DeepSeek-V4-Pro-0813 vs MiniMax M3
DeepSeek-V4-Pro-0813 leads the LLM Stats Score 54.1 to 41.9. MiniMax M3 is 1.0x cheaper per token.
DeepSeek · MiniMax · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 leads the overall LLM Stats Score 54.1 to 41.9, ranking #7 overall.
In the 2 individual benchmarks reported for both models, DeepSeek-V4-Pro-0813 wins 2; this is a narrower head-to-head signal than the composite indexes.
DeepSeek-V4-Pro-0813 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-Pro-0813
- overall performance matters — it scores 54.1 and ranks #7 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 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 Aug 2026
Choose MiniMax M3
- 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
12 reported for DeepSeek-V4-Pro-0813 · 35 for MiniMax M3
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (NL2Repo, Terminal-Bench 2.1), while MiniMax M3 is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 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-Pro-0813 ($0.43/1M tokens) is 1.4x more expensive than MiniMax M3 ($0.30/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 1.4x cheaper than MiniMax M3 ($1.20/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than MiniMax M3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1172.0B more parameters than MiniMax M3, making it 273.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to MiniMax M3's 512,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while MiniMax M3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
MiniMax M3 supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
MiniMax M3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
MiniMax M3
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-Pro-0813 was released on 2026-08-13, while MiniMax M3 was released on 2026-06-01.
DeepSeek-V4-Pro-0813 is 2 months newer than MiniMax M3.
Aug 13, 2026
2 weeks ago
2mo newerJun 1, 2026
2 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-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. MiniMax M3 is available from Fireworks, MiniMax, Novita, Together.
DeepSeek-V4-Pro-0813
MiniMax M3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and MiniMax M3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs MiniMax M3.