DeepSeek
DeepSeek V4 Flash Vision Exp
deepseek-v4-flash-vision-exp
Total Context
1M
Max Output
384K
Released
N/A
Input$0.2857 / M tokens
Output$1.1429 / M tokens
DeepSeek V4.1 Flash
deepseek-v4.1-flashDeepSeek-V4.1-Flash is a fast and efficient multimodal AI model with advanced reasoning, coding, and agent capabilities. Supporting up to 1M tokens of context and native vision understanding, it delivers frontier-level intelligence with lower inference costs, making it ideal for AI agents, automation, coding assistants, and scalable applications.
Context Window
1M tokens
Maximum Output
384K tokens
Release Date
Sep 10, 2026
Modalities
DeepSeek V4.1 Flash Pricing
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.2857/M | $1.1429/M | $0.0057/M |
DeepSeek V4.1 Flash API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Supported
Knowledge Base
2025-05-01
Endpoint Protocols
Completions APIResponses APIMessages API
DeepSeek V4.1 Flash Model Highlights
DeepSeek V4.1 Flash combines native visual understanding, a one-million-token context window, controllable reasoning and an architecture optimized for input-heavy agent workloads.
Native Visual Understanding
The model jointly processes image and text representations, enabling visual evidence to inform its reasoning and text responses.
Million-Token Context
A context window of up to one million tokens supports analysis of large repositories, extensive documents and long-running agent histories.
Controllable Reasoning
Reasoning effort can be continuously adjusted from 1 to 100, allowing developers to balance deliberation against runtime for each workload.
Efficient Asymmetric Architecture
Its Causal Encoder-Decoder activates 8B parameters during input processing and 16B during generation, while compressed attention reduces persistent KV-cache requirements.
DeepSeek V4.1 Flash Use Cases
DeepSeek V4.1 Flash is suited to repository-scale software work, visual document analysis, long-context research and multi-step automation.
Repository-Scale Development
Inspect large codebases, trace dependencies, implement cross-file changes and verify fixes through terminal-based coding workflows.
Visual Document Analysis
Interpret screenshots, charts and document images to answer questions, extract relevant details and produce text summaries.
Long-Context Research
Review extensive technical materials in one working context, connect evidence across sections and produce structured findings.
Multi-Step Agent Automation
Plan and execute tool-assisted workflows that combine information gathering, reasoning and code execution to complete operational tasks.
How to Use DeepSeek V4.1 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 V4.1 Flash Benchmarks
DeepSeek V4.1 Flash (Reasoning, Max Effort)
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 39.5 |
Metrics sourced from Artificial Analysis
DeepSeek V4.1 Flash FAQs
Common questions about DeepSeek V4.1 Flash on TokenHub.
What is DeepSeek V4.1 Flash?+
It is DeepSeek's multimodal Mixture-of-Experts model with a 552B-parameter backbone, asymmetric Causal Encoder–Decoder architecture, and support for text and image inputs.
What is DeepSeek V4.1 Flash best suited for?+
It is well suited to coding agents, long-context analysis, visual document understanding, and high-throughput workflows that repeatedly process large inputs.
What are its main strengths?+
Its strengths include efficient asymmetric computation, a substantially smaller persistent KV cache, a one-million-token context, native image understanding, and strong support for agentic workloads.
What limitations or tradeoffs should I consider?+
The open weights are very large to self-host, visual inputs produce text rather than images, and important outputs still require validation. Test quality, latency, and total cost on your own workload.
How do I use DeepSeek V4.1 Flash on TokenHub?+
Select deepseek-v4.1-flash in your TokenHub API request and use the endpoint and credentials shown in your account. Confirm the current input format and limits in TokenHub's model documentation.
How is pricing and availability determined?+
DeepSeek offers the model through its API and uses peak and off-peak pricing. TokenHub rates and regional availability can differ, so check the live TokenHub model page before deployment.
When should I choose another model?+
Choose another model if you need image generation, a much smaller local deployment, or a provider-specific ecosystem feature. Compare candidates on representative prompts before committing.
Ready to use DeepSeek V4.1 Flash?
Use one API key to access DeepSeek V4.1 Flash and more AI models through TokenHub.
DeepSeek V4.1 Flash Media and Demos
Verified public announcements, technical explainers, and community tests of DeepSeek V4.1 Flash.
X (Twitter)
DeepSeek ·
🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6 https://t.co/wxJGiyX56o
https://pbs.twimg.com/media/HR1UyHiaAAAtpqw.jpgView originalDeepSeek ·
💾 Smaller KV cache. Bigger savings. Compared with the previous generation, V4.1-Flash’s KV cache needs just: 🔹 1/4 the HBM 🔹 1/8 the SSD storage Cache-hit charges often account for a large share of agent costs. Compressing the cache cuts those costs significantly. 3/6 https://t.co/rLzo9cTJzR
https://pbs.twimg.com/media/HR1VAiCaUAA4jnX.jpgView originalDeepSeek ·
⚡ V4.1-Flash is now live on the DeepSeek API with native multimodal support. Set your model to deepseek-flash. 🔹 V4-Flash & V4-Flash-Vision-Exp are retired. For compatibility, deepseek-v4-flash and deepseek-v4-flash-vision-exp temporarily route to V4.1-Flash. 🔹 Tests by
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DeepSeek-V4.1-Flash Release (official)
dnohrdk ·
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It’s officially out and the prices have been updated. /// Today, we officially release the DeepSeek-V4.1-Flash model. It is the smallest model in our new architecture family, with native multimodal visual understanding. The new architecture is designed for a higher capability ceiling, faster inference, higher throughput, and scaling to larger models. GPQA Diamond: 90.9 HLE: 36.8 (39.1*) Codeforces (Rating): 3471 MathArena Apex: 65.6 Terminal-Bench 2.1: 90.6 Terminal-Bench 3.0: 30.0 Terminal-Bench 4.0: 31.2 DeepSWE v1.1: 74.2 ProgramBench: 20.3 NL2Repo-Bench: 65.4 CyberGym: 88.1 SEC-Bench Pro: 62.8 ExploitGym: 15.3 HLE (w/tools): 63.9 Automation-Bench: 54.8 Agents' Last Exam: 31.8 Chartography (w/tools): 78.9 BabyVision (w/tools): 89.6 ZeroBench-main (w/tools): 49.0 * Tested only on the pure-text subset of the HLE benchmark set. API changes DeepSeek V4.1 Flash is now available on the DeepSeek API with native multimodal support. Change the model name to deepseek-flash to call the latest V4.1 Flash model. The previous-generation models V4 Flash and V4 Flash Vision Exp have been retired; for compatibility, the model names deepseek-v4-flash and deepseek-v4-flash-vision-exp are temporarily routed to V4.1 Flash. Meanwhile, extensive testing shows that V4.1 Flash now outperforms DeepSeek V4 Pro across performance, cost, speed, and total time, so we plan to retire V4 Pro in an orderly manner. After 12:00 Beijing Time on September 14, 2026, and until the future release of V4.1 Pro, all requests to deepseek-v4-pro will be routed to V4.1 Flash and billed at the V4.1 Flash price. API apricing adjustment With the release of DeepSeek-V4.1-Flash, API prices have been reduced accordingly. For details, please refer to Models & Pricing. /// Source: https://api-docs.deepseek.com/updates/#deepseek-v41-flash-release
DeepSeek V4.1 Flash: Stronger, Faster, More Accessible
Top_Power5877 ·
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Original Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our brand-new model architecture series, with native multimodal visual understanding. The new architecture was designed with these goals in mind: a higher capability ceiling, faster inference, greater throughput, and scalability to larger-parameter models. Asymmetric architecture: big intelligence at low cost DeepSeek V4.1 Flash is a 552B-parameter MoE model built on a brand-new Causal-Encoder-Decoder architecture. Input and output are asymmetric: only 8B parameters are activated on the input side and 16B on the output side, making it significantly cheaper than known models of the same size. V4.1 Flash also uses a new pre-training approach and has gone through larger-scale reinforcement learning post-training. In benchmark testing, it surpasses the intelligence level of a range of flagship models, including DeepSeek V4 Pro. r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-qq5p9q5qymoh1.png?width=1080&format=png&auto=webp&s=3ae8cc6dbdd62a2b2ec7f59dc207d147c9165c0f) r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-2tvpysuvymoh1.png?width=1080&format=png&auto=webp&s=84ed35f530b3c0a4534036eaa4fc83cfce641b7d) Less cache, lower cost The new generation of models dramatically reduces the size of the KV cache. Compared with the previous generation, HBM requirements drop to 1/4 and SSD requirements to 1/8. In agent scenarios, cache-hit charges often make up a large share of the bill, so compressing the KV cache substantially lowers the cost of agent-style tasks. r/LocalLLaMA - Figure: DeepSeek's continued progress in reducing context storage. Relative to the first-generation model, the KV cache has shrunk 437×. (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-p47kb004zmoh1.png?width=1080&format=png&auto=webp&s=e67d215932f53fa7fb9987c31a0ee55490811bad) Figure: DeepSeek's continued progress in reducing context storage. Relative to the first-generation model, the KV cache has shrunk 437×. API support DeepSeek V4.1 Flash is now live on the DeepSeek API with native multimodal support. Simply change the model name to deepseek-flash to call the latest V4.1 Flash. The older V4 Flash and V4 Flash Vision Exp models have been retired; for compatibility, the model names deepseek-v4-flash and deepseek-v4-flash-vision-exp will temporarily be routed to V4.1 Flash. In addition, extensive testing shows that V4.1 Flash comprehensively outperforms V4 Pro on performance, cost, speed, and total time-to-completion, so we plan to phase out the V4 Pro model in an orderly fashion. After 12:00 Beijing time on September 14, 2026, and until V4.1 Pro launches, all requests to deepseek-v4-pro will be routed to V4.1 Flash and billed at V4.1 Flash's unit price. Tencent (WorkBuddy, CodeBuddy) and OpenCode, as official partners, have now fully integrated DeepSeek V4.1 Flash — give it a try! API pricing adjustment Thanks to the architectural innovations, DeepSeek V4.1 Flash can serve more users at lower cost, so we have cut V4.1 Flash's pricing accordingly. To allocate resources more sensibly, we continue to use peak/off-peak pricing, with off-peak prices at half the peak rate, and encourage users to schedule tasks around their actual usage patterns. The new prices take effect at 12:00 on September 10, 2026. r/LocalLLaMA - DeepSeek V4.1 Flash: Stronger, Faster, More Accessible (https://preview.redd.it/deepseek-v4-1-flash-stronger-faster-more-accessible-v0-qkhui0kjzmoh1.png?width=1690&format=png&auto=webp&s=0d6ce254d63e29bfabea7c65638342717a5d1e54) Open-source release We will fully support the open-source community in adapting inference for the new model, and will explore various ways to broaden deployment. If you have large-scale deployment needs and the corresponding resources (a 2k-GPU cluster with storage cluster), please get in touch. Model weights: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash Paper: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf
DeepSeek V4.1 Flash vs V4 Flash Vision Exp: 38% faster and 43% fewer tokens in my quick coding test
Diru14 ·
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DeepSeek V4.1 Flash Is INSANE – Is THIS the Best Open Model Yet?
Bijan Bowen
View originalDeepseek V4.1 Flash (Fully Tested): 200 TPS & Beats Astra!? (+New Architecture Overview)
AICodeKing
View originalDeepSeek V4.1 Flash 有多快？架构简析 + Pi Agent 演示
01Coder
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