Model Comparison
DeepSeek-V3.2 (Thinking) vs DeepSeek-V4-Flash-0731Which is better in 2026?
DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks. DeepSeek-V4-Flash-0731 is 2.8x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs DeepSeek-V4-Flash-0731 — which is better?
DeepSeek-V3.2 (Thinking) (by DeepSeek) and DeepSeek-V4-Flash-0731 (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2 (Thinking) 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.
Choose DeepSeek-V3.2 (Thinking) if…
- you want predictable pricing at $0.28/M input and $0.42/M output
Choose DeepSeek-V4-Flash-0731 if…
- 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
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) 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
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 3.1x more expensive than DeepSeek-V4-Flash-0731 ($0.09/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 2.3x more expensive than DeepSeek-V4-Flash-0731 ($0.18/1M tokens).
In conclusion, DeepSeek-V3.2 (Thinking) 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 (Thinking) 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 (Thinking)'s 131,072 tokens. Both models can generate responses up 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 (Thinking) 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 (Thinking).
Dec 1, 2025
8 months ago
Jul 31, 2026
6 days 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 (Thinking) is available from DeepSeek. DeepSeek-V4-Flash-0731 is available from DeepInfra, Fireworks, Novita.
DeepSeek-V3.2 (Thinking)
DeepSeek-V4-Flash-0731
Outputs Comparison
Key Takeaways
No standout differentiators in the data we have for this pair.
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and DeepSeek-V4-Flash-0731 side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs DeepSeek-V4-Flash-0731.