DeepSeek V4 Flash: Specs, Pricing, and What V4.1 Changed
DeepSeek V4 Flash came out in April 2026 as DeepSeek's fast, cheap AI model, and less than five months later it's already been swapped out. Since September 10, if your app asks DeepSeek's API for V4 Flash, it gets the newer V4.1 Flash instead, at V4.1 prices.
We have a practical reason to care, because Lindy switched most of its AI agents to V4 Flash in May and June, and the cost of that traffic fell by about 90%.
Everything below was checked against DeepSeek's own pages, so you'll know exactly which model you're getting and what it costs.
What is DeepSeek V4 Flash?
DeepSeek V4 Flash is the smaller, cheaper model in DeepSeek's V4 family. It's an open-weight mixture-of-experts (MoE) model with 284 billion total parameters, 13 billion of them active per token, and a 1-million-token context window.
DeepSeek released it as a preview on April 24, 2026 alongside the much larger V4 Pro, and pitched Flash as the "fast, efficient, and economical choice." The same note says its reasoning closely approaches V4 Pro and matches Pro on simple agent tasks.
Think of the MoE design as a huge office with a small shift on duty. All 284B parameters live in the building, but only about 13B clock in for any given token, which is why Flash answers quickly and bills cheaply.
It was built with coding agents in mind, too. DeepSeek's launch note names Claude Code, OpenClaw, and OpenCode as agents V4 already worked with on day one.
DeepSeek retired V4 Flash on its API on September 10, 2026. The legacy name deepseek-v4-flash still works for now, and DeepSeek's pricing page says those requests are served by DeepSeek-V4.1-Flash and billed at the Flash price.
The open weights are still on Hugging Face under the MIT license, for both the preview and the 0731 release.
DeepSeek V4 Flash specs and release timeline
Two open-weight builds of V4 Flash are on Hugging Face. According to DeepSeek's change log, the official 0731 build "keeps the same model architecture and size" as the preview and "was only re-post-trained."
V4.1 Flash replaced it on September 10 with nearly twice the backbone parameters and native image input:
The long context is cheap because of how V4 handles attention. The V4 Flash model card describes a hybrid of Compressed Sparse Attention and Heavily Compressed Attention, built to cut compute and memory at 1M tokens.
DeepSeek puts numbers on that for the bigger sibling: at a 1M-token context, V4 Pro needs 27% of the single-token inference FLOPs and 10% of the KV cache that V3.2 needed. The V4 technical report has the full architecture.
The 0731 build added one more upgrade. Its model card says it ships with a speculative decoding module (DSpark) attached, which inference servers can switch on with a single flag.
Release timeline
- April 24, 2026: V4 preview launches with V4 Pro (1.6T total, 49B active) and V4 Flash (284B total, 13B active), both open-weight and both on the API.
- July 24, 2026: the old deepseek-chat and deepseek-reasoner names shut off. At launch, DeepSeek said both were routing to V4 Flash's non-thinking and thinking modes.
- July 31, 2026: DeepSeek-V4-Flash-0731 becomes the official V4 Flash release (in public beta on the API), with stronger agent scores and native OpenAI Responses API support.
- August 13, 2026: V4 Pro reaches general availability, both models get low, high, and max thinking effort, and peak and off-peak pricing starts on August 16.
- August 21, 2026: V4-Flash-Vision-Exp adds image input as an experimental API model.
- September 10, 2026: V4.1 Flash launches, and V4 Flash and V4-Flash-Vision-Exp are retired on the API.
What changed in DeepSeek V4.1 Flash
V4.1 Flash is a new architecture wearing a familiar name. DeepSeek's V4.1 announcement calls it "the smallest model in our new architecture family," and it nearly doubles the backbone parameter count while shrinking what runs on the input side.
- Bigger model, fewer active input parameters: it's a 552B-parameter MoE. Per the V4.1 Flash model card, its Causal Encoder-Decoder design activates 8B parameters per token while reading input and 16B while writing output.
- Native image input: text and images go in together, and text comes out. That folds the Vision-Exp experiment into the main model.
- A much smaller KV cache: DeepSeek says the cache needs 1/4 the HBM and 1/8 the SSD storage of the previous generation.
- A new model name: deepseek-flash. The old deepseek-v4-flash and deepseek-v4-flash-vision-exp names temporarily route to it.
- Better agent scores: on the change log, Terminal-Bench 2.1 went from 82.7 for V4 Flash 0731 to 90.6 for V4.1 Flash.
The cache change is the one your bill feels. DeepSeek's own note points out that "cache-hit charges often account for a large share of agent costs," and DeepSeek credits its more efficient architecture, lighter cache included, for the price cut that took effect September 10.
V4.1 Flash is open-weight under MIT as well, but it's a much heavier download (a 552B backbone against 284B). DeepSeek says it will work with the open-source community on V4.1 inference support, so local tooling is still catching up, and llama.cpp support for V4.1 is still an open pull request.
DeepSeek V4 Flash price: what the API charges today
DeepSeek's API now bills every deepseek-v4-flash call at the deepseek-flash price, so these are the numbers you'll pay today, straight from DeepSeek's pricing page:
Peak hours run 01:00 to 04:00 and 06:00 to 10:00 UTC, Monday through Friday, excluding Chinese public holidays. Every other hour (weekends and Chinese public holidays included) is off-peak at half price, which rewards any batch job you can schedule.
The cache-hit rate is where agent workloads save the most. When your system prompt and tool definitions repeat on every call, those tokens bill at $0.006 per million at peak, which is 2% of the cache-miss price.
Third-party hosts set their own rates: on September 28, 2026, InferenceNet, one of the providers on OpenRouter's V4.1 Flash page, charged $0.035 per million input tokens and $0.29 per million output tokens.
A cheaper host can be a great deal, or it can serve a model that scores differently from the one you tested, which is exactly what Lindy ran into. Check which build and which provider you're paying for before you compare prices.
DeepSeek V4 Flash vs V4 Pro
V4 Pro launched as the flagship and Flash as the budget pick, and DeepSeek's own benchmarks show Flash catching up within three months.
The 0731 model card says Flash 0731 beat V4 Pro (Preview) on every benchmark it lists, despite far fewer active parameters. V4 Pro's August GA release pulled ahead again on Terminal-Bench 2.1, and then V4.1 Flash passed it.
DeepSeek's September 10 announcement leaned into that. It said tests put V4.1 Flash ahead of V4 Pro "on performance, cost, speed & total runtime" and that DeepSeek was phasing out V4 Pro.
The same note said that from 04:00 UTC on September 14, every deepseek-v4-pro request would route to V4.1 Flash until V4.1 Pro launches.
The September 10 entry in DeepSeek's change log now reverses that plan: "in response to user demand," it will keep serving V4 Pro after September 14, "with the billing method remaining unchanged."
The pricing page matches the change log. It still lists deepseek-v4-pro as DeepSeek-V4-Pro-0813 with its own rates, so as of October 5, 2026, you can still call V4 Pro directly.
Our take: unless you have a Pro-specific eval that says otherwise, V4.1 Flash is the better default. It's faster by DeepSeek's account, handles images, and costs about 30% of Pro's output price.
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How to use DeepSeek V4 Flash
You have three ways in, and they differ a lot in effort.
In DeepSeek's chat app
The quickest test is DeepSeek's web chat or mobile app. At the V4 launch, DeepSeek pointed users to the chat's Expert Mode and Instant Mode, and since the August 13 GA release, V4 Pro has been available through Expert Mode.
DeepSeek's change log doesn't say which build sits behind each chat mode today. If you need to know exactly which model answered, use the API.
Through the API
The API takes a two-line change for most apps. Point your client at https://api.deepseek.com (OpenAI format) or https://api.deepseek.com/anthropic (Anthropic format), and set the model to deepseek-flash.
from openai import OpenAI
client = OpenAI(api_key="YOUR_DEEPSEEK_API_KEY", base_url="https://api.deepseek.com")
response = client.chat.completions.create(
model="deepseek-flash",
messages=[{"role": "user", "content": "Summarize this support thread in three bullets."}],
reasoning_effort="high",
)
print(response.choices[0].message.content)
A few details save you a debugging session:
- Thinking is on by default: per DeepSeek's thinking mode guide, the default effort is high, and you can set low, high, or max.
- Temperature and penalties are ignored in thinking mode: sending them won't throw an error, but they have no effect there.
- Limits: 1M-token context, up to 384K output tokens, plus JSON output, tool calls, and the Responses API.
Because the API speaks both OpenAI and Anthropic formats, most AI coding agents can use it after a base URL and key swap. OpenCode is one of DeepSeek's official V4.1 Flash partners.
On your own hardware
The weights are the other half of the appeal. V4 Flash and V4 Flash 0731 are on Hugging Face under MIT, and Hugging Face shows vLLM and SGLang launch commands for both.
Plan for serious hardware. The 0731 card's example serves the model with vLLM on a single node with four NVIDIA GB300 GPUs, with DSpark speculative decoding switched on.
For smaller rigs, Unsloth's GGUF builds of V4 Flash 0731 run in llama.cpp and similar tools. They range from 82.5 GB for the smallest 1-bit quant to 162 GB for the Q8 build, which Unsloth calls "full precision lossless."
Ollama users should know that Ollama's own library entry now says "deepseek-v4-flash was retired on August 27, 2026." The Unsloth repo page on Hugging Face shows the workaround, which pulls the GGUF straight from Hugging Face: ollama run hf.co/unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-Q4_K_XL.
Ollama does list V4.1 Flash, but only as a cloud model (deepseek-v4.1-flash:cloud), so it runs on Ollama's servers rather than yours.
If you'd rather use NVIDIA's stack, NVIDIA NIM lists deepseek-v4-flash as "a 284B MoE model with 1M-token context optimized for fast coding and agents," with partner endpoints and self-hosted deployment options.
Hardware rule of thumb: the GGUF you pick has to fit in memory. Plan for combined RAM and VRAM above the file size (82.5 GB at the very least), plus headroom for your context window.
Is DeepSeek V4 Flash good enough? What Lindy's switch showed
For Lindy's workloads, yes: V4 Flash won Lindy's offline evals and then held up with real users.
Lindy is an AI teammate that lives in your company's Slack, so its model sits behind a long list of small jobs like emails, calendars, meeting prep, and follow-ups.
Teams weighing Claude alternatives often test several models before moving production traffic, and Lindy did the same. Staff software engineer Bruno Škvorc covers the full process in Lindy's write-up on migrating from Claude to DeepSeek.
In May and June, Lindy "moved most managed-agent model traffic, including Claude/Sonnet-backed paths and the remaining Gemini/Google paths, to DeepSeek v4 Flash on Atlas Cloud." On the migrated traffic, "inference costs fell by about 90%."
The write-up credits four steps:
- Offline evals first: the team ran evals across several candidates, including "GLM5.1, Kimi K2.6, and DeepSeek v4 Flash," and V4 Flash "won for the workloads we cared about."
- The provider changed the results: "Annoyingly, the provider mattered," because some hosts were serving quantized versions of the same model.
- Real users as the final test: an earlier candidate, Kimi K2.5, "did well in offline evals," then "failed the vibe test almost immediately" once it reached a small slice of real usage.
- A slow ramp: the rollout started with a small percentage of users, including internal users, and grew only while online evals and retention held.
The write-up is candid about limits, too. Sonnet "can still run when a user explicitly selects it or a higher-intelligence path needs it," and the team's rule of thumb is to "ask where it is good enough."
Developers outside Lindy have praised V4 Flash as well. One r/opencode thread about the model is titled "DeepSeek V4 Flash is magical."
Early V4.1 Flash reports are more mixed. One r/DeepSeek thread is titled "Tried DeepSeek V4.1 Flash for $10, total failure for me so far."
One caveat before you copy the playbook: Lindy's write-up was last updated June 9, 2026, before DeepSeek's 0731 and V4.1 releases and before Moonshot shipped Kimi K3. Treat it as evidence for the process, and run your own evals on the exact build and host you'll ship.
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How to tell if V4.1 Flash fits your workload
DeepSeek V4 Flash had a short run on DeepSeek's API, and V4.1 Flash now carries the same pitch at lower API prices.
A simple way to run it this week:
- Replay real tasks: take a batch of production prompts and compare V4.1 Flash with your current model side by side.
- Test the host you'll pay for: DeepSeek's API, a third-party router, and your own GPUs can all give different results for the same model name.
- Ramp slowly: send a small slice of live traffic first and watch retention along with your evals.
- Move batch work off-peak: anything that can wait costs half as much outside DeepSeek's peak windows.
If a day-to-day chat assistant is closer to what you need than an API model, start with AI tools like ChatGPT.
Frequently asked questions
Is DeepSeek V4 Flash free?
DeepSeek V4 Flash is free to download and self-host, because its open weights are MIT-licensed on Hugging Face. The API is paid per token: deepseek-v4-flash calls now run on V4.1 Flash at $0.30 per million cache-miss input and $1.20 per million output tokens at peak, half that off-peak.
Is DeepSeek V4 Flash faster than V4 Pro?
Yes, DeepSeek V4 Flash is faster than V4 Pro, according to DeepSeek, which pairs its faster response times with a smaller size (13B active parameters per token against Pro's 49B). DeepSeek also says tests put V4.1 Flash ahead of V4 Pro on speed and total runtime.
Why is DeepSeek V4 Flash so cheap?
DeepSeek V4 Flash is cheap because only 13B of its 284B parameters run for each token, and its compressed attention cuts the memory cost of long context. V4.1 Flash shrinks the KV cache further, and DeepSeek halves prices during off-peak hours.
What are the RAM requirements for DeepSeek V4 Flash?
DeepSeek V4 Flash needs more than 82.5 GB of combined RAM and VRAM to run locally, because that's the size of Unsloth's smallest GGUF build of V4 Flash 0731. The Q8 build is 162 GB, and either one needs extra room for your context window.
Can you still use DeepSeek V4 Flash 0731?
Yes, you can still run DeepSeek V4 Flash 0731 yourself from its MIT-licensed Hugging Face weights. On DeepSeek's own API, the deepseek-v4-flash name now routes to V4.1 Flash, though some third-party hosts, such as OpenRouter, still list the 0731 build.
