host or fine-tune
Choose MiniMax M3.1 Flash
- overall performance matters — it scores 48.5 and ranks #29 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
15 reported for DeepSeek-V2.5 · 14 for MiniMax M3.1 Flash
DeepSeek-V2.5 and MiniMax M3.1 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.1x cheaper than MiniMax M3.1 Flash ($0.15/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 2.1x cheaper than MiniMax M3.1 Flash ($0.60/1M tokens).
In conclusion, MiniMax M3.1 Flash is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
MiniMax M3.1 Flash accepts 1,000,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. MiniMax M3.1 Flash can generate longer responses up to 524,288 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
MiniMax M3.1 Flash supports multimodal inputs, whereas DeepSeek-V2.5 does not.
MiniMax M3.1 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
MiniMax M3.1 Flash
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while MiniMax M3.1 Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while MiniMax M3.1 Flash was released on 2026-10-19.
MiniMax M3.1 Flash is 30 months newer than DeepSeek-V2.5.
May 8, 2024
2.4 years ago
Oct 19, 2026
0 days ago
2.4yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. MiniMax M3.1 Flash is available from MiniMax.
DeepSeek-V2.5
MiniMax M3.1 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and MiniMax M3.1 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs MiniMax M3.1 Flash.