V4-Flash-0731: DeepSeek published the table where it loses 9 to 0 — and that is the best piece of the launch

DeepSeek took V4-Flash out of beta on July 31 under the codename V4-Flash-0731, and one headline made the rounds: the house's cheap model now beats the flagship V4-Pro on all nine agentic benchmarks published. That is true — and it is not the interesting part.

I went to the official model card. The table the headline came from has two columns the coverage never mentions, and in one of them Claude Opus 4.8 beats V4-Flash-0731 on all nine rows. Nine to zero, published by DeepSeek itself. What a company gains by announcing its own defeat is the right question about this launch — and the answer is in the price.

What actually changed

The verifiable facts, from the official model card and the API pricing table:

The decisive sentence in the announcement is the one that draws the least attention: the gains come from "re-post-training, not a new design". Same architecture, same endpoint, same price, same latency — they only swapped the final training stage. Hold on to that sentence; it comes back later.

The operational details that are worth money

Four items that only surface by reading the documentation, in the usual spirit:

1. The cache discount is the hidden product. 98% off the cached prefix (industry practice hovers around 90%). A long-running agent with a stable system prompt pays almost only for output.
2. Peak pricing was announced, with no date. The documentation warns the API will adopt dual pricing — double during the 9am–12pm and 2pm–6pm Beijing windows. Anyone planning cheap batch runs now knows which timezone to schedule in.
3. The Responses API only covers the Flash. The Codex-compatible format works today only on deepseek-v4-flash; the Pro is promised for early August.
4. V4-Pro was not updated. Neither were the site and app models. The launch is the Flash alone — which makes the table below stranger still.

The full table

The model card's table, complete — with the two columns the headline skipped:

Two readings, in order of astonishment.

The column of Flash's own April preview. On DeepSWE, the preview scored 7.3; 0731 scores 54.4 — seven and a half times higher, without touching a single architecture parameter. On Cybergym, from 38.7 to 76.7. If those numbers hold up outside the house harness, it is the most aggressive demonstration yet that the post-training recipe now matters more than the model's design — the cost of catching up to a competitor dropped from "train another model" to "re-train the final stage of what you already have".

The Opus 4.8 column. It wins all nine rows. DeepSeek chose to publish that — not in an appendix, in the card's main table. It is not an oversight; it is the sales argument read back to front, and it only works because of the next section.

Methodological note — read before citing this table. The agentic benchmarks ran on the "minimal mode" of the DeepSeek Harness, which has not been published, at temperature 1.0 and max effort. Until the harness ships, none of these numbers is independently reproducible. And beware of crossing announcements: this table's "AutomationBench Public" gives Opus 4.8 a 27.2, while the Opus 5 announcement table, one week earlier, gave the same model a 17.0 on "AutomationBench" — different subset and harness, incomparable numbers. The same goes for DeepSWE (here with no version suffix; there, "v1.1"). Vendor benchmarks only compare within their own announcement.

Losing 9 to 0 while costing one eighty-ninth

The math the table does not show: Opus 4.8 costs US$ 5 for input and US$ 25 for output per million tokens — the same prices as Opus 5, verified in the article about its launch. V4-Flash-0731 costs US$ 0.14 and US$ 0.28. Thirty-six times less on input; eighty-nine times less on output. The average gap in the table is a single-digit percentage per row.

And here comes the third-party test the Opus 5 article ended up asking for — this time it arrived on launch day. Artificial Analysis measured 0731 at 50 on its Intelligence Index. The neighborhood for context: GPT-5.6 Luna and GLM-5.2 at 51, Gemini 3.6 Flash at 50, Kimi K3 at 57, Claude Opus 5 at 61 — and DeepSeek's own V4-Pro at 44, six points below the house's cheap model.

The chart's vertical jump is the entire launch: ten index points at the same price, the line climbing without moving right. Artificial Analysis estimates 0731's cost per task at roughly 60% less than GPT-5.6 Luna's for the same intelligence band — an effect owed less to the list price and more to the 98% cache discount.

That is why publishing the 9-to-0 defeat works as advertising: the table does not claim "we are the best"; it claims "we are a few points behind someone who charges 89 times more". For the market segment that decides on cost per completed task — agents burning millions of tokens a day — the second argument is the stronger one. With the usual caveat: what measures that "few points" distance is DeepSeek's own unpublished harness; the independent version, for now, is only AA's aggregate index.

What this index does not say. The Intelligence Index is an aggregate average — it does not measure your use case, and AA has not yet published 0731's agentic breakdown. The 11 points between it and Opus 5 may be irrelevant for autocomplete and decisive for an agent driving a terminal for hours. Both things fit inside the same number.

Post-training became the product

The technical detail with strategic consequence: a jump from 7.3 to 54.4 on a software engineering benchmark with no new architecture means the agentic frontier is, right now, limited less by model size and more by the quality of the post-training pipeline — agent trajectory data, RL over tool use, evaluation harness. Architecture gets published in papers; the post-training recipe is the industrial secret of the moment. DeepSeek opened the weights and kept the recipe — MIT for the artifact, silence about the method that produced it. It is open-weights, not open-science, and the distinction has never been this visible.

The same package ships DSpark, the speculative-decoding module described in a paper of its own: 60% to 85% faster per user, measured on real traffic in DeepSeek's serving system — not on a bench. The paper's own caveat: the gain holds at matched throughput, under their production interactivity constraints; your local deploy does not inherit the number automatically.

The first 48 hours outside the harness

The launch has two audiences, and they measured different things.

The local-inference camp celebrated what can be weighed. The official weights running on two DGX Sparks at 82 tok/s peak (60–72 typical) with the 1M context; on an RTX 5090 with 192 GB of RAM at 12 tok/s — "not frontier level... but pretty good at functional code"; and the refrain that any 128 GB Mac now runs "Opus-level coding" locally. On the API side, one user added up the day's bill: 110 million tokens processed, US$ 0.77.

The skeptic camp aimed exactly where the methodological note above points. The top-voted comment (+385) on the main r/LocalLLaMA thread opens with the entire caveat in italics: "*in benchmarks". One dev summed up the structural problem: "evaluate model + harness, not weights". Another, after several rounds on an independent harness, saw "extremely inconsistent" quality and classified the first good result as a "lucky run". And there was a measured regression: better at HTML generation, worse than the preview at JSON and reasoning, sustained throughput 7% lower. Some called the table cherry-picking — an accusation the Opus 4.8 column, published by DeepSeek itself, answers on its own.

And Artificial Analysis itself complicated its own number. On the AA-Omniscience index, the company reported that 0731's accuracy stayed flat at 37%; what dropped 11 points was the hallucination rate. Part of the 10-point jump in the aggregate index, therefore, is not the model knowing more — it is the model making things up less. For an autonomous agent that is perhaps the most valuable improvement in the package; it is just not the one the headline suggests.

MIT, no gate, on a 128 GB MacBook

The launch's third axis has no benchmark: the weights are on Hugging Face under MIT, no sign-up. With 13B active per token, the 3-bit build runs in about 110 GB of combined RAM+VRAM — inside a 128 GB MacBook; the 8-bit build asks for 162 GB. A model that competes in AA's 50-point band, capable of operating as an agent, running entirely on the local machine, is a new ceiling for the "depends on nobody's API" category — with the usual ceiling attached: workstation speed, not datacenter speed.

What I would do with this

I have not tested 0731 in production — it shipped two days ago. Where I would look first:

Measure cost per task, not per token. The chart's lesson: an 89-times-lower list price only becomes 89-times-lower cost if the task completion rate keeps up. The honest test is your own backlog of real tasks, the same prompt, both models, cost per completed task — and their unpublished harness is one more reason to measure with yours.

Design the agent around the cache. A 98% discount on the prefix changes the optimal architecture: a long, stable system prompt becomes nearly free; what costs money is varying the prefix. It is worth reordering what is fixed and what is dynamic in your prompt before comparing prices with any competitor.

Schedule batch runs outside Beijing peak hours. The announced dual pricing doubles the cost in known windows (9am–12pm and 2pm–6pm, China time). A Brazilian overnight pipeline falls outside the peak — for free.

And do not cross announcement tables. The same Opus 4.8 scores 27.2 in one announcement and 17.0 in the other, on the "same" benchmark. Any comparison you assemble by pasting numbers from different posts is wrong by construction. The only ruler that counts is the one you hold yourself.

Sources

• Official model card: huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731
• Pricing: api-docs.deepseek.com/quick_start/pricing
• DSpark paper: arxiv.org/abs/2607.05147 · V4 technical report: arxiv.org/abs/2606.19348
• Independent index: officechai.com — V4-Flash-0731 on Artificial Analysis
• Coverage: MarkTechPost · TechTimes · XenoSpectrum

Prices and specs checked against DeepSeek's official documentation on Aug 1, 2026; the benchmark table was transcribed from the model card on Hugging Face; the chart's values come from Artificial Analysis's public index and the official price tables — the April preview point (≈40) is derived from the reported 10-point gain, not from direct measurement. Community quotes were collected on Aug 1, 2026 via X and Reddit search and transcribed from the original posts, each with its link; vote counts are as of collection time. The Opus 4.8/5 price (US$ 5 input, US$ 25 output) was re-checked on Aug 1, 2026 against Anthropic's official pricing page.