DeepSeek-V4-Flash-0423 vs DeepSeek-V4.1-Flash
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.5 to 36.1. DeepSeek-V4-Flash-0423 is 2.9x cheaper per token.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.5 to 36.1, ranking #13 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4-Flash-0423 is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0423 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-Flash-0423
- cost matters — it's about 2.9x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose DeepSeek-V4.1-Flash
- overall performance matters — it scores 51.5 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- 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
19 reported for DeepSeek-V4-Flash-0423 · 20 for DeepSeek-V4.1-Flash
DeepSeek-V4-Flash-0423 outperforms in 2 benchmarks (Humanity's Last Exam, MathArena Apex), while DeepSeek-V4.1-Flash is better at 2 benchmarks (CodeForces, GPQA).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0423 ($0.09/1M tokens) is 2.4x cheaper than DeepSeek-V4.1-Flash ($0.22/1M tokens).
For output processing, DeepSeek-V4-Flash-0423 ($0.18/1M tokens) is 3.7x cheaper than DeepSeek-V4.1-Flash ($0.66/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than DeepSeek-V4-Flash-0423.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 479.2B more parameters than DeepSeek-V4-Flash-0423, making it 168.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0423 accepts 1,048,576 input tokens compared to DeepSeek-V4.1-Flash's 1,040,000 tokens. DeepSeek-V4-Flash-0423 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-Flash-0423 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-Flash-0423
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-Flash-0423 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-Flash-0423.
Apr 23, 2026
5 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-Flash-0423 is available from DeepInfra, Novita. DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita.
DeepSeek-V4-Flash-0423
DeepSeek-V4.1-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0423 and DeepSeek-V4.1-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0423 vs DeepSeek-V4.1-Flash.