DeepSeek’s smallest new model out-scores the V4-Pro it replaced, needs about a quarter of the cache, and now ships with official Codex and Claude Code integrations. SiliconFlow, notably, has not switched it on yet.
DeepSeek released DeepSeek-V4.1-Flash on 10 September 2026. It is the smallest model in a brand-new architecture family, which sounds like a downgrade right up until you read the benchmark table. On agentic and coding work this “small” model beats V4-Pro, the flagship DeepSeek shipped in August.
Two other things landed in the same release note, and both matter more if you are the one paying. Prices came down. And DeepSeek quietly scheduled a date, 14 September, after which every request still pointed at deepseek-v4-pro gets served by V4.1 Flash instead.
There is a third thing, and for most Malaysian teams it is the practical one. The two coding agents you are probably already paying for, OpenAI’s Codex and Anthropic’s Claude Code, now have officially documented routes onto this model. We will get to those in detail, because that is where the money is.
What DeepSeek actually shipped
| Released | 10 September 2026. Pricing took effect at 12:00 Beijing time the same day. |
| Architecture | 552B-parameter MoE, and DeepSeek’s first model built on its Causal Encoder-Decoder design |
| Active parameters | 8B active on input, 16B on output. Asymmetric on purpose. |
| Context | 1M tokens in, up to 384K tokens out |
| Modality | Text and image, trained together from the start rather than bolted on afterwards |
| Licence | MIT, weights on Hugging Face, technical report published |
| API model name | deepseek-flash |
| Also new | Controllable reasoning effort, on a scale from 1 to 100 |
The previous generation is already gone. V4-Flash and V4-Flash-Vision-Exp are retired. Their old model names still resolve, but they now hand you V4.1 Flash. For total size, Mia (@miaai_lab) put the weights at 510GB, roughly 204% larger than the V4-Flash-Vision-Exp it replaces.
The architecture trick, and why it changes your bill
Most large models are symmetric. Reading your prompt and writing the answer use roughly the same machinery. V4.1 Flash breaks that symmetry: a 20-layer causal encoder builds one global representation of the input, then a 20-layer decoder only has to handle its own local window. DeepSeek’s technical report calls the result a near-halving of prefill compute.
The interesting consequence is in the cache. DeepSeek says the global KV cache now needs about 890 bytes per token, against V4-Flash’s 3,514. HBM demand drops to a quarter, SSD demand to an eighth, and the cache ends up 437 times smaller than DeepSeek’s very first model.
Why care about cache size? Because of how agent loops spend money. An agent reads a long context, calls a tool, reads again, calls again. The same tokens get re-sent dozens of times, and in most agent workloads the cached input is the bill. DeepSeek built the architecture around that, then priced it in directly. Which brings us to the invoice.
The pricing, and the peak-hour trap most write-ups skip
| Per 1M tokens | Off-peak (USD) | Peak (USD) | Off-peak (CNY) |
|---|---|---|---|
| Input, cache hit | $0.003 | $0.006 | 0.02 yuan |
| Input, cache miss | $0.15 | $0.30 | 1.00 yuan |
| Output | $0.60 | $1.20 | 4.00 yuan |
The gap between the first two rows is the whole ballgame. A cache hit costs 50 times less than a miss. Any workload with a stable system prompt, a reusable document context or a repeated tool schema should be architected around caching before anything else gets optimised.
Now the part that catches people out. Peak hours are weekdays 01:00 to 04:00 and 06:00 to 10:00 UTC. In Malaysia, at UTC+8, that is 9am to 12pm and 2pm to 6pm, which is precisely when most teams do their heaviest work. Everything else, weekends included, bills at half price. Against the old V4-Flash rates, that works out to a 60% cut on cached input, 33% on uncached input and 11% on output.
DeepSeek’s own price table, with the peak window stated in Beijing time. Beijing and Malaysia share UTC+8, so the window is identical here. The same notice names Tencent WorkBuddy as an official partner whose China edition went live on day one with a two-week limited discount, via Qianjiang Evening News.
The date that matters: 14 September
Buried in the release note is the sentence production teams need to act on.
“Starting at 04:00 UTC on Sept 14, 2026, all deepseek-v4-pro requests will route to V4.1-Flash at V4.1-Flash rates. This will continue until V4.1-Pro launches.”
Three model IDs that teams pinned this year, deepseek-v4-pro, deepseek-v4-flash and deepseek-v4-flash-vision-exp, now return, or will shortly return, a model none of them were evaluated against. DeepSeek frames that as good news, and for most workloads it will be. If your monitoring thresholds, prompt tuning and failure handling were calibrated to V4-Pro’s specific habits, you still want to re-run your own evals before the switch lands.
The DeepSeek API release note. Set your model to deepseek-flash, the legacy names route there temporarily, and the official partner line names WorkBuddy, including CodeBuddy, alongside OpenCode.
What the benchmarks show, and where they stop being useful
On DeepSeek’s own table, at maximum reasoning effort, V4.1 Flash beats V4-Pro on most agentic and coding measures.
| Benchmark | V4.1 Flash | V4-Pro 0813 | Claude Opus 5 |
|---|---|---|---|
| Terminal-Bench 2.1 | 90.6 | 87.9 | 89.1 |
| DeepSWE v1.1 | 74.2 | 62.7 | 74.0 |
| CyberGym | 88.1 | 83.3 | not listed |
| Terminal-Bench 3.0 | 30.0 | 11.8 | 43.3 |
| GPQA Diamond | 90.9 | 92.4 | 93.4 |
| Humanity’s Last Exam | 36.8 | 42.7 | 56.3 |
Read that honestly. V4.1 Flash is genuinely strong where agents live: terminal work, software engineering, security. It is clearly behind on knowledge-heavy reasoning, and on the hardest agentic benchmark in the table Opus 5 wins by a wide margin. There is also a harness effect that vendor tables tend to hide. DeepSeek’s own report shows DeepSWE v1.1 swinging between 65.5 and 74.2 for the same model under a different agent framework.
The full vendor table, including the columns we left out: V4-Flash 0731, GLM 5.3, Kimi K3 and GPT-5.6-Sol.
Independent measurement is arriving too. Artificial Analysis scores V4.1 Flash at Reasoning, Max Effort, 40 on its Intelligence Index, 6th of 113 models tracked, at 194.3 output tokens per second (5th) and $0.27 of cost per index task (19th). It also notes the model is fast and reasonably priced but “very verbose”, spending 250M output tokens to complete the index. Verbosity is not a rounding error when you are billed per output token.
Artificial Analysis, independent of DeepSeek: intelligence 6th, speed 5th, cost 19th, verbosity 32nd of 113 models.
What people actually said
DeepSeek’s launch thread pulled 4.1 million views and 25,000 likes in a day, which is not unusual for a DeepSeek release. The tone this time was.
DeepSeek’s own launch post, with the headline benchmark spread.
Thomas Wolf (@thom_wolf), co-founder of Hugging Face, warned against reading too much into the name: “don’t get distracted by all the hedging words in its name (‘flash’, minor version): seems like DeepSeek V4.1 Flash is a major update.” Nik (@ns123abc) went straight to the arithmetic “at ~86x cheaper cost per million tokens running at 420-507 tok/s”, against GPT-5.6 Sol and Opus 5 on coding and cybersecurity. Zizheng Pan (@zizhpan) summarised the release in one line: “DeepSeek-V4.1-Flash is now publicly available on App, Web, and API. Our first flagship model with native multimodal support. Faster. More capable. Lower price. Open weights as usual.”
Two reactions stood out for less obvious reasons. Security researcher Nick Mykhailyshyn (@whoareme33) reported the model “found a 0day RCE in handlebars.js v4.7.9 in minutes for just $0.05”, and noted that V4-Pro 0813 needed multiple runs on the same prompt while Flash found it consistently. Zain (@zainhas) flagged the reasoning dial: “oh wow have not seen this before for any model… this complicates things”, reacting to the 1-to-100 controllable effort.
Techmeme’s roundup of the launch window, which is how we found most of the quotes above.
Hacker News was more measured. The thread drew 934 points and 515 comments, and the top item was genuine praise, one commenter calling it “so refreshing to see DeepSeek’s tech report full of juicy details” while a rival lab’s system card was mostly safety boilerplate. Then came the objections, and they are worth repeating accurately because they are about the older Flash builds as much as this one.
The launch thread on Hacker News.
One commenter reported that in the web UI the flash model “has very poor language following abilities”: you can prompt in English and get a Chinese thinking chain and a Chinese answer, with retry landing on the same question roughly half the time. Another widened the complaint to coding harnesses, pointing at V4-Flash 0731’s “infinite loops, hallucinations, premature termination, and invalid tool calls” and arguing the fix is harness design, not the model, because you can validate tool schemas and detect runtime failures around it. A third pushed back that “this never happens on the DeepSeek API” and blamed third-party providers running lower quantisation.
The practical objections, including the harness-design advice that followed them.
Over on r/LocalLLaMA the weight release moved the conversation to hardware. One commenter worked out that the native 4-bit backbone with vision and speculative decoding lands around 310GB, with the remainder in engram parameters, so a 256GB machine could manage a heavily quantised run. Another summed up the mood: “Yay! We have weights! … I couldn’t host this on my 256GB Xeon server.”
Practical local-run arithmetic, which is where the 552B number stops being abstract.
Who has V4.1 Flash today
| DeepSeek API | Live via deepseek-flash |
| WorkBuddy, including CodeBuddy | Live. Named as an official DeepSeek launch partner, with the China edition shipping a two-week limited discount on day one. |
| OpenCode | Live. The other named official partner. |
| OpenRouter | Live, as deepseek/deepseek-v4.1-flash, routed across Fireworks, Parasail, GMICloud, NovitaAI, DeepInfra, io.net and Morph, priced from $0.22 to $0.30 per million input tokens. |
| Other gateways | Vercel AI Gateway, ZenMUX, Nano-GPT, AIHubMix, Requesty and LiteLLM are among the routers already listing it. |
| SiliconFlow | Not yet. Checked 11 September 2026: its model library still tops out at V4-Flash-Vision-Exp from 4 September, with no V4.1 Flash listing. |
OpenRouter’s provider ladder. If you would rather not open a DeepSeek platform account, this is the same model behind a different checkout.
Running V4.1 Flash inside Codex and Claude Code
The part that saves money
If you are on Codex or Claude Code, the odds are you have hit a wall this year. Anthropic’s own pricing page is clear about why: Claude Code is included in every paid plan, and it draws from the same usage pool as Claude chat and Cowork, with a rolling five-hour session window and weekly limits stacked on top for paid plans. Hit either one and you stop until it resets, or you switch on usage credits and keep working at standard API rates. Pro is $17 a month billed annually, $200 up front, or $20 month to month. Max starts at $100.
OpenAI is blunter still. Its pricing page describes Codex access as limited on Free and Go, “higher Codex usage” on Plus, and “maximum Codex tasks” on Pro.
The shape of the problem is the same in both cases. You are renting a quota, and that quota is sized for somebody else’s working pattern. Moving the backend to a pay-per-token model converts it into a bill you can actually reason about, and V4.1 Flash is an unusually cheap one because of the cache economics above. Both vendors now document the switch, and neither of them is pretending you are doing something exotic.
Route A: Claude Code, on DeepSeek’s Anthropic-compatible endpoint
DeepSeek exposes an Anthropic-format API, so Claude Code talks to it directly. No proxy, no patched binary, no third-party router. This is the configuration from DeepSeek’s own integration guide:
export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic
export ANTHROPIC_AUTH_TOKEN=YOUR_DEEPSEEK_API_KEY
export ANTHROPIC_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_OPUS_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_SONNET_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_HAIKU_MODEL=deepseek-flash
export CLAUDE_CODE_SUBAGENT_MODEL=deepseek-flash
export CLAUDE_CODE_EFFORT_LEVEL=max
export CLAUDE_CODE_AUTO_COMPACT_WINDOW=786432Three details are worth knowing before you paste that in. The [1m] suffix selects the 1M-token context variant and it is not cosmetic. DeepSeek also remaps the Claude model names you pass through, which is how the same config can serve both roles: anything beginning claude-opus goes to deepseek-v4-pro, while claude-sonnet and claude-haiku go to deepseek-flash.
And here is the timing catch. Until 12:00 Beijing time on 14 September, that opus mapping still bills at V4 Pro’s price. After it, V4 Pro itself routes to V4.1 Flash and bills at Flash prices, so the entire Claude Code setup lands on the cheap model. That is the same midday 14 September deadline as the reroute above, which is not a coincidence.
Web search works too. DeepSeek’s API supports the Claude Code web search tool natively, though invoking it spends extra tokens summarising whatever it retrieves, so it is not free.
Route B: Codex, via DeepSeek’s official setup script
Codex is a different shape of problem, because it talks to models through the OpenAI Responses API rather than Chat Completions. DeepSeek’s API supports that format natively, and DeepSeek publishes a configuration script that does the whole job for you.
One prerequisite: Codex must have been installed and launched at least once, so that the ~/.codex directory exists. Then, on macOS or Linux:
curl -fsSL https://cdn.deepseek.com/api-docs/codex-deepseek-setup-en.sh -o codex-deepseek-setup.sh
bash codex-deepseek-setup.shOn Windows, the equivalent one-liner is published as a PowerShell script at codex-deepseek-setup-en.ps1 on the same host. Once it launches you pick from a menu: option 1 configures deepseek-flash, option 2 configures deepseek-v4-pro, and option 9 restores your original Codex configuration.
We have deliberately shown it as two steps rather than DeepSeek’s piped one-liner. It is a remote script either way, and downloading it first costs you nothing except the chance to read what you are about to run.
What the script does, and this is the useful part: it backs up ~/.codex/config.toml into ~/.codex/backup-deepseek/, writes a model catalogue to ~/.codex/models.json that tells Codex about the context window, the reasoning levels and the tool-call format, and adds a [model_providers.deepseek] block pointing base_url at https://api.deepseek.com/ with wire_api = "responses". Your MCP servers and project trust settings are left alone. It validates both files before writing anything, and aborts without touching a byte if validation fails.
Confirm it worked the simple way: the startup banner should read model: deepseek-flash. If sessions from your old ChatGPT subscription look like they vanished, they have not. Codex keeps third-party API sessions in a separate history group, and running the script again to pick option 9 brings the originals back.
One hard rule the script enforces on itself, and the reason a lot of older blog posts will break Codex for you: wire_api has to be "responses". A leftover wire_api = "chat" from a 2026 guide stops Codex from starting at all in current versions. If you are pasting config from somewhere else, check that line first.
The caveats, before you move a whole team
This is a backend swap, not a drop-in upgrade, and the honest position is that you should treat it as an eval project rather than a migration.
The harness-dependence finding cuts both ways. If your agent framework was tuned against Claude or GPT behaviour, the same prompts may behave differently, and DeepSeek’s own numbers show a nine-point swing on DeepSWE v1.1 purely from changing frameworks. DeepSeek’s Codex catalogue also disables Codex’s built-in web search, because these models do not support it, so anything depending on that tool changes shape. And the language-following complaints from the thread above are real, if contested, so run your own tests on prompts you actually use rather than on benchmarks.
What makes it worth doing anyway is the arithmetic. A cache hit at $0.003 per million tokens is two orders of magnitude below a cache miss at $0.15. If your agent workflow reuses a system prompt or a document context, which almost every serious one does, then a well-built cache moves most of your traffic into the cheapest row on that table.
What this means for Malaysian teams
Four things, in rough order of what they save.
Shift the heavy jobs out of the peak window. Peak is 9am to 12pm and 2pm to 6pm here, on weekdays. Overnight batch runs, nightly review agents and weekend jobs all bill at half price, and that is a scheduling decision, not an engineering one.
Architect for cache hits, not for cheap tokens. Prompt golf saves fractions of a sen. Keeping a stable prefix in front of your agent saves half your bill or better.
If you are on V4 Pro, re-run your own evals before 14 September. After that date your requests are served by a different model whether or not your code changed. Aggregate benchmarks will not tell you whether your particular workflow still behaves.
You do not need a DeepSeek platform account to try this. OpenRouter lists the same model across seven providers, and your existing card works there. It is a slightly higher per-token price for a much shorter path to a first request.
And for teams that genuinely cannot send source code to a hosted API, the MIT licence and public weights remain the quieter argument. You do not have to run this on a 256GB machine tomorrow, but nothing in the licence stops you from trying.
What we are watching next
DeepSeek has promised a V4.1 Pro without attaching a date, and the obvious question is unanswered: if Flash already beats the old Pro on cost, speed and most agentic benchmarks, what exactly is the new Pro for? Locally, the one to watch is SiliconFlow, which remains the odd one out among the major gateways and is a common route for developers in this region who want a China-friendly endpoint.
We will update this piece when either of them moves.
Sources and further reading
DeepSeek official release note, 10 September 2026
DeepSeek API docs: integrating with Claude Code
DeepSeek API docs: integrating with Codex
DeepSeek API docs: integrating with AI coding tools
Model card and technical report, Hugging Face
Artificial Analysis model page, checked 11 September 2026
OpenRouter model and provider page, checked 11 September 2026
Anthropic pricing and Claude Code usage limits
ChatGPT plans and Codex usage tiers
Hacker News discussion threads 49639090 and 49624603
Techmeme launch-window roundup, 10 September 2026
Qianjiang Evening News via Toutiao, on Tencent WorkBuddy’s day-one integration
Related reading
We run this stack ourselves. Our WhatsApp CRM, AI chatbot, broadcast tool and event RSVP platform were built with agentic AI rather than a dev hire, and that case study walks through what it cost and how it was put together. If you would rather start with the community than with the API, the WorkBuddy AI Agentic Malaysia group is where we share what is working.
Explore Agentic AI Custom app development Join the community
About BD Media
BD Media is the editorial desk of Big Domain, a Malaysian hosting and cloud provider and a Tencent WorkBuddy partner. We write about the AI tools our own team and customers actually run, which is why the pieces on this site tend to include the prices, the deadlines and the parts that did not work.
Have a question about running V4.1 Flash in your own stack, or want a second opinion before you move production traffic? Talk to us.
