DeepSeek V4 Flash 0423
9192°
<deepseek-v4-flash>
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Guides for this model
Best local LLM by use case (October 2026): the right open model for every job
How much VRAM do I actually need for local AI?
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
DeepSeek | 1,048,576 token context | $0.09/M input, $0.18/M output. DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and high-throughput workloads, while maintaining strong reasoning and coding performance. The model includes hybrid attention for efficient long-context processing. Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is well suited for applications such as coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are important.
Captain's notes
Notes from 2026-10-13
DeepSeek V4 Flash is a 284B-parameter Mixture-of-Experts model from DeepSeek, with only 13B parameters activated per token, built for fast inference and high-throughput workloads at a very low price. Its hybrid attention architecture handles a genuine one-million-token context window efficiently. It suits developers who need a capable reasoning and coding model without paying frontier-model rates.
Known for
- 1M-token context window with efficient hybrid attention, not just a nominal limit.
- Only 13B parameters activated per inference — fast and cheap to run.
- Adjustable reasoning effort: standard, high, or maximum (xhigh) modes.
- Strong coding and reasoning performance relative to its inference cost.
- Open weights available on Hugging Face for self-hosting.
Watch out for
- On pure knowledge tasks and the most complex agent workflows, it trails its sibling DeepSeek V4 Pro.
- 284B total parameters means self-hosting requires serious hardware; check VRAM requirements before committing.
- Weights use FP4 for MoE experts and FP8 elsewhere — not all inference stacks handle mixed precision cleanly.
Prompts to try
Debug a tricky async bug
Here is a Python async function that occasionally raises a RuntimeError: 'Task attached to a different loop'. The function fetches URLs concurrently using asyncio.gather inside a class that creates its own event loop in __init__. Explain exactly why this happens and rewrite the class so it works correctly from both synchronous and async calling contexts.
Plan a multi-step agent
Design a minimal agent loop in Python that uses tool calls to answer the question: which of the last five US presidents had the highest average approval rating? Define the tools the agent needs, show the loop logic, and explain how the agent decides when it has enough information to stop calling tools and return a final answer.
Reason through a logic puzzle
Five engineers join a company in five consecutive years. Alex joined before the person who uses Rust. The Go developer joined third. Morgan joined immediately after the Python developer. The C++ developer joined last. Jamie joined second. Who uses which language, and in what order did they join? Show your reasoning step by step.
Compare with
- DeepSeek V4.1 FlashOpen-weights 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 Pro 0813Open-weights model1,048,576 token context · $1.32/M input, $3.96/M output
- DeepSeek V4 Pro 0423Open-weights model1,048,576 token context · $0.96/M input, $1.91/M output
More text models from deepseek
Questions people ask
- Are the weights open and can I run DeepSeek V4 Flash locally?
- Yes, the weights are published on Hugging Face under deepseek-ai/DeepSeek-V4-Flash. With 284B total parameters in FP4/FP8 mixed precision, you will need substantial GPU memory to run it locally — check the VRAM guide before downloading.
- What does DeepSeek V4 Flash cost and what is its context window?
- Via API it is priced at $0.09 per million input tokens and $0.18 per million output tokens, with a context window of 1,048,576 tokens.
- How does DeepSeek V4 Flash differ from DeepSeek V4 Pro?
- Flash activates 13B parameters per token versus Pro's 49B, making it faster and cheaper; Pro has 1.6T total parameters and pulls ahead on knowledge-heavy tasks and the most demanding agent workflows, while Flash-Max (maximum reasoning mode) can approach Pro-level reasoning when given a larger thinking budget.
Captain's verdictA genuinely cheap, genuinely long-context reasoning model — the open weights make it worth a look before you reach for a closed alternative.
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.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #12 Hottest Text / LLM model · #12 Hottest Text / LLM text model
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DeepSeek V4 Flash 0423 Popularity Trend
Heat score since 24 Apr 2026
Heat score
9,192°
This week
▲
24.0%
9,163° then, 9,192° now
This month
▲
169%
9,060° then, 9,192° 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.
+1,844°
OpenRouter tokens
14,026,338,867,497 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, 1,402,633 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+348°
Adoption rate
About 43,432 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 9,192°, 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.1,402,633 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.+1,844°8,844°running total
- 3Add the adoption rate.About 43,432 downloads a day right now, measured between two readings, at a quarter of the weight.+348°9,192°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,192°
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 Flash 0423 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, check its heat score to see how it is trending, and open it on PirateDiffusion or BitVector to try it.
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