Deepseek V4 Flash 0731
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model from DeepSeek, featuring 284B total parameters with 13B activated per token and a 1-million-token context window. Designed for fast inference and high-throughput workloads, it delivers strong reasoning and coding capabilities while maintaining excellent cost efficiency. Its hybrid attention architecture enables efficient long-context processing. The model supports **high** and **xhigh** reasoning levels, with **xhigh** representing the maximum reasoning effort. It is ideal for coding assistants, conversational systems, and agentic workflows where responsiveness, scalability, and cost efficiency are essential.
Related Models
Deepseek V4.1 Flash
DeepSeek V4.1 Flash is DeepSeek's open-weights, ultra-efficient vision-language Mixture-of-Experts (MoE) foundation model. Built on a novel Causal Encoder-Decoder (CED) architecture, it distributes 552B total parameters while activating only 8B parameters during prefill and 16B parameters during decode. Featuring native visual-text joint embedding, Compressed Sparse Attention, Engram memory, and a 4x KV-cache memory reduction, it effortlessly processes up to a 1M-token context window at high throughput. Engineered for extreme cost efficiency and frontier agentic coding, DeepSeek V4.1 Flash powers autonomous software engineering, multimodal document RAG, UI browser automation, and high-concurrency API integrations.
Deepseek V4 Pro 0813
DeepSeek-V4-Pro-0813 is a flagship MoE model released on August 13, 2026, serving as the official GA release built for complex logical reasoning, code construction, and AI Agent collaboration. Architecture & Performance: 1.6T parameters (49B active), built-in DSpark speculative decoding, with significantly enhanced inference throughput. Compatible with OpenAI/Anthropic APIs, deeply integrated with DeepSeek Harness, and delivers outstanding performance on Terminal Bench.
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Deepseek V4 Flash API
DeepSeek V4 Flash is DeepSeek's open-weight model for fast, efficient reasoning, long-context processing, and practical Agent workflows. Its Mixture-of-Experts design contains 284 billion parameters while activating 13 billion per token, reducing the active compute footprint without giving up the V4 family's million-token context.
On iCreat, you can call deepseek-v4-flash through an OpenAI-compatible Chat Completions API. The model supports text input, streamed output, configurable thinking, and tool-oriented workflows, so teams can test prompts in the Playground and move them into applications built with familiar OpenAI SDK patterns.
Model Positioning
DeepSeek V4 Flash is the efficiency-focused member of the V4 Preview family. DeepSeek positions it as the faster and more economical option whose reasoning approaches V4 Pro and whose performance can match Pro on simple Agent tasks.
Flash is not a renamed Pro model. Its smaller parameter capacity makes it better suited to high-volume text workloads and rapid execution, while V4 Pro remains the stronger choice for pure knowledge tasks and the most complex Agent workflows. Both models provide a 1M-token context window, a 384K-token maximum output, and Non-think, Think High, and Think Max modes.
Key Capabilities
Efficient Reasoning
V4 Flash activates 13B of its 284B parameters per token. Non-think handles direct tasks, Think High adds deliberate analysis, and Think Max expands the reasoning budget when a difficult request justifies more computation.
Million-Token Context
The 1M-token context window can hold large codebases, document collections, logs, specifications, and long conversation state. The 384K-token maximum output gives applications substantial room for generated code, structured transformations, and long technical deliverables.
Practical Agent Workflows
DeepSeek reports that Flash performs on par with V4 Pro on simple Agent tasks. With an application-provided tool harness, it can support iterative coding, API calls, retrieval, result inspection, and other bounded multi-step workflows.
Fast Text Processing
The model's efficiency-oriented design suits classification, extraction, summarization, rewriting, code assistance, and other text workloads where request volume and response time matter alongside reasoning quality.
Open Weights
DeepSeek publishes V4 Flash under the MIT License. Teams can evaluate the model through iCreat without operating the specialized infrastructure needed to serve a 284B-parameter MoE model themselves.
Pricing
| Token Type | Price |
|---|---|
| Input | $0.14 per 1M tokens |
| Output | $0.28 per 1M tokens |
| Cache Read | $0.028 per 1M tokens |
Use Cases
- High-volume text automation: classify, extract, normalize, rewrite, and summarize text across large request volumes.
- Long-context analysis: review extensive repositories, logs, policies, transcripts, or retrieved document collections in one working context.
- Coding assistance: explain code, draft implementations, investigate errors, and support iterative development through a controlled tool harness.
- Bounded Agents: run focused search, tool-calling, and verification loops where the workflow has clear goals and validation rules.
- Large text generation: produce structured reports, migration plans, specifications, or code artifacts that require more output room than conventional LLM limits provide.
Execution safety remains an application responsibility: constrain tool permissions, verify generated actions, and require approval before code, files, external systems, or consequential decisions are affected.
Model Comparison
DeepSeek V4 Flash vs DeepSeek-V3.2
| Factor | DeepSeek V4 Flash | DeepSeek-V3.2 |
|---|---|---|
| Positioning | Efficient long-context reasoning | General reasoning and tool use |
| Parameters | 284B total; 13B active | 671B total; 37B active |
| Context Window | 1M tokens | 128K tokens |
| Attention Architecture | Hybrid CSA and HCA | DeepSeek Sparse Attention |
| Reasoning Control | Non-think, Think High, Think Max | Thinking and non-thinking |
| Core Difference | 1M context; 13B active; Max mode | 128K context; 37B active |
| Best Fit | High-volume long-context Agents | General reasoning and tool workflows |
DeepSeek V4 Flash vs MiniMax-M2.7
| Factor | DeepSeek V4 Flash | MiniMax-M2.7 |
|---|---|---|
| Positioning | Efficient long-context text model | Software-engineering Agent model |
| Parameters | 284B total; 13B active | 229.9B total; 9.8B active |
| Context Window | 1M tokens | 204,800 tokens |
| Official Input Modalities | Text | Text |
| Reasoning Control | Non-think, Think High, Think Max | Interleaved Thinking |
| Weight Availability | MIT | Published; MiniMax license |
| Core Difference | 1M context; 384K maximum output | Production coding and troubleshooting |
| Best Fit | Long-context text Agents | Coding and professional delivery |
Why Choose DeepSeek V4 Flash?
Choose DeepSeek V4 Flash when the application needs more reasoning and context than a basic high-throughput model, but does not require the full knowledge capacity of V4 Pro. It is particularly well matched to text-first products that process large inputs, generate long outputs, or run focused Agent and coding loops at scale.
iCreat provides a managed route to the model through a familiar OpenAI-compatible interface. Teams can validate the workload in the Playground, then use the same model ID for streaming and production integration without managing the model's serving footprint.
Specifications
| Category | Description |
|---|---|
| Model Name | DeepSeek V4 Flash |
| Provider | DeepSeek |
| Model ID | deepseek-v4-flash |
| Release Date | April 24, 2026 |
| Model Type | Text MoE reasoning LLM |
| Context Window | 1M tokens |
| Maximum Output | 384K tokens |
| Knowledge Cutoff | Not published by DeepSeek |
| Official Input Modalities | Text |
| Output Modalities | Text |
| Official Reasoning Control | Non-think, Think High, Think Max |
| Default Effort | Thinking on; High by default |
| Total Parameters | 284B |
| Activated Parameters | 13B per token |
| Weight Availability | MIT |
| iCreat Input Modalities | Text |
| iCreat API Features | OpenAI-compatible Chat Completions, streaming, thinking, tool workflows |
| Best Suited For | Long-context text, coding, bounded Agents |
Architecture
DeepSeek V4 Flash is a Mixture-of-Experts language model with 284B total parameters and 13B activated per token. The V4 Hybrid Attention Architecture combines Compressed Sparse Attention with Heavily Compressed Attention to improve million-token inference efficiency. DeepSeek also discloses Manifold-Constrained Hyper-Connections for signal stability and the Muon optimizer for training convergence.
The V4 series was pretrained on more than 32T diverse, high-quality tokens. Its post-training pipeline first develops domain-specific experts through supervised fine-tuning and reinforcement learning with GRPO, then consolidates those capabilities through on-policy distillation. The published Flash instruct weights use mixed FP4 and FP8 precision.
Production Notes
Treat High and Max as the two effective thinking levels. DeepSeek maps low and medium to High and maps xhigh to Max, so changing among the mapped aliases does not create additional official reasoning behaviors.
Do not use sampling controls to tune thinking behavior. DeepSeek accepts temperature, top_p, presence_penalty, and frequency_penalty in a thinking request but ignores them; use the prompt, reasoning mode, output limits, tools, and validation instead.
When a thinking request produces a tool call, preserve the complete reasoning_content and return it with the tool result in subsequent turns. Omitting it can cause a request error. Ordinary conversation turns without tool calls do not require previous reasoning content.
Large published limits should still be budgeted deliberately. Reserve context for system instructions, tool definitions, retrieved evidence, conversation state, reasoning, and the final answer, and use streaming for long completions.
FAQ
Is DeepSeek-V4-Flash-Max a separate model on iCreat?
No. It is the maximum reasoning-effort mode of V4 Flash, not a separate model ID. Use deepseek-v4-flash and select the effective Max level through request configuration.
Does 13B activated parameters mean V4 Flash is only a 13B model?
No. The model contains 284B total parameters. Its MoE router activates about 13B parameters for each token, which describes active computation rather than total model capacity.
Does Flash Max fully replace V4 Pro?
No. DeepSeek reports that Flash Max can approach Pro's reasoning when given a larger thinking budget, and Flash can match Pro on simple Agent tasks. Pro still has an advantage in pure knowledge and the most complex Agent workflows.
Can I keep using deepseek-chat or deepseek-reasoner in production?
Those legacy names currently route to V4 Flash non-thinking and thinking modes, but DeepSeek has scheduled them for retirement on July 24, 2026 at 15:59 UTC. Use the explicit iCreat model ID deepseek-v4-flash.