DeepSeek V4-Flash

Spec

Attribute	Value
Developer	DeepSeek
Released	2026-07-31 (official / public beta, build 0731; preview earlier)
Announced	2026-07-31
Context window	1,048,576 (1M) — OpenRouter catalogue deepseek/deepseek-v4-flash, read 2026-08-01; DeepSeek's own launch material did not state it
Pricing	¥1.50/M input (cache miss) · ¥0.05/M input (cache hit) · ¥4.50/M output off-peak; 2× at peak, from 2026-08-16 — see Pricing and Conflicting Reports
License	MIT (open-weight)
Availability	DeepSeek API (Responses format, Codex-adapted), Hugging Face (deepseek-ai/DeepSeek-V4-Flash-0731)

Release Date

2026-07-31, moving V4-Flash out of Preview into public beta under the build designation 0731 (source).

The release is post-training only — same 284B total / 13B active MoE architecture as the Preview, re-post-trained for agentic tasks, no architectural change (source).

A statement accompanying the release says "The official release of DeepSeek-V4-Pro will follow soon" (source).

Pricing — moved off the flat rate 2026-08-16, and the new figures are in RMB

From 2026-08-16 at 16:00 UTC, V4-Flash left flat per-token rates for the peak/off-peak schedule alongside DeepSeek V4-Pro-0813 (source).

Per 1M tokens	Cache-hit input	Cache-miss input	Output
Off-peak, RMB	¥0.05	¥1.50	¥4.50
Peak, RMB	¥0.10	¥3.00	¥9.00
Peak hours are 01:00–04:00 and 06:00–10:00 UTC; all other hours are off-peak
(source).

The currency changed with the rate, and that is not cosmetic. Every source read gives the new Flash schedule in RMB only, where the rates it replaced were quoted in USD — $0.0028 cache-hit input, $0.14 cache-miss input, $0.28 output per 1M (source). The two rows above are therefore not directly comparable to the row they replace, and this page does not convert them: a converted figure would be this wiki's arithmetic published as DeepSeek's price. The round RMB values are also the best evidence that the sibling page's dollar figures are conversions — see DeepSeek V4-Pro-0813 ## Conflicting Reports, where the reported multipliers and the reported dollars miss each other by ~1.1%.

Benchmarks

Vendor-stated figures as relayed by coverage; the primary model card was not readable from this environment. The comparison column is DeepSeek's own larger V4-Pro-Preview (source):

Benchmark	Flash Preview	Flash 0731	V4-Pro-Preview
Terminal Bench 2.1	61.8	82.7	72.1
DeepSWE	7.3	54.4	unknown
Cybergym	38.7	76.7	unknown
NL2Repo	39.4	54.2	unknown
Toolathlon-Verified	49.7	70.3	unknown
Coverage reports the retrained Flash beating V4-Pro (Preview) on **nine agent
benchmarks** despite a far smaller activated parameter count
(source).

Per Eval Harness Configuration, these are vendor-run numbers on agentic benchmarks with no published harness configuration, so they are recorded as claims about a (model, harness) pair rather than as model properties.

Independent measurement — Artificial Analysis (2026-08-01)

Artificial Analysis scored the model on its own index. Figures are quoted with the column heading and AA's own variant qualifier (source):

Model	Artificial Analysis Intelligence Index
GPT-5.6 Luna (max)	51
GLM-5.2 (max)	51
DeepSeek V4 Flash 0731 (max)	50
DeepSeek V4 Pro	44
DeepSeek V4 Flash (April 2026)	40
AA states the score is a 10-point jump over the April Flash and **6 points ahead of
DeepSeek V4 Pro**, that the model lands on AA's **Pareto frontier for Intelligence vs
Cost per Task**, and that with weights released it is among the **top 3 open-weight
models** on the leaderboard. Cost per task is reported at roughly **60% below GPT-5.6
Luna (max)**, attributed largely to DeepSeek's ~98% cache-hit discount on its
first-party API
(source)
(the-decoder).

This is a single third-party index rather than a harness-controlled comparison, but it is measured the same way for every model in the table, and it agrees with the direction of the vendor claims above without depending on them. Note the V4-Pro row: the larger sibling whose "official release will follow soon" scores below this model on AA's index.

Capture caveat: scripts/aa-fetch.py could not run — the sandbox egress proxy answers 403 to CONNECT for artificialanalysis.ai — so these figures come from AA's own article and posts rather than from a structured leaderboard snapshot.

Architecture

• MoE, 284B total / 13B active — unchanged from the Preview
• Self-hosting: minimum a single H100 for FP8, or roughly 170 GB VRAM for full weights (source)

Use Cases

• Agentic coding and terminal/tool use — the benchmarks that moved are all agentic
• Cost-sensitive serving where V4-Pro is too expensive
• Local and self-hosted inference under MIT

Compared To

Model	Terminal Bench 2.1	Open?
DeepSeek V4-Flash 0731	82.7 (vendor-stated)	Yes (MIT)
DeepSeek V4-Pro-Preview	72.1 (vendor-stated)	Yes (MIT)
DeepSeek V4 (V4-Pro-Max)	unknown	Yes (MIT)

Conflicting Reports

• Pricing. The rates above ($0.14 input cache-miss / $0.28 output) come from digitalapplied's release write-up. AlphaSignal's headline for the same model quotes "$0.08 per million tokens". Nothing captured reconciles the two — $0.08 may be a blended or off-peak rate, but no source read says so (source).
• Artificial Analysis placement — resolved 2026-08-02, not a conflict. On 2026-08-01 this page recorded an r/LocalLLaMA report of "50 on the Artificial Analysis Intelligence Index, 1 point below GLM-5.2 and GPT-5.6" as a community claim that could not be quoted as a benchmark (source). Artificial Analysis has since published the figure itself, and it agrees: 50, one point behind GPT-5.6 Luna (max, 51) and GLM-5.2 (max, 51). The community report was accurate. The figures now sit under ## Benchmarks above, sourced to AA (source).

Sources

• Release capture (source)
• Hugging Face — deepseek-ai/DeepSeek-V4-Flash-0731
• Artificial Analysis — DeepSeek V4 Flash
• TechTimes