On July 31, 2026, DeepSeek released DeepSeek-V4-Flash-0731 as the official release of DeepSeek V4 Flash, in public beta on the API. It supersedes the April 24 Preview, keeps the same architecture and size, and changes post-training only. The stable API model ID deepseek-v4-flash did not change, which means existing API users started receiving the new version without touching their code. The same day brought a native Responses API for Codex-style agents, a new vendor-reported agent benchmark table, and official open weights on Hugging Face under the MIT license (DeepSeek changelog, official 0731 repository). We are an independent fan site, not DeepSeek, and this page keeps each of those claims separated and dated.
DeepSeek V4 Flash 0731 Release Status
The July 31 changelog entry uses two labels at once: this is the official release of V4 Flash, and the API serving it is in public beta. Both halves matter, and dropping either one misstates the launch. The Preview that launched on 2026-04-24 (Preview release note) is now superseded on the API side; the 0731 version is what the hosted deepseek-v4-flash endpoint serves as of 2026-07-31.
Official release and public beta
"Official release" describes the model: DeepSeek-V4-Flash-0731 is no longer a preview checkpoint, and the 0731 model card says it supersedes the Preview version. "Public beta" describes the service: the changelog entry dated 2026-07-31 states the V4 Flash API is "now in public beta" (changelog). The distinction is practical: the model label tells you which weights answer your requests; the beta label tells you the service contract — endpoints, limits, behavior — may still change. DeepSeek's own logged-out homepage banner uses the same public-beta wording, observed directly on deepseek.com on 2026-07-31, so the two labels are consistent across DeepSeek's surfaces, not an artifact of one page.
Why this is not a GA claim
Nothing in the reviewed first-party sources calls 0731 "GA," "generally available," or "production-stable," and this site will not either. A public beta gives no stability promise for request or response semantics: fields the Responses API currently ignores could later be honored or rejected, and documented limits such as the default account concurrency of 2500 for deepseek-v4-flash (per the rate-limit docs, as of 2026-07-31) are dynamic account-level values. If your deployment checklist requires a GA-grade SLA, the honest reading of today's status is: the model version is final for this release cycle, but the API around it is explicitly still in beta. Treat any third-party page that says "GA" as contradicting the primary source.
What Changed in DeepSeek V4 Flash 0731
Three things are new: the model was re-post-trained, the API gained a native Responses surface, and DeepSeek published a fresh agent benchmark table. Everything else — architecture, parameter count, context window, and the API model ID — carried over from the Preview.
New post-training
DeepSeek states that DeepSeek-V4-Flash-0731 "keeps the same model architecture and size" as the Preview and "was only re-post-trained" (changelog). No new pretraining run and no architecture change are claimed, but post-training is where instruction following, tool use, and agent behavior live, so the behavioral difference can still be large. The 0731 model card frames the result as "substantially enhanced agentic capabilities," and one same-day independent signal points the same direction: Artificial Analysis measured its Intelligence Index at 50 for 0731 versus 40 for the prior V4 Flash on its own nine-benchmark harness (AA article, 2026-07-31). Post-training changes are exactly the kind that shift outputs on existing prompts, which is why the rerun advice below is not optional.
Native Responses API
0731 shipped with a native Responses API, added — per the official guide — in response to Codex demand. As of 2026-07-31 it supports only deepseek-v4-flash (Pro support is listed as planned for early August 2026, a forward-looking date to recheck). Within the documented limits it supports streaming via typed SSE events, function tools, server-side web search, and exactly one custom tool name, apply_patch; other custom tool names return HTTP 400. It is stateless: previous_response_id, conversation, store, and background are not supported, and several unsupported request fields are silently ignored. So this is field-by-field compatibility, not full OpenAI Responses parity. The full endpoint comparison, including the Anthropic-compatible surface, lives on our DeepSeek V4 Flash API guide.
Updated agent benchmark results
DeepSeek published a new benchmark table with the release: Terminal Bench 2.1 at 82.7, NL2Repo 54.2, Cybergym 76.7, DeepSWE 54.4, Toolathlon-Verified 70.3, Agents' Last Exam 25.2, AutomationBench Public 25.1, plus DSBench-FullStack 68.7 and DSBench-Hard 59.6 on internal test sets (changelog). All of these are vendor-reported. The footnotes matter as much as the scores: public Code Agent tasks were run in DeepSeek Harness minimal mode — a harness not yet released — at reasoning_effort=max, temperature=1.0, top_p=0.95. DeepSeek also reports that 0731 outperforms the V4 Pro Preview on these agent benchmarks, a claim scoped to that table and to the Preview build of Pro, not to every task or to any future Pro release. The caveats get their own section below and a deeper treatment on the benchmarks page.
What Stayed the Same
The upgrade deliberately left the model's identity intact. If you are diffing spec sheets between April and July, these are the rows that should not move.
Architecture and parameter count
DeepSeek V4 Flash remains a mixture-of-experts text-generation model with 284B total parameters and 13B activated per token — never "a 13B model" — with a context window of up to 1M tokens (the config lists 1,048,576 positions) and a maximum output of 384K tokens, with input plus output still bounded by the context (pricing/spec table, 0731 config.json). The config contains no vision section, and the current official API remains text-only — see is DeepSeek V4 Flash multimodal for what that means for images and PDFs. What "up to 1M" does and does not guarantee in practice is covered on the context window page. One wrinkle worth knowing: the 0731 repository card mentions a 304B total figure alongside an attached DSpark speculative-decoding module, which we address in the weights section below.
Stable API model ID
The API model ID is still deepseek-v4-flash — lowercase, hyphenated, no date suffix. DeepSeek-V4-Flash-0731 is the model version string shown on the pricing page and the name of the open-weight repository; it is not a valid value for the model field in API calls. Pricing also carried over unchanged at the snapshot: $0.0028 per million cache-hit input tokens, $0.14 per million cache-miss input tokens, and $0.28 per million output tokens as of 2026-07-31, with 2x peak-hour pricing announced but carrying no effective date yet (official prices). Worked cost examples are on the pricing page.
How Existing API Users Received 0731
If you were calling deepseek-v4-flash on July 30, you did not opt into this release — it arrived underneath you. That is convenient and risky at the same time.
Automatic change behind deepseek-v4-flash
Because the release reuses the stable ID, the version swap happened server-side with zero code changes for existing integrations. There is no dated API alias like deepseek-v4-flash-0424 in the official model list to pin the old behavior (List Models); the Preview checkpoint remains available only as open weights, not as a hosted official endpoint. Community threads on launch day show real users discovering this property in real time — "is it auto-upgrade for the same model key?" was a top question in the r/LocalLLaMA release thread (community observation, 2026-07-31). The answer is yes for DeepSeek's first-party API. Note that this is precisely the opposite of how some resellers behaved on day one: OpenRouter's deepseek/deepseek-v4-flash alias still served the April revision at retrieval time (see provider freshness below).
Why prompts and evaluations should be rerun
A re-post-trained model behind an unchanged ID means your production behavior changed without a deploy on your side. Prompts tuned against the Preview — few-shot formats, tool-calling patterns, stop conditions, verbosity controls — should be treated as untested against 0731 until rerun. The same applies to any internal evaluation numbers you recorded before July 31: they now describe a model you are no longer calling. Practical checklist: rerun your eval suite against the live endpoint, diff outputs on a fixed prompt set, re-measure token usage per task (post-training can shift reasoning verbosity, which changes cost even at identical prices), and re-verify tool-call behavior, since requests carrying tools must pass reasoning_content back or receive a 400 error under the documented thinking-mode rules (Thinking Mode guide, as of 2026-07-31). Agent users should recheck harness configs too — our Claude Code setup page covers the Anthropic-compatible path.
DeepSeek Website and 0731 Access
The chat website and the API are different product surfaces, and the July 31 announcement is explicit only about the API. Here is what can and cannot be said about the website today.
What the website currently surfaces
As of 2026-07-31, DeepSeek's website surfaces V4 Flash access and the release announcement. The April release note already offered V4 Flash on web and app (Preview release note), and on launch day the logged-out deepseek.com homepage carried a banner announcing that the official V4 Flash API is in public beta, with V4 Pro unchanged — checked directly today. So a visitor asking "does the website have V4 Flash?" gets a yes: the entry point exists and the announcement is visible without logging in. What the banner does not contain is a checkpoint name: no "0731" string appears in it.
Why the exact website checkpoint remains unknown
Surfacing a model and naming the checkpoint that answers your chats are two different disclosures, and DeepSeek has made only the first. The reviewed first-party sources do not publish which exact checkpoint serves website conversations, and the July 31 X announcement (quoted via secondary coverage; the primary post was not directly retrievable today) says the upgrade "applies only to the DeepSeek-V4-Flash API," with app/web models unchanged for now. A UI label is not a backend version string. This site therefore keeps both halves: the website presents V4 Flash and the announcement, and the exact website serving checkpoint is not publicly named as of 2026-07-31. If DeepSeek publishes a named web checkpoint, this section changes; until then, anyone who needs 0731 specifically should use the API, where the version is documented.
Same-Day Hugging Face Weights
Unlike many hosted-first launches, 0731 shipped its open weights on release day. That single fact drives most of the local-deployment questions this site covers.
The official 0731 repository
The official repository is deepseek-ai/DeepSeek-V4-Flash-0731, created 2026-07-31, public and non-gated, tagged text-generation. The model card names vLLM and SGLang as the serving paths and documents the attached DSpark speculative-decoding module. It is a separate repository from the April Preview (deepseek-ai/DeepSeek-V4-Flash) — two checkpoints, two repos, and benchmark numbers that must never be mixed. One unresolved detail: the 0731 card mentions 304B total parameters, while the architecture spec everywhere else — including the Preview card and technical report — says 284B total / 13B activated. The likely explanation is that the 304B figure counts the bundled DSpark draft module, but no first-party sentence confirms that yet, so we report the conflict rather than pick silently. The spec figure remains 284B/13B.
MIT license and model identity
The repository and weights are MIT-licensed (LICENSE file): you may use, modify, distribute, and sell, provided the license notice is preserved, with no warranty. Two boundaries keep this honest. First, MIT weights do not make the hosted API free — API usage is billed at the prices above, and the license covers the artifacts you download, not the service. Second, an open-weight license is not a trademark or domain grant; DeepSeek's terms restrict unauthorized use of DeepSeek-branded domains and site names, which is part of why this independent site is a "fans" project and says so. On identity: weights downloaded from this repository are the official 0731 release, but DeepSeek has not published a server-side hash, so "bit-identical to what the hosted API serves" is not a claim anyone can verify today.
Why API and repository names differ
You will meet three names for one release, and each has one job. deepseek-v4-flash is the API model ID — the only string that belongs in a model field. DeepSeek-V4-Flash-0731 is the version label on the pricing page and the Hugging Face repo name — use it when you need to say which release you mean. "DeepSeek V4 Flash" is the product name in prose. Putting the repo name into an API call fails; calling the API ID "the 0731 model" is fine today but silently becomes wrong the day a newer checkpoint replaces it behind the same ID. Launch-day community reaction shows the cost of this scheme — "why not call it V4.1?" was a recurring complaint — but it is the scheme in force, so pin your language, and your evals, to the version string.
DeepSeek V4 Flash 0731 Benchmark Changes
The benchmark story is the loudest part of this release and the easiest to over-read. The numbers are real vendor claims with published settings; they are not yet independent measurements.
The new headline scores
In DeepSeek's own July 31 evaluation, the flagship number is Terminal Bench 2.1 at 82.7, alongside DeepSWE 54.4 and Cybergym 76.7 (changelog). Two same-day checks put that in context. Artificial Analysis — the only confirmed same-day independent rerun — scored 0731 at 50 on its Intelligence Index, up 10 points from the prior V4 Flash, while measuring unusual verbosity: about 210M output tokens for its suite versus a roughly 62M median for comparable models (AA article, 2026-07-31). And the official Terminal Bench 2.1 leaderboard at tbench.ai did not list DeepSeek at all when checked on 2026-07-31 — so 82.7 is DeepSeek-reported, not leaderboard-verified. Avoid computing a "Preview to 0731" delta from Terminal Bench, too: the Preview card reported 56.9 on Terminal Bench 2.0 at max effort, and 2.1 repaired 28 of the 89 tasks, so the versions are not apples-to-apples.
Harness and internal-dataset caveats
Three caveats travel with every number in the vendor table. One: the public Code Agent results used DeepSeek Harness minimal mode, and that harness is unreleased, so nobody outside DeepSeek can reproduce the runs yet — and harness choice demonstrably moves agent scores by large margins. Two: the runs used maximum reasoning effort with temperature=1.0 and top_p=0.95; your latency and cost at those settings will not match a default configuration, which matters because effort levels are billed through output tokens. Three: DSBench-FullStack (68.7) and DSBench-Hard (59.6) are DeepSeek-internal test sets with no public copy, so those two rows are unverifiable by construction. None of this means the scores are wrong — the independent AA jump is directionally consistent — but it means "82.7" belongs in a sentence with "vendor-reported," and comparisons against the V4 Pro Preview belong on the Flash versus Pro page with their version labels attached.
What Preview Users Should Check
If you built anything on the April Preview — prompts, integrations, or local artifacts — this release gives you a short, concrete migration checklist.
Prompt and behavior regressions
Re-post-training changes behavior by design, and improvements on agent benchmarks do not guarantee your workload improved. Check the failure modes that are cheap to test: instruction adherence on long system prompts, output format stability (JSON and structured outputs), refusal boundaries, language consistency, and verbosity — the independent launch-day measurement found 0731 verbose relative to peers, which inflates cost per task even at unchanged per-token prices. Run the same fixed prompt set against archived Preview outputs if you have them, or against the Preview open weights if you self-host. Reasoning-effort settings deserve a fresh sweep too: low, high, and max map to different token spend, and the settings that balanced cost and quality in April were tuned on a model that no longer answers your API calls.
Responses API and Codex migration
Preview-era integrations used Chat Completions or the Anthropic-compatible endpoint; 0731 adds a third option that is the documented path for Codex. If you are moving an agent onto the Responses API, plan around its stateless design: no previous_response_id, no server-side conversation storage, no background mode — your client owns history replay. Function tools and web search work; the only accepted custom tool name is apply_patch, and unsupported fields may be silently ignored rather than rejected, so verify behavior from responses, not from the absence of errors. There is no forced migration: Chat Completions continues to work, and Claude Code users stay on the Anthropic-compatible endpoint. Choose per harness — Codex to Responses, Claude Code to Anthropic-compatible — and confirm the served model from response metadata after switching. Setup details are on the API guide.
Provider and local-artifact freshness
On launch day, third-party channels lag the first-party API, sometimes invisibly. The clearest case: as of 2026-07-31, OpenRouter's deepseek/deepseek-v4-flash alias still served the April revision (listed at $0.09/$0.18 per million tokens), while the new checkpoint lived at a separate slug, deepseek/deepseek-v4-flash-0731, priced to match the official $0.14/$0.28. The same alias name means the new version on DeepSeek's API and the old version on OpenRouter — verify the revision, not the name, on every provider. Local artifacts have the same trap: Unsloth's 0731 GGUFs live in a dedicated DeepSeek-V4-Flash-0731-GGUF repository (only Q4 and Q8 variants uploaded at retrieval time), distinct from the Preview-based repo without the suffix, and Ollama's official listing is a cloud alias, not a local download. Check the exact repo name and upload date before pulling anything.
DeepSeek V4 Flash 0731 FAQ
When was DeepSeek V4 Flash 0731 released?
July 31, 2026. The dated entry in the DeepSeek API changelog announces DeepSeek-V4-Flash-0731 as the official release in public beta, superseding the Preview that launched on April 24, 2026. Same-day official weights appeared on Hugging Face.
Is DeepSeek V4 Flash 0731 GA?
No. DeepSeek's own wording is "official release" in "public beta," and no first-party source reviewed for this page uses GA or production-stable language as of 2026-07-31. Treat the model version as final for this cycle and the API service contract as still subject to change.
Do API users need a new model ID?
No. The stable ID deepseek-v4-flash now serves the 0731 version automatically. That also means the change reached existing integrations without any action, so rerunning prompts and evaluations is the required follow-up, not switching IDs. Do not put the repository name DeepSeek-V4-Flash-0731 in API calls.
Does the website offer DeepSeek V4 Flash?
Yes. The website has offered V4 Flash access since the April release, and on 2026-07-31 the logged-out homepage displayed the public-beta announcement for the official Flash API. The website entry point and the API release are still separate surfaces, though — see the next question.
Is the website definitely serving the 0731 checkpoint?
Unknown. DeepSeek states the July 31 upgrade applies to the Flash API, and no reviewed first-party source names the exact checkpoint behind website chats. The site surfaces V4 Flash and the announcement, but a UI label is not a backend version string. Users who need 0731 specifically should use the API.
Where can I download the 0731 weights?
From the official Hugging Face repository deepseek-ai/DeepSeek-V4-Flash-0731, public and MIT-licensed as of 2026-07-31. It is a different repository from the April Preview (deepseek-ai/DeepSeek-V4-Flash), and community conversions such as GGUF builds are separate, third-party artifacts — check whether any conversion is built from the 0731 repo before using it.
Did the model architecture change?
No. DeepSeek states 0731 keeps the same architecture and size as the Preview — a mixture-of-experts design with 284B total and 13B activated parameters, up to 1M context and 384K maximum output — and was only re-post-trained. One open item: the 0731 model card also mentions a 304B total figure, most plausibly counting the attached DSpark speculative-decoding module, which we flag as unresolved rather than treating as an architecture change.