DeepSeek-V4-Flash-0731 vs GPT-3.5 Turbo
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -9.4. DeepSeek-V4-Flash-0731 is 8.3x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to -9.4, ranking #36 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 8.3x 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-V4-Flash-0731
- overall performance matters — it scores 44.7 and ranks #36 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 8.3x 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
- you need open weights you can self-host or fine-tune
Choose GPT-3.5 Turbo
- you want predictable pricing at $0.50/M input and $1.50/M output
At a glance
The differences that matter most.
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 8 for GPT-3.5 Turbo
DeepSeek-V4-Flash-0731 and GPT-3.5 Turbodon'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-V4-Flash-0731 ($0.06/1M tokens) is 8.3x cheaper than GPT-3.5 Turbo ($0.50/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 8.3x cheaper than GPT-3.5 Turbo ($1.50/1M tokens).
In conclusion, GPT-3.5 Turbo is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while GPT-3.5 Turbo uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while GPT-3.5 Turbo was released on 2023-03-21.
DeepSeek-V4-Flash-0731 is 41 months newer than GPT-3.5 Turbo.
Jul 31, 2026
1 months ago
3.4yr newerMar 21, 2023
3.5 years ago
Knowledge Cutoff
When training data ends
GPT-3.5 Turbo has a documented knowledge cutoff of 2021-09-30, while DeepSeek-V4-Flash-0731's cutoff date is not specified.
We can confirm GPT-3.5 Turbo's training data extends to 2021-09-30, but cannot make a direct comparison without DeepSeek-V4-Flash-0731's cutoff date.
—
Sep 2021
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. GPT-3.5 Turbo is available from Azure, OpenAI.
DeepSeek-V4-Flash-0731
GPT-3.5 Turbo
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and GPT-3.5 Turbo side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs GPT-3.5 Turbo.