DeepSeek-V3.1 vs DeepSeek-V4-Flash-0731
Comparing DeepSeek-V3.1 and DeepSeek-V4-Flash-0731 across benchmarks, pricing, and capabilities.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-V3.1 and DeepSeek-V4-Flash-0731 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4-Flash-0731 is roughly 4.0x 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-V3.1
- you want predictable pricing at $0.27/M input and $1.00/M output
Choose DeepSeek-V4-Flash-0731
- cost matters — it's about 4.0x 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-V3.1 and DeepSeek-V4-Flash-0731don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 3.0x more expensive than DeepSeek-V4-Flash-0731 ($0.09/1M tokens).
For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 5.6x more expensive than DeepSeek-V4-Flash-0731 ($0.18/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 367.0B more parameters than DeepSeek-V4-Flash-0731, making it 120.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to DeepSeek-V3.1's 163,840 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 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-V3.1 was released on 2025-01-10, while DeepSeek-V4-Flash-0731 was released on 2026-07-31.
DeepSeek-V4-Flash-0731 is 19 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.6 years ago
Jul 31, 2026
3 weeks ago
1.6yr 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-V3.1 is available from DeepInfra, Novita. DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks.
DeepSeek-V3.1
DeepSeek-V4-Flash-0731
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.1 and DeepSeek-V4-Flash-0731 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.1 vs DeepSeek-V4-Flash-0731.