DeepSeek V4-Flash-0731: what changed, and how to use it over the API

Aug 1, 2026

On July 31, DeepSeek shipped V4-Flash-0731 — not a bigger model, a retrained one. Same 284B-total / 13B-active architecture, same 1M-token context, same price bracket — but re-post-trained for agent work, and the reported jump is unusual: DeepSeek says the new Flash now beats its own larger V4-Pro-Preview on every one of the nine agent benchmarks it publishes.

If you use the Core Pool, there is nothing to migrate: the model id is still deepseek-v4-flash, and requests already serve the new build. Unlimited, flat-rate access starts from $14.99/mo ($12.74/mo billed annually) — live pricing on /pools.

The retrain, measured

Same architecture, new post-training. This is DeepSeek’s own reported before/after (vendor harness — no independent verification of these specific numbers yet):

V4-Flash-0731V4-Flash preview (the previous build, April 2026)

Full vendor-stated table for the 0731 build, next to the April preview it replaces:

Benchmark	Flash-0731	Flash preview (Apr)
Terminal-Bench 2.1	82.7	61.8
Cybergym	76.7	38.7
Toolathlon-Verified	70.3	49.7
DSBench-FullStack†	68.7	37.0
DSBench-Hard†	59.6	25.8
DeepSWE	54.4	7.3
NL2Repo	54.2	39.4
Agents’ Last Exam	25.2	15.8
AutomationBench Public	25.1	10.8

† DeepSeek-internal test sets; the rest are public benchmarks.

The DeepSWE number is the striking one: the retrain multiplied the preview’s score by seven without touching the architecture. And like the April preview, the 0731 checkpoint is a genuine open-weights release: the weights are published under MIT at deepseek-ai/DeepSeek-V4-Flash-0731 (304B parameters on the repo — the 284B base plus a draft module).

Where that lands among frontier models

The independent signal comes from Artificial Analysis, which measures Flash-0731 at 50 on its Intelligence Index — ten points above the previous Flash. Honest framing: it does not beat Claude Opus 5 (61) or GPT-5.6 Sol (59) — nothing near this price does. What it does is land within seven points of Kimi K3 (57, the highest open-weights score on the index) from the budget model of the field — with a reference per-token price of $0.28 per 1M output tokens, roughly 90× below Claude Opus 5’s $25:

Served in a CheapestInference poolReference frontier models

Same data as a table, with list output prices alongside for scale — each model at its best published configuration (max effort where the index reports one); the median across all models Artificial Analysis tracks is 17:

Model	AA Intelligence Index	List output $/1M	On CheapestInference
Claude Opus 5	61	$25.00	—
Claude Fable 5*	60	$50.00	—
GPT-5.6 Sol	59	$30.00	—
Kimi K3	57	$15.00	Flagship Pool
Claude Opus 4.8	56	$25.00	—
GLM 5.2	51	$4.40	Frontier Pool
DeepSeek V4-Flash-0731	50	$0.28	Core Pool
MiniMax M3	44	$1.20	Frontier Pool

* Yes, one point below Claude Opus 5, even though Anthropic positions Fable 5 above Opus in capability. The index scores what AA actually evaluates: Fable 5 is measured in its “Adaptive Reasoning, Max Effort, Opus 4.8 Fallback” serving configuration — the variant with Anthropic’s additional dual-use safeguards — while Opus 5 runs at plain max effort. Independent index, published configurations, taken as-is.

Four of the eight models on that chart are served here on flat-rate subscriptions — and the 0731 retrain just moved the cheapest pool of the three into frontier territory.

V4-Flash-0731 vs Claude Opus 4.8, GPT-5.6 Luna, and Gemini 3.6 Flash

Is DeepSeek V4-Flash-0731 better than Claude Opus? On raw capability, no — and we won’t pretend otherwise. The strongest competitor in DeepSeek’s own release table is Claude Opus 4.8, and Opus wins every one of the nine shared benchmarks. What the table actually shows is how little it wins by, against a model priced roughly 90× higher per output token at list:

Benchmark (DeepSeek’s release table, vendor-run)	V4-Flash-0731	Claude Opus 4.8
Terminal-Bench 2.1	82.7	85.0
Cybergym	76.7	83.1
Toolathlon-Verified	70.3	76.2
DSBench-FullStack†	68.7	71.6
DSBench-Hard†	59.6	71.7
DeepSWE	54.4	58.0
NL2Repo	54.2	69.7
Agents’ Last Exam	25.2	25.7
AutomationBench Public	25.1	27.2
Reference output price, per 1M tokens	$0.28	$25.00

On Agents’ Last Exam the gap is half a point — effective parity. On Terminal-Bench 2.1 it is 2.3 points. NL2Repo and the (internal) DSBench-Hard are where the distance stays wide.

Against its actual price peers, the independent picture flips. Per Artificial Analysis: Flash-0731 sits one point behind GPT-5.6 Luna (50 vs 51 at max effort) with a cost per task roughly 60% lower — even after OpenAI’s price cut — ties Gemini 3.6 Flash (50), and lands on AA’s Pareto frontier for Intelligence vs Cost per Task: at this intelligence level, nothing tracked is cheaper per task.

Here is that frontier drawn out — intelligence against list output price. A model is on the frontier when nothing tracked is both smarter and cheaper; everything below-right of the line pays more for less. The 0731 retrain moved Flash onto it, and pushed MiniMax M3 off:

Served in a CheapestInference poolReference frontier modelsPareto frontier

Three of the five models on that frontier — Flash-0731, GLM 5.2 and Kimi K3 — are served here on flat rate. This chart is a snapshot of the frontier’s cheap end; the monthly-updated, full-field version (with cost-per-task data and edition history) lives in our LLM Pareto Frontier report.

And on a flat-rate subscription the per-token column stops mattering altogether: a Core Pool block is the same price whether your agent burns one million tokens or one billion.

Call it

curl:

curl https://api.cheapestinference.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": "Hello"}]}'

OpenAI SDK (Python):

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cheapestinference.com/v1",
    api_key="sk-...",  # your subscriber key
)

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Fix the failing test in this repo..."}],
)
print(response.choices[0].message.content)

No account yet? Register, subscribe to the Core Pool, and mint a key at cheapestinference.com/keys.

Run it in Claude Code

The API also speaks the Anthropic Messages format, so a retrained agent model drops straight into the most popular coding agent:

export ANTHROPIC_BASE_URL="https://api.cheapestinference.com/anthropic"
export ANTHROPIC_AUTH_TOKEN="sk-..."   # your subscriber key
export ANTHROPIC_MODEL="deepseek-v4-flash"
export ANTHROPIC_SMALL_FAST_MODEL="deepseek-v4-flash"

Start claude as usual — every request runs on Flash-0731 with no per-token meter. The same key works in Cline, Roo Code, Continue, and anything that accepts a custom OpenAI base URL (setup guides).

Why this release matters for flat-rate users

Agent workloads are exactly where per-token bills explode: an agent re-sends its growing context on every tool call, and iteration count — not task value — drives the invoice. A model that is suddenly much better at agent work makes that math worse per-token and better flat-rate: more capable loops, same fixed monthly price. We ran the full per-token vs. flat break-even math in Unlimited DeepSeek: what a flat monthly subscription changes — every row of that table just got more favorable, because the same subscription now serves a stronger model.

The trade-offs, as always: usage is unlimited in tokens during your reserved 8-hour blocks, each subscription runs one request at a time (fair use), and outside your blocks the key doesn’t serve. Full details: DeepSeek V4 Flash API docs · Plans & Limits.

Check live Core Pool availability →

CheapestInference serves Kimi K3 (Flagship Pool), Kimi K2.7, GLM 5.2, and MiniMax M3 (Frontier Pool) and DeepSeek V4 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.