DeepSeek-V3.2-Speciale vs DeepSeek-V4-Flash-0731
DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks. DeepSeek-V4-Flash-0731 is 2.8x cheaper per token.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V3.2-Speciale outperforms in 0 benchmarks, while DeepSeek-V4-Flash-0731 is better at 1 benchmark (Toolathlon). DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Flash-0731 is roughly 2.8x 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2-Speciale
- you want predictable pricing at $0.28/M input and $0.42/M output
Choose DeepSeek-V4-Flash-0731
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 2.8x 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.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Speciale outperforms in 0 benchmarks, while DeepSeek-V4-Flash-0731 is better at 1 benchmark (Toolathlon).
DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 3.1x more expensive than DeepSeek-V4-Flash-0731 ($0.09/1M tokens).
For output processing, DeepSeek-V3.2-Speciale ($0.42/1M tokens) is 2.3x more expensive than DeepSeek-V4-Flash-0731 ($0.18/1M tokens).
In conclusion, DeepSeek-V3.2-Speciale 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-Speciale 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-Speciale's 131,072 tokens. DeepSeek-V3.2-Speciale can generate longer responses up to 131,072 tokens, while DeepSeek-V4-Flash-0731 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-Speciale was released on 2025-12-01, while DeepSeek-V4-Flash-0731 was released on 2026-07-31.
DeepSeek-V4-Flash-0731 is 8 months newer than DeepSeek-V3.2-Speciale.
Dec 1, 2025
8 months ago
Jul 31, 2026
3 weeks ago
8mo 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-Speciale is available from DeepSeek. DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks.
DeepSeek-V3.2-Speciale
DeepSeek-V4-Flash-0731
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Speciale and DeepSeek-V4-Flash-0731 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Speciale vs DeepSeek-V4-Flash-0731.