DeepSeek-V4-Pro-Max vs DeepSeek-V4.1-Flash
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 43.1. DeepSeek-V4.1-Flash is 4.9x cheaper per token.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 43.1, ranking #13 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4.1-Flash is roughly 4.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-Max 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-Max
- you process long inputs — it offers a 1,048,576 token context window
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 4.9x cheaper per token
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for DeepSeek-V4-Pro-Max · 20 for DeepSeek-V4.1-Flash
DeepSeek-V4-Pro-Max outperforms in 2 benchmarks (Humanity's Last Exam, MathArena Apex), while DeepSeek-V4.1-Flash is better at 1 benchmark (GPQA).
DeepSeek-V4-Pro-Max has a slight edge in benchmark performance.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-Max ($1.30/1M tokens) is 5.9x more expensive than DeepSeek-V4.1-Flash ($0.22/1M tokens).
For output processing, DeepSeek-V4-Pro-Max ($2.60/1M tokens) is 3.9x more expensive than DeepSeek-V4.1-Flash ($0.66/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-Max has 836.8B more parameters than DeepSeek-V4.1-Flash, making it 109.6% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-Max accepts 1,048,576 input tokens compared to DeepSeek-V4.1-Flash's 1,040,000 tokens. DeepSeek-V4-Pro-Max can generate longer responses up to 1,048,576 tokens, while DeepSeek-V4.1-Flash is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.
DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-Max
DeepSeek-V4.1-Flash
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-Max was released on 2026-04-23, while DeepSeek-V4.1-Flash was released on 2026-09-10.
DeepSeek-V4.1-Flash is 5 months newer than DeepSeek-V4-Pro-Max.
Apr 23, 2026
4 months ago
Sep 10, 2026
1 weeks ago
4mo 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-V4-Pro-Max is available from DeepInfra, Novita, DeepSeek, Fireworks, Together. DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita.
DeepSeek-V4-Pro-Max
DeepSeek-V4.1-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-Max and DeepSeek-V4.1-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-Max vs DeepSeek-V4.1-Flash.