DeepSeek が提供する DeepSeek V4.1-Flash は、最大 1M トークンのコンテキストウィンドウを備え、入力 $0.3/100万トークン・出力 $1.2/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
Technical Specifications
- Provider
- DeepSeek
- Category
- open-source
- Release Date
- 2026-09
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- deepseek-reasoner
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.3/1M in) across full 1000K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0.006/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
DeepSeek V4.1-Flash Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~368 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to DeepSeek V4.1-Flash:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 74.2%
- GPQA Diamond
- 90.9%
- MMLU-Pro
- 89.5%
- HumanEval
- 96.5%
- Humanity's Last Exam
- 16.8%
- Terminal-Bench
- 31.2%
- SciCode
- 48%
- LiveCodeBench
- 73%
- AIME
- 92.4%
- Long-Context Reasoning
- 93%
- Chatbot Arena Elo
- 1455pts
Strengths
- DeepSeek の高度な Causal Encoder-Decoder (20 enc + 20 dec layers) アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.3/1M、出力 $1.2/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Limitations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Editorial Assessment
DeepSeek V4.1-Flash は、性能・速度・API コストのバランスに優れた DeepSeek の主力モデルです。
Best for: エンタープライズ自動化、ソフトウェア開発、AI エージェント構築、DeepSeek エコシステムの統合
Compare DeepSeek V4.1-Flash
Frequently Asked Questions about DeepSeek V4.1-Flash
How much does the DeepSeek V4.1-Flash API cost per 1M tokens?+
DeepSeek V4.1-Flash by DeepSeek is priced at $0.3 per 1M input tokens and $1.2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.006 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the DeepSeek V4.1-Flash pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in DeepSeek V4.1-Flash's context window?+
DeepSeek V4.1-Flash features a 1M tokens context window, which equals approximately 750,000 English words or ~2,000 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 384,000 tokens.
Can I run DeepSeek V4.1-Flash locally on my own GPU (Open Weights)?+
Yes, DeepSeek V4.1-Flash (763B total (8B prefill / 16B decode active)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does DeepSeek train on data sent to the DeepSeek V4.1-Flash API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does DeepSeek V4.1-Flash perform on coding and reasoning benchmarks?+
DeepSeek V4.1-Flash achieves a composite Intelligence Index of 58/100, scoring 74.2% on SWE-bench Verified, 90.9% on GPQA Diamond, 89.5% on MMLU-Pro, and 1455 pts on Chatbot Arena Elo.
What is DeepSeek V4.1-Flash best used for?+
DeepSeek V4.1-Flash is best suited for: エンタープライズ自動化、ソフトウェア開発、AI エージェント構築、DeepSeek エコシステムの統合. Assessment: DeepSeek V4.1-Flash は、性能・速度・API コストのバランスに優れた DeepSeek の主力モデルです。
DeepSeek Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to DeepSeek's flagship reasoning tier:
DeepSeek V4 Pro
DeepSeek V4
DeepSeek Reasoner
DeepSeek Chat
DeepSeek-R1-0528
DeepSeek-V3-0324
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to DeepSeek V4.1-Flash:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
公的ソース: DeepSeek V4.1-Flash Technical Report & Open Weights Release (September 2026)↗