DeepSeek
DeepSeek V4.1 Flash
deepseek-v4.1-flash
Total Context
1M
Max Output
384K
Released
N/A
Input$0.2857 / M tokens
Output$1.1429 / M tokens
DeepSeek V4 Flash
deepseek-v4-flashDeepSeek V4 Flash keeps the V4 family’s 1M-token context window but uses a lighter MoE configuration, commonly described as 284B total parameters with 13B activated parameters. The emphasis is throughput: fast inference, lower cost per call, and production workloads that still need long-context handling. It is the better fit when the task volume is high and the workload benefits from V4-style long-context architecture without always requiring the deepest reasoning tier.
Context Window
1M tokens
Maximum Output
384K tokens
Release Date
Apr 24, 2026
Modalities
DeepSeek V4 Flash Pricing
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.2857/M | $1.1429/M | $0.0057/M |
DeepSeek V4 Flash API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Not supported
Knowledge Base
2025-05-01
Endpoint Protocols
Completions APIResponses API
DeepSeek V4 Flash Model Highlights
DeepSeek V4 Flash combines agentic coding, adjustable reasoning effort and efficient long-context processing for demanding development work.
Agentic Coding
Post-training strengthens performance on terminal operation, repository modification and multi-step software-engineering tasks.
Adjustable Reasoning
Low, high and max reasoning levels let developers balance deliberation depth against response time for different workloads.
Long-Context Efficiency
The V4 architecture supports million-token context processing, helping the model work across large codebases and extensive document collections.
DeepSeek V4 Flash Use Cases
DeepSeek V4 Flash is suited to repository engineering, long-document analysis and multi-step automation that combine reasoning with code execution.
Repository Engineering
Inspect dependencies across a codebase, identify affected files and implement coordinated changes for software maintenance tasks.
Long-Document Analysis
Review extensive technical specifications or document collections, connect information across sections and produce structured findings.
Multi-Step Automation
Plan and execute workflows involving terminal commands, code changes and result checks to complete bounded operational tasks.
How to Use DeepSeek V4 Flash via the TokenHub API
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)DeepSeek V4 Flash Benchmarks
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 40.3 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 38.7 |
| Knowledge & Reasoning | ||
| GPQA | Advanced science problem solving | 89.4% |
| HLE | Broad expert-level exam set | 32.1% |
| Coding & Engineering | ||
| SciCode | Scientific coding challenges | 44.9% |
| Terminal-Bench Hard | Hard terminal task execution | 35.6% |
| Instruction Following & Agent Tasks | ||
| IFBench | Prompt constraint adherence | 79.2% |
| AA-LCR | Long-context reasoning | 63% |
| τ²-Bench | Agent workflow tasks | 95.0% |
Metrics sourced from Artificial Analysis
DeepSeek V4 Flash Comparisons
DeepSeek V4 Flash Guides & Articles
DeepSeek V4 Flash FAQ
DeepSeek V4 Flash: capabilities, use cases, limits, and TokenHub guidance.
How is DeepSeek V4 Flash positioned?+
DeepSeek V4 Flash is a DeepSeek model for fast, efficient reasoning and agent work.
Where does DeepSeek V4 Flash add value?+
Best for latency-sensitive applications, agent workflows and high-volume requests, especially when speed and cost efficiency is the priority.
What is DeepSeek V4 Flash's practical edge?+
Key strength: a smaller design with faster, more economical inference and switchable thinking and non-thinking modes.
Which constraint matters most?+
It has less headroom on the hardest reasoning and engineering tasks. For maximum answer quality, consider DeepSeek V4 Pro.
How do I integrate DeepSeek V4 Flash safely?+
Use the exact ID shown by TokenHub; follow your account docs and verify current features.
Ready to use DeepSeek V4 Flash?
Use one API key to access DeepSeek V4 Flash and more AI models through TokenHub.
Media and Discussions
Selected public videos and posts related to this model.
X (Twitter)
DeepSeek ·
🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length. 🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models. 🔹 DeepSeek-V4-Flash: 284B total / 13B active params. https://t.co/n1AgwMIymu
https://pbs.twimg.com/media/HGo9ronbUAAKlRk.jpgView originalOpenCode ·
DeepSeek V4 Flash is now available in OpenCode Zen
View originalOpenModel ·
DeepSeek-V4-Flash Free Event – Quick Setup Guide A lot of people are asking how to configure it. Here’s the fastest way: 1. Base URL → https://api.openmodel.ai/v1 2. Use your OpenModel API key 3. Model name → deepseek-v4-flash Full documentation: https://docs.openmodel.ai/ Still
View originalReddit
DeepSeek V4 Flash is a monster! Cheap & Good, and so fast
Maleficent-Movie-625 ·
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Hey, thought I share it, used to be on Claude a lot, and at work on GPT/Codex via OpenCode. Anyhow Claude sucks and so I went 2 months ago on a path to try things in paralell. And now not so long ago when the Claude Subscription ran out, to fully replace it with Chinese models at home. So, long story short. My main drive is Minimax, with Kimi for understanding screenshots, via copy and paste. But sometimes these get stuck. And the xiaomi model is good, but slow and expensive, deepseek v4 pro is good but slow and innefficient and expensive. So did like two days now ,with the deepseek-v4-flash, and based on that I can recommend it. Crazy fast, 100 to 150 TPS, super good. It is a bit outdated and needs reminding (e.g. context7) on what we use these days, but oh man is it good, helps get stuff unstuck. So if you didn't already, It is probably less than $1 a day of API usage, try it. This is the good stuff, cheap, crazy fast, and good. Fuck claude and anthropic.
DeepSeek V4 Flash is magical
amjadmh73 ·
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I started using this model after seeing positive reviews about it both on Reddit and in X. This is the closest thing to Opus 4.5 since Opus 4.5 (haven't tried Chat GPT 5.5). If you haven't tried it yet and you want a good model with competitive pricing for software engineering, you are missing out. Good luck! ————- EDIT ————- Most people pointed out this model is great at instruction following and implementation. For planning, I should use the pro variant or Claude’s main models (Opus or Sonnet). Thanks for the feedback, folks. r/opencode - DeepSeek V4 Flash via OpenCode Go (https://cf.preview.redd.it/deepseek-v4-flash-is-magical-v0-herrkbjda85h1.png?width=776&format=png&auto=webp&s=d15be98a8a60526762eb1f29018e2da5bf111585) DeepSeek V4 Flash via OpenCode Go
DeepSeek V4 Flash is amazing! (WIP llama.cpp PR #24162)
Lowkey_LokiSN ·
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In case you're not aware already, the DeepSeek V4 series is finally getting supported on llama.cpp with this PR (https://github.com/ggml-org/llama.cpp/pull/24162)! The PR is at a very early stage right now, so only try it if you're consciously willing to experiment out of curiosity and accept severe stability/performance tradeoffs. It runs very slow (5-6 tps), GPU and FA support need work, etc., but it is reliable-enough already for correctness. This is my most anticipated model and I had some time to spare, so I ended up downloading the HF model for DS-V4-Flash and quantizing it myself using the PR(Made a custom 3-bit quant to mimic the full-sized model's tensor layout). And wow! The model perfectly addresses the crucial three pillars for local inference IMO: The model's intelligence is amazing for its size. First time a local model in this size range actually feels comparable to frontier models, and I'm not exaggerating. Fares a lot better against quantization since it's natively an FP4-FP8 hybrid. This is crucial for local deployment and is my primary problem with models like MiniMax M2.7, where I'm not happy even with UD-Q4_K_XL. Incredibly efficient with context window scaling. Consumes way less KV cache size with no flash attention! Qwen 3.5/3.6 series is also a huge hit amongst the local community since it addresses the three pillars above way better than its competitors. However, I feel the DeepSeek model has levelled it up even further, and I predict it will easily dominate the 80-140GB model space for many more months to come. Huge shoutout and thanks to fairydreaming for their relentless work on getting DSA implemented (https://github.com/ggml-org/llama.cpp/pull/21149), and to am17an and pwilkin for taking this up! Really looking forward to this PR getting merged!
YouTube
Let's Run DeepSeek V4 Flash vs Pro - Local AI Coding, Maths & Logic TESTED 🧐
xCreate
View originalDeepSeek V4 Flash Inference on Strix Halo: ds4, Quantizations, Distributed Inference and Benchmarks
Donato Capitella
View originalDeepSeek-V4-Flash - Self Hosting Frontier AI
0xSero
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