DeepSeek V4.1 Flash
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DeepSeek | 1,048,576 token context | $0.04/M input, $0.68/M output. DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision…DeepSeek | 1,048,576 token context | $0.04/M input, $0.68/M output. DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision ExpOpens in new tab. It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 ProOpens in new tab on performance, speed, and task completion time.
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Captain's notes
Notes from 2026-10-13
DeepSeek V4.1 Flash is a 552B sparse mixture-of-experts model from DeepSeek, built on a novel Causal Encoder-Decoder architecture that activates only 8B parameters on input and 16B on output, keeping per-token costs unusually low for its scale. Native vision support and aggressive KV cache compression make it a practical choice for multi-step agents and long-context workloads. Open weights are available, and the API price sits at the budget end of the frontier.
Known for
- Asymmetric activation (8B prefill, 16B decode) cuts compute cost relative to total model size.
- KV cache footprint of ~890 bytes per token, roughly a quarter of the previous Flash generation.
- Native multimodal architecture: vision trained jointly with text from day one, not bolted on.
- Controllable reasoning effort (1–100 integer) lets you trade accuracy for inference cost.
- Designed for coding, computer-use agents, and long-horizon multi-step task completion.
Watch out for
- At 552B total parameters, running this locally demands serious hardware; most users will hit the API.
- The CED architecture is new and largely untested in production outside DeepSeek's own benchmarks.
- Output tokens cost roughly 16× more than input tokens, so verbose completions add up quickly.
Prompts to try
Multi-step terminal agent
You have access to a bash shell. A user has a Python project where `pytest` exits with code 1 but prints no useful error. Walk through a complete diagnosis: check the pytest version, inspect conftest files, run with verbose flags, and propose a fix. Show each command and explain what you expect it to reveal before running it.
Analyse a screenshot
I'm attaching a screenshot of a web application's dashboard. Identify any UI elements that look broken or misaligned, note what data is being displayed and whether the layout makes it easy to scan, and suggest two specific improvements a front-end developer could implement in under an hour.
Long-context contract review
Here is a software services agreement. Identify every clause that limits liability or indemnification, flag any that are one-sided in favour of the vendor, and summarise the termination conditions. Present your findings as a numbered list with the relevant section reference beside each point.
Compare with
- DeepSeek Flash LatestAPI-only model1,048,576 token context · $0.04/M input, $0.68/M output
- DeepSeek V4 Flash 0731Open-weights model1,048,576 token context · $0.01/M input, $1.28/M output
- DeepSeek V4 Flash 0423Open-weights model1,048,576 token context · $0.09/M input, $0.18/M output
- DeepSeek V4 Pro 0813Open-weights model1,048,576 token context · $1.32/M input, $3.96/M output
More text models from deepseek
Questions people ask
- Are the weights open and can I run DeepSeek V4.1 Flash locally?
- Yes, weights are published on Hugging Face under deepseek-ai/DeepSeek-V4.1-Flash. With 552B total parameters you will need substantial hardware to run it locally; most people will use the API instead.
- What does DeepSeek V4.1 Flash cost and what is its context window?
- The API is priced at $0.04 per million input tokens and $0.63 per million output tokens, with a context window of 1,048,576 tokens (roughly one million).
- How does V4.1 Flash differ from the rest of the V4.1 family?
- V4.1 Flash is the cost-efficient tier, using the new CED architecture to keep active parameters low and compress KV cache aggressively; DeepSeek reports it exceeds V4 Pro on performance, speed, and task completion despite the lower per-token cost.
Captain's verdictA genuinely novel architecture that earns its 'Flash' label: cheap per token, built for agents, and open if you have the hardware.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
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Updated Oct 22, 2026
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Also ranked: #1 Hottest Text / LLM model · #1 Hottest Text / LLM text model
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DeepSeek V4.1 Flash Popularity Trend
Heat score since 10 Sep 2026
Heat score
9,552°
This week
▲
38.4%
9,509° then, 9,552° now
This month
▲
832%
9,260° then, 9,552° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+2,124°
OpenRouter tokens
119,552,466,382,730 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 11,955,246 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+428°
Adoption rate
About 516,730 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 9,552°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.11,955,246 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+2,124°9,124°running total
- 3Add the adoption rate.About 516,730 downloads a day right now, measured between two readings, at a quarter of the weight.+428°9,552°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,552°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
DeepSeek V4.1 Flash is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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