Co-Founder & Lead Programmer of AcceleratedLogic AI
DeepSeek V4 Flash 0731 is now a historical model version, so an older review calling it a current API workhorse is no longer reliable guidance. DeepSeek's API documentation says the previous V4 Flash generation has been retired and that legacy Flash model names route to V4.1 Flash. This update explains the version history and the migration checks that matter for an existing integration.
What the 0731 version was
DeepSeek's model-series documentation describes V4 Flash as a 285-billion-parameter model with 13 billion active parameters. A DeepSeek API changelog entry says V4-Flash-0731 retained the architecture and size of V4-Flash-Preview and differed through additional post-training. That describes a particular checkpoint revision; it should not be mistaken for the name or behavior of the model currently served by the API.
Current API status and migration
DeepSeek's model and pricing documentation now identifies the Flash API model as DeepSeek-V4.1-Flash and recommends deepseek-flash as the model name. It says the previous V4 Flash and V4 Flash Vision Exp versions are retired; the legacy names deepseek-v4-flash and deepseek-v4-flash-vision-exp are temporarily routed to V4.1 Flash and billed at the Flash rate. Check DeepSeek's API change log before using an older model identifier, because compatibility routing can be removed or changed.
If an application still names the 0731 checkpoint, verify what endpoint actually responds, change the configured model to the documented current ID where appropriate, and rerun regression tests. Recheck context limits, supported features, peak/off-peak rates, and cached-token pricing in the current docs; do not carry forward the old flat price quoted in this article's original version.
Why historical benchmark scores need context
The original page included an informal rating and an ambitious browser-game generation report. It did not preserve a reproducible harness, repeated runs, a task rubric, or enough usage data to support broad claims about quality, verbosity, or cost per task. Those claims have been removed rather than presented as a current model comparison.
For your own evaluation, keep the exact model version, date, prompt, tools, temperature/reasoning settings, and outputs. Use several real tasks, score them against written acceptance criteria, and compare cost and latency alongside success. A saved demo or a vendor benchmark can help identify what to test next, but it cannot predict performance on your workload by itself.