DeepSeek V4.1 Flash Goes Live: Stronger, Faster, More Affordable, with Native Multimodal

On September 10, 2026, DeepSeek released V4.1 Flash. The name says Flash, but this is not just a cheaper tier. It is the first member of an entirely new model architecture family to ship, and the first time DeepSeek has put native multimodal vision directly into a mainline model.

The louder signal is what DeepSeek decided to do with V4 Pro. Traffic to the flagship is being routed to V4.1 Flash. A lightweight-tier model taking over flagship traffic is not something you see often in this industry.

What V4.1 Flash changes

V4.1 Flash is a 552B MoE model built on a new Causal-Encoder-Decoder structure. The interesting part is that input and output are asymmetric. Only 8B parameters activate on the input side, and 16B on the output side. Reading is cheap. The compute goes where generation actually happens.

DeepSeek says that with a new pretraining recipe and larger-scale reinforcement learning post-training, V4.1 Flash beats a group of flagship models in benchmarks, V4 Pro included.

Item	Specification
Parameters	552B (MoE)
Architecture	Causal-Encoder-Decoder, asymmetric input/output
Activated parameters	8B input / 16B output
Context window	1M tokens
Max output	384K tokens
Multimodal	Native vision understanding, not an attached module
Thinking mode	Thinking and non-thinking, thinking on by default
Concurrency limit	2,500

Native multimodality deserves a separate note. The earlier V4 Flash Vision Exp bolted vision onto a text model, which meant switching model names to use it. V4.1 Flash folds image understanding into the same model. Text and images enter through one endpoint, with no separate routing. If you want the background on the previous generation’s approach, read our hands-on V4 Flash Vision Exp API test.

KV cache cut to a quarter, and agent costs follow

Anyone running agents is sensitive to cache costs. In multi-turn conversations, history gets re-read constantly, and cache-hit tokens usually dominate the bill.

V4.1 Flash changed the architecture here. Compared with the previous generation, KV cache demand on HBM drops to 1/4 and on SSD to 1/8. DeepSeek’s own comparison puts the cumulative reduction against its first-generation model at 437x.

What that means in practice is that the marginal cost of long, multi-turn tasks falls sharply. Running a tool-calling agent through dozens of turns used to scale your bill roughly linearly with turn count. That curve is noticeably flatter now.

The new API pricing

The new rates took effect at 12:00 on September 10, 2026. DeepSeek kept its peak and off-peak schedule, and off-peak rates are exactly half of peak. Peak hours are weekdays 09:00 to 12:00 and 14:00 to 18:00 Beijing time. Everything else is off-peak.

Prices below are per million tokens, in CNY.

Billing item	Period	deepseek-flash	deepseek-v4-pro
Input (cache hit)	Off-peak	¥0.02	¥0.15
Input (cache hit)	Peak	¥0.04	¥0.30
Input (cache miss)	Off-peak	¥1	¥4.5
Input (cache miss)	Peak	¥2	¥9.0
Output	Off-peak	¥4	¥13.5
Output	Peak	¥8	¥27.0
Concurrency limit	—	2,500	500

Side by side, cached input is about 87% cheaper, uncached input about 78% cheaper, and output about 70% cheaper. The concurrency ceiling goes from 500 to 2,500, a fivefold increase.

If you are tracking how DeepSeek’s pricing has moved across product lines, our V4 Pro permanent price cut writeup is worth reading alongside this one. The motivation behind that cut was different from this one.

Where it lands against other models

First, the provenance of these numbers. DeepSeek did not publish a full benchmark table with the announcement. The scores below come from third-party aggregation and community tracking, not from official materials, so treat them with appropriate caution. Three DeepSeek versions sit alongside four outside rivals, GLM 5.3, Kimi K3, GPT 5.6-Sol and Claude Opus 5, across all 19 tests, so the comparison no longer lives inside an image alone.

Benchmark	V4.1 Flash	V4-Pro 0813	V4-Flash 0731	GLM 5.3	Kimi K3	GPT 5.6-Sol	Claude Opus 5
GPQA Diamond	90.9	92.4	89.9	88.1	92.9	94.1	93.4
HLE	36.8 (39.1*)	42.7*	37.8*	42.0*	43.5	44.5	56.3
Codeforces (Rating)	3471	3348	3289	-	-	-	-
MathArena Apex	65.6	65.3	58.6	-	65.6	-	-
Terminal-Bench 2.1	90.6	87.9	82.7	88.2	88.3	88.8	89.1
Terminal-Bench 3.0	30.0	11.8	7.6	28.3	17.7	34.4	43.3
Terminal-Bench 4.0	31.2	12.4	7.0	37.9	12.6	39.9	51.8
DeepSWE v1.1	74.2	62.7	54.4	66.9	67.5	73.0	74.0
ProgramBench	20.3	15.5	-	19.0	17.5	23.0	37.0
NL2Repo-Bench	65.4	61.5	54.2	58.0	58.0	56.8	75.3
CyberGym	88.1	83.3	76.7	84.5	80.0	84.5	-
SEC-Bench Pro	62.8	56.4	30.9	-	-	74.3	-
ExploitGym	15.3	5.4	1.8	15.0	-	33.7	22.1
HLE (w/tools)	63.9	60.0	51.5	62.5	59.8	-	63.6
Automation-Bench	54.8	43.2	37.7	48.8	46.7	45.8	50.3
Agents’ Last Exam	31.8	25.7	25.2	28.5	27.6	26.7	28.6
Chartography (w/tools)	78.9	-	-	-	68.1	79.9	84.0
BabyVision (w/tools)	89.6	-	-	-	85.7	88.9	94.1
ZeroBench-main (w/tools)	49.0	-	-	-	41.0	53.0	52.0

Scores marked with * come from text-only runs; - means no public figure exists for that row. The numbers are compiled from third-party evaluations and community threads and serve only as a rough cross-model reference.

On agentic and coding work, V4.1 Flash sits near the front of its tier. It posts 90.6 on Terminal-Bench 2.1, 88.1 on CyberGym, a 3471 Codeforces rating, and 63.9 on HLE with tools, which edges past Claude Opus 5 at 63.6.

But the weak spot is real. On HLE without tools it scores 36.8, or 39.1 on the text-only slice, well below GPT-5.6 Sol at 44.5 and Claude Opus 5 at 56.3.

The positioning is clear. This is a model that is very good at driving tools, not a new reasoning champion. If you plan to throw closed-book reasoning problems at it, calibrate your expectations.

Speed is the other advantage. Multiple independent testers measured sustained output throughput between 300 and 355 tokens per second, with individual peaks reported above 500. In one head-to-head comparison, V4.1 Flash ran at 355 tokens/s against V4 Pro’s 63 tokens/s, and time to first token fell from 766ms to 178ms.

How to integrate

Integration is cheap. Change the model name and you are done.

Use deepseek-flash as the model name. The base URL stays at https://api.deepseek.com, with the Anthropic-format endpoint at https://api.deepseek.com/anthropic. The old names deepseek-v4-flash and deepseek-v4-flash-vision-exp still work, but they now serve V4.1 Flash underneath and bill at Flash rates.

Feature	Support
JSON Output	Yes
Tool Calls	Yes
Responses API	Yes
Anthropic API	Yes
Chat prefix completion	Yes (Beta)
FIM completion	Non-thinking mode only
Vision understanding	Yes

One item needs planning ahead. After 12:00 Beijing time on September 14, 2026, and until V4.1 Pro launches, every request sent to deepseek-v4-pro will be routed to V4.1 Flash and billed at Flash rates.

If you have production traffic pinned to V4 Pro, the next few days are your last validation window. Run a comparison against real traffic and confirm output stability before you migrate. Prompts you tuned on V4 Pro need fresh testing on V4.1 Flash. Do not assume behavior carries over.

Weights and the technical report are out, and downloadable from Hugging Face. DeepSeek also notes that teams with large-scale deployment needs and the hardware for it, meaning roughly 2k GPUs plus a storage cluster, can reach out directly.

What developers actually say

The Hacker News thread pulled 415 points and 217 comments, and r/singularity ran its own discussion. Reading through both, the praise and the complaints are unusually specific.

The positive feedback centers on cost-effectiveness. One developer working in the agent industry said V4.1 Flash competes directly with Gemini Flash, produces better quality on agentic tasks, and costs 20 to 30 percent of the per-task price. Another used it to bulk-convert a pile of .metal kernels to .cu, reporting that it needed less steering than Opus, wrote better comments, and ran at 300 to 400 tokens per second.

The complaints are concrete too. One developer described agents getting dragged into retry loops when a tool call fails, and said the fix is writing explicit recovery instructions into error messages. Another flagged unstable language following, where an English prompt sometimes returns a Chinese reasoning chain, and a question about a local German topic came back answered in German. Others want a proper medium setting for reasoning effort, since the dial effectively jumps from low straight to high and max, and high burns tokens fast.

The most contested decision is routing Pro traffic to Flash. Opponents argue that if someone has validated a workflow on V4 Pro, silently switching the model under production is not acceptable. Supporters counter that this lab has swapped the underlying model at every release since V3.2, and that anyone needing long-term stability should host the open weights themselves.

Reddit landed in a similar place, impressed by the efficiency but skeptical that a Flash-tier model genuinely outclasses something like Kimi K3 in day-to-day use. That skepticism is fair. Benchmarks oversample tidy puzzles and undersample messy real work.

One more thing that gets overlooked. The previous V4 Flash was 284B, small enough for many people to run locally. V4.1 Flash grows to 552B, and the local deployment bar rises with it.

Frequently asked questions

When was V4.1 Flash released? September 10, 2026.

What do I need to change to call it? Set the model name to deepseek-flash. Nothing else changes, and it stays compatible with the OpenAI SDK.

Does it accept images? Yes, through native vision understanding rather than an attached module. Text and images share one model endpoint.

How large is the context window? 1M tokens, with up to 384K tokens of output.

Is it better than V4 Pro? On agentic and coding tasks, DeepSeek says it comprehensively surpasses V4 Pro, and is retiring the flagship on that basis. On pure reasoning it still trails reasoning-first models such as Opus 5 and GPT-5.6 Sol.

How much cheaper is it now? Off-peak output is ¥4 per million tokens against V4 Pro’s ¥13.5, roughly a 70% cut. Cached input falls from ¥0.15 to ¥0.02.

Can I run it locally? The weights are open, but at 552B the memory requirements put it out of reach for personal hardware.

Is there a free way to try it? The API is metered. For a quick trial, use the entry point below.

Try it online

If you would rather not wire up an API, you can use it directly on the official DeepSeek site at deepseek4.hk. This route is already running V4.1 Flash, with the 1M context window, vision understanding, and tool calls all available. No API key required, just open it and start chatting.

Our DeepSeek usage guide walks through the fastest path to get productive. If you are still sorting out entry points and account details, the official website guide covers that in more depth. And for the wider V4.1 story, our funding, price cut, and multimodal roundup works well as background reading.