DeepSeek-V3.2-Exp vs DeepSeek-V4-Flash-0731
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 28.2. DeepSeek-V4-Flash-0731 is 3.4x cheaper per token.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to 28.2, ranking #35 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 3.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 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-V3.2-Exp
- you want predictable pricing at $0.27/M input and $0.41/M output
Choose DeepSeek-V4-Flash-0731
- overall performance matters — it scores 44.7 and ranks #35 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 3.4x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
At a glance
The differences that matter most.
Individual benchmarks
14 reported for DeepSeek-V3.2-Exp · 9 for DeepSeek-V4-Flash-0731
DeepSeek-V3.2-Exp and DeepSeek-V4-Flash-0731don'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-V3.2-Exp ($0.27/1M tokens) is 4.5x more expensive than DeepSeek-V4-Flash-0731 ($0.06/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 2.3x more expensive than DeepSeek-V4-Flash-0731 ($0.18/1M tokens).
In conclusion, DeepSeek-V3.2-Exp is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 381.0B more parameters than DeepSeek-V4-Flash-0731, making it 125.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
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-V3.2-Exp was released on 2025-09-29, while DeepSeek-V4-Flash-0731 was released on 2026-07-31.
DeepSeek-V4-Flash-0731 is 10 months newer than DeepSeek-V3.2-Exp.
Sep 29, 2025
11 months ago
Jul 31, 2026
1 months ago
10mo 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-V3.2-Exp is available from Novita. DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks.
DeepSeek-V3.2-Exp
DeepSeek-V4-Flash-0731
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and DeepSeek-V4-Flash-0731 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Exp vs DeepSeek-V4-Flash-0731.