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.
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 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.
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Deepseek V4 Pro API
DeepSeek V4 Pro is DeepSeek's open-weight flagship model for agentic coding, advanced reasoning, and long-context professional work. It uses a 1.6-trillion-parameter Mixture-of-Experts design with 49 billion parameters activated per token, pairing frontier-scale capacity with selective computation.
On iCreat, you can call deepseek-v4-pro through an OpenAI-compatible Chat Completions API. The model supports text input, streamed output, tool-oriented workflows, and configurable thinking, giving teams a direct way to test it in the Playground and integrate it with existing OpenAI SDK applications.
Model Positioning
DeepSeek V4 Pro is the capability-focused model in the V4 Preview family. It is designed for difficult coding, knowledge, STEM reasoning, long documents, and multi-step Agent workflows where output quality matters more than minimum latency.
Its defining combination is a 1M-token context window, a 384K-token maximum output, and three effective reasoning modes: Non-think, Think High, and Think Max. The model is text-only, so applications that require native screenshot or image understanding need a vision-capable model or a separate extraction stage.
Key Capabilities
Agentic Coding
DeepSeek positions V4 Pro for repository-scale coding and long-running engineering tasks. With an application-provided tool harness, it can plan changes, inspect code, call development tools, evaluate results, and continue across multiple execution steps.
Million-Token Context
The 1M-token context window can accommodate large repositories, document collections, specifications, logs, and extended conversation state. Its 384K-token maximum output also supports unusually large generated artifacts when the workflow genuinely requires them.
High and Maximum Reasoning
Think High targets demanding analysis and planning, while Think Max gives the model a larger reasoning budget for the hardest tasks. Non-think remains available for direct responses that do not justify extended deliberation.
Knowledge, STEM, and Code Reasoning
V4 Pro is the knowledge-rich member of the V4 family. DeepSeek reports strong results across coding, mathematics, science, long-context retrieval, and agent benchmarks, making it suitable for work that combines domain knowledge with structured execution.
Open Weights
DeepSeek publishes V4 Pro weights under the MIT License. Teams can evaluate the same model family through iCreat without operating the substantial infrastructure required for self-hosting a 1.6T-parameter MoE system.
Pricing
| Token Type | Price |
|---|---|
| Input | $0.437 per 1M tokens |
| Output | $0.87 per 1M tokens |
| Cache Read | $0.003625 per 1M tokens |
Use Cases
- Repository-scale engineering: analyze large codebases, plan multi-file changes, investigate failures, and coordinate testing through a controlled coding harness.
- Long-context research: synthesize extensive reports, policies, specifications, logs, or retrieved evidence while preserving cross-document relationships.
- Tool-driven Agents: power multi-step workflows that search, call APIs, execute tools, inspect results, and recover from failed actions.
- STEM problem solving: work through difficult mathematics, science, and technical reasoning with High or Max thinking.
- Large structured deliverables: generate detailed implementation plans, migration documents, technical reports, or other long-form text artifacts.
Any workflow that executes code, changes files, calls external systems, or makes consequential decisions still requires scoped tools, validation, and approval controls outside the model.
Model Comparison
DeepSeek V4 Pro vs DeepSeek-V3.2
| Factor | DeepSeek V4 Pro | DeepSeek-V3.2 |
|---|---|---|
| Positioning | Flagship knowledge, reasoning, and complex Agent model | Reasoning-first daily driver for agents |
| Parameters | 1.6T total; 49B activated | 671B total; 37B activated |
| 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 | Larger knowledge capacity, longer context, and stronger complex-agent focus | Earlier integration of thinking with tool use |
| Best Fit | Maximum-capability coding, research, and long-horizon Agents | General reasoning and tool workflows with smaller context needs |
DeepSeek V4 Pro vs Claude Fable 5 and GPT-5.6 Sol
| Factor | DeepSeek V4 Pro | Claude Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| Positioning | Open-weight flagship for reasoning and Agentic Coding | Highest-capability widely released Claude model for demanding reasoning and long-horizon agents | GPT-5.6 frontier model for complex professional work |
| Context Window | 1M tokens | 1M tokens | 1.05M tokens |
| Maximum Output | 384K tokens | 128K tokens | 128K tokens |
| Official Input Modalities | Text | Text and image | Text and image |
| Reasoning Control | Non-think, Think High, Think Max | Always-on Adaptive Thinking with effort control | none, low, medium, high, xhigh, max; optional Pro mode |
| Weight Availability | MIT open weights | Closed weights | Closed weights |
| Best Fit | Open-weight text Agents, very long output, and advanced coding | Quality-first visual and text Agents that run across many stages | Multimodal production workflows needing broad tools and fine-grained reasoning control |
Why Choose DeepSeek V4 Pro?
Choose DeepSeek V4 Pro when the workload needs a high-capability text model, exceptionally large working context, and deep control over reasoning. It is especially compelling for custom coding Agents, research systems, and long-form technical workflows that benefit from open weights but do not require native image input.
iCreat provides a managed path to that capability through a familiar OpenAI-compatible interface. Teams can validate prompts in the Playground, then move the same model ID into streamed applications and tool-enabled production workflows without managing the model's self-hosting footprint.
Specifications
| Category | Description |
|---|---|
| Model Name | DeepSeek V4 Pro |
| Provider | DeepSeek |
| Model ID | deepseek-v4-pro |
| Release Date | April 24, 2026 |
| Model Type | Text Mixture-of-Experts 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 enabled; regular thinking requests default to High |
| Total Parameters | 1.6T |
| Activated Parameters | 49B per token |
| Weight Availability | MIT open weights |
| iCreat Input Modalities | Text |
| iCreat API Features | OpenAI-compatible Chat Completions API, streaming, thinking configuration |
| Best Suited For | Agentic coding, long-context research, STEM reasoning, tool-driven Agents |
Architecture
DeepSeek V4 Pro is a Mixture-of-Experts language model with 1.6T total parameters and 49B activated per token. Its Hybrid Attention Architecture combines Compressed Sparse Attention with Heavily Compressed Attention to reduce the computation and KV-cache demands of million-token inference. DeepSeek also discloses Manifold-Constrained Hyper-Connections for signal stability and the Muon optimizer for training convergence.
The model 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.
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 model behaviors.
In thinking mode, temperature, top_p, presence_penalty, and frequency_penalty are accepted but ignored. Control response behavior through the prompt, reasoning mode, output limits, tools, and validation rather than relying on those sampling parameters.
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 break the reasoning chain and cause a request error. Ordinary conversation turns without tool calls do not require previous reasoning content.
Budget context and output deliberately even though the published limits are large. Reserve space for system instructions, tool definitions, retrieved evidence, conversation state, reasoning, and the final answer, and use streaming for long completions.
FAQ
Is DeepSeek-V4-Pro-Max a separate model on iCreat?
No. DeepSeek-V4-Pro-Max is the maximum reasoning-effort mode of V4 Pro. Use the deepseek-v4-pro model ID on this page and select the effective Max reasoning level through request configuration.
Should I choose DeepSeek V4 Pro or V4 Flash?
Choose V4 Pro for the hardest knowledge, reasoning, coding, and complex Agent tasks. V4 Flash is the smaller V4 model aimed at faster, more economical execution and simpler Agent work.
Can DeepSeek V4 Pro analyze images or screenshots directly?
No. Its official input modality is text. Extract image content with a vision-capable model or OCR pipeline first, then pass the resulting text to V4 Pro when its reasoning or long-context capabilities are needed.
Can I use deepseek-chat or deepseek-reasoner as an alias for V4 Pro?
No. DeepSeek states that those legacy names route to V4 Flash rather than V4 Pro and are scheduled for retirement. Use the explicit iCreat model ID deepseek-v4-pro.