AI MODEL PROFILE

DeepSeek-V4-Flash

A genuinely permissive MIT licence on a model with a million token context, at roughly a thirtieth of the price of the Western flagships.

Source review Last reviewed August 22, 2026 By DeepSeek

The facts at a glance

ProviderDeepSeek
TypeOpen weight reasoning model
Current version or tierDeepSeek-V4-Flash-0731, public beta 31 July 2026
ModalityText
AccessOpen weights under the MIT licence
Context or limits1,000,000 token context, 384,000 max output, 304 billion total parameters in a mixture of experts design
Free optionYes, by self hosting the MIT licensed weights from Hugging Face
Paid priceUSD 0.44 per million input on cache miss (0.22 off-peak) and USD 1.32 per million output (0.66 off-peak), under a peak/off-peak schedule DeepSeek introduced 16 August 2026. Cache hits are USD 0.044 per million (0.022 off-peak). Peak hours are 01:00-04:00 and 06:00-10:00 UTC.

Pricing last checked August 22, 2026. Prices change and vary by region. Check the official pricing page ↗

Assessment

What it does well

  • MIT licensed weights, which is as permissive as open weight releases get
  • DeepSeek reports 82.7 on Terminal Bench 2.1 and 76.7 on Cybergym, and states it outperforms the larger V4-Pro preview despite far fewer activated parameters
  • Cache hit pricing of USD 0.0028 per million input is effectively free for repeated context

Where it falls short

  • The speculative decoding module needs specific vLLM or SGLang flags and there is no standard chat template, so deployment requires the repository's own encoding scripts
  • DeepSeek's pricing page warns that a significant API price increase is expected
  • Text only

Privacy and data control

DeepSeek's privacy policy states it collects prompts, uploaded files, and photos and uses them to train its models, stores personal data in the People's Republic of China, and offers a training opt out only by emailing privacy@deepseek.com. Self hosting the open weights avoids this entirely.

The longer read

The licence is the story

A great deal of what gets called open in AI is not. Custom licences with commercial restrictions, acceptable use clauses, and redistribution limits are common. DeepSeek-V4-Flash ships under plain MIT. If you need to run a capable model on your own hardware, with your own data, under terms your legal team will actually approve, that combination is rare and it is the reason to look here first.

How it performs by outcome

We assess products against a job, not a leaderboard. These are the outcomes this entry is compared under.

Worth comparing against

Qwen3.8-Max Multi day autonomous coding projects Kimi K3 Research teams that need frontier scale weights they can inspect Mistral Medium 3.5 Coding agents running on a small GPU cluster Grok Questions about what is happening right now on the open web and on X

How this profile was produced

Source review. Every fact here is drawn from the provider documentation linked below and checked on the date shown. We have not yet run this entry through a controlled test, and no scoring is implied.

Read the full review methodology →

Primary sources