DeepSeek-V4-Flash
DeepSeek-V4-Flash is an open-weight 1M context AI model scoring 54.4% on SWE-bench at $0.14 per 1M tokens, optimized for agentic coding and reasoning.
deepseekDeepSeek V42026-07-31About DeepSeek-V4-Flash
Learn about DeepSeek-V4-Flash's capabilities, features, and how it can help you achieve better results.
DeepSeek-V4-Flash is an open-weight Mixture of Experts (MoE) language model designed for software engineering and multi-step reasoning tasks. It contains 284 billion total parameters with 13 billion active parameters per token during inference. The model utilizes a native 1,048,576 token context window, allowing developers to process entire code repositories or long technical documents in a single request.
The July 31, 2026 update maintains the architecture of the initial preview model while applying post-training focused on agentic behavior. This re-post-training improved its score on Terminal-Bench 2.1 from 61.8 to 82.7 and increased its SWE-bench result to 54.4%. The model supports variable reasoning effort levels and native responses API integration for streaming intermediate commentary alongside final outputs.
Developers can access the model through DeepSeek's OpenAI-compatible API or run open weights locally on hardware configurations with 128GB to 192GB of unified memory. The standard API pricing is $0.14 per 1 million input tokens and $0.28 per 1 million output tokens, with cached input hits dropping to $0.03 per million tokens.
Use Cases
Discover the different ways you can use DeepSeek-V4-Flash to achieve great results.
Autonomous Agentic Coding
Running multi-step terminal and file modifications using Codex CLI or Cline harnesses to fix GitHub issues and automate test creation.
Local AI Deployment
Hosting the full model on workstations equipped with 128GB unified memory or four GPUs using 4-bit quantizations.
Large-Scale Repository Refactoring
Ingesting up to 1 million tokens of codebase context to map dependencies and execute architectural updates across multiple modules.
Interactive 3D and Front-End Web Generation
Building single-file web applications, Three.js 3D environments, and complex SVG diagrams directly from prompt specifications.
High-Volume Data Processing
Processing extensive text logs and structured documents using context caching to lower API costs to $0.03 per million tokens.
Multi-Step Terminal Automation
Executing shell commands and script pipelines where the model uses internal reasoning to handle unexpected command errors.
Strengths
Limitations
API Quick Start
deepseek/deepseek-v4-flash-0731
import OpenAI from 'openai';
const openai = new OpenAI({
baseURL: 'https://api.deepseek.com',
apiKey: process.env.DEEPSEEK_API_KEY,
});
async function main() {
const completion = await openai.chat.completions.create({
model: 'deepseek-v4-flash',
messages: [{ role: 'user', content: 'Write a TypeScript function to balance a binary search tree.' }],
temperature: 1.0,
top_p: 0.95,
});
console.log(completion.choices[0].message.content);
}
main();Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about DeepSeek-V4-Flash
“DeepSeek V4 Flash 0731 is the undisputed price-performance leader: ~158,000 requests/month within the $60 limit, intelligence 49.9 and agentic score 45.7.”
“We are making the updated DeepSeek V4-Flash 0731 free in Cline. This is the first flash model we've found performs at SOTA levels for autonomous coding.”
“The 'reasoning effort' setting is brilliant. I use low for tests and max for the actual logic. Saves so much time in daily pipelines.”
“DeepSeek V4 Flash 0731 should probably be the number one contender right now if you are into local AI.”
“Models you can run locally now have the intelligence score of top frontier models from 5 months ago. This is absolutely nuts for open weights.”
“DeepSeek V4 Flash is ~150x cheaper than proprietary options while delivering highly competitive UX and design task outputs.”
Related Videos
Watch tutorials, reviews, and discussions about DeepSeek-V4-Flash
“DeepSeek V4 Flash is actually the cheapest out of all of the models, and GPT 5.6 Luna that costs the same is actually two points behind.”
“So, the model's called DeepSeek V4 Flash 0731, and the TLDR of the model is it's got 300 billion parameters, with 13 billion of those being active, has a million token context window, and its weights are on Hugging Face already with”
“actually ran for, DeepSeek was pretty bad here. It took 23 minutes. When you compare that to something like 5.6 Sol, which only took 3 minutes and 42 seconds to create that super impressive result, I do think it could do a bit of work on”
“On Terminal Bench 2.1, the new Flash gets 82.7, while the Flash preview got 61.8... The new flash is now nearly three times better than the big model's preview.”
“This small model just beat Fable 5, Opus 5, and every other frontier model on the single hardest question of my bench, the 3D wristwatch.”
“model of the V4 family, a mixture of experts model with 284 billion total parameters and around 13 billion active parameters with a 1 million token context window. It's designed to be the fast and cheap one, while V4 Pro is the”
“DeepSeek V4 Flash 0731 should probably be the number one contender for you right now if you are into local AI and you have things like 3090s.”
“Having at least 384,000 tokens of context size set. The max”
“So, I would recommend grabbing up that Q3 KXL or possibly going down to the IQ3 XXS if you are interested in that. That”
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Pro Tips
Expert tips to help you get the most out of DeepSeek-V4-Flash and achieve better results.
Calibrate Top-P for Agent Workflows
Set top_p to 0.95 and temperature to 1.0 when deploying the model in coding harnesses to improve tool execution stability.
Set Minimum Context Size for Max Reasoning
Configure a context buffer of at least 384,000 tokens when using max reasoning effort to ensure room for deep chain-of-thought processing.
Utilize Context Caching
Structure repeated system prompts and codebase context to hit API cache layers, reducing input costs from $0.14 down to $0.03 per million tokens.
Use Off-Peak Processing Windows
Schedule bulk API batch jobs during non-peak hours, as API pricing doubles during high-traffic periods.
Testimonials
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Frequently Asked Questions
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