DeepSeek AI Models on AtlasCloud
Atlas Cloud hosts the full DeepSeek lineup via the DeepSeek API: V3.2, V4, and R1. Models range from 128K to 1M token context, all open-source and pay-as-you-go.
Large Language Models by DeepSeek
Power chat, reasoning, and agents at scale with leading large language models, served fast and affordably on Atlas Cloud.
DeepSeek Models API Pricing Details
Compare standard vs. our pricing across every DeepSeek model.
| Model | Standard Price (USD) | Our Price (USD) | Discount | |
|---|---|---|---|---|
| DeepSeek V4 Pro | $1.74/$3.48per 1M tokens1048.6K context | $1.68/$3.38M in/outper 1M tokens1048.6K context | — | View |
| DeepSeek V4 Flash | $0.14/$0.28per 1M tokens1048.6K context | $0.14/$0.28M in/outper 1M tokens1048.6K context | — | View |
| DeepSeek V3.2 | $0.287/$0.431per 1M tokens163.8K context | $0.26/$0.38M in/outper 1M tokens163.8K context | — | View |
| DeepSeek V3.2 Exp | $0.287/$0.43per 1M tokens163.8K context | $0.27/$0.41M in/outper 1M tokens163.8K context | — | View |
| DeepSeek-V3.1 | $0.574/$1.721per 1M tokens131.1K context | $0.3/$0.95M in/outper 1M tokens131.1K context | — | View |
| DeepSeek OCR | $0.03/$0.03per 1M tokens8.2K context | $0.04/$0.08M in/outper 1M tokens8.2K context | — | View |
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DeepSeek API Use Cases You Can Build on Atlas Cloud
DeepSeek's open-source models cover the full range from cost-efficient high-throughput tasks to frontier-level agentic coding with 1M context. Teams choose between V3.2, V4 Flash, and V4 Pro based on context requirements and task complexity.
Autonomous GitHub Issue Resolution
Engineering teams use DeepSeek V4 Pro to build coding agents that autonomously resolve real GitHub issues, including reading issue descriptions, tracing cross-file dependencies, writing fixes, and running tests. V4 Pro scores 80.6% on SWE-Bench Verified, within 0.2 points of Claude Opus 4.6, and is natively integrated with Claude Code, OpenCode, and OpenClaw agent frameworks. Switching to DeepSeek V4 on Atlas Cloud from a closed-source model requires only a base URL change in the existing SDK setup.
Full Codebase Analysis with 1M Context
Development teams use DeepSeek V4's 1M token context window to load an entire repository into a single API call for cross-file analysis, dependency tracing, and architecture review. V4 achieves 97% accuracy on multi-query Needle in a Haystack at full context length, meaning specific information embedded anywhere in a million tokens is reliably retrieved. At full 1M context, V4 Pro requires only 27% of the inference compute and 10% of the KV cache that V3.2 needs for the same task.
Self-hosted Deployment for Data-sensitive Workloads
Enterprise teams with compliance or data privacy requirements use DeepSeek's MIT license to self-host V4 Flash or V3.2 on their own infrastructure. This is an option that closed-source models like GPT-5 and Claude Opus cannot offer, and it eliminates API dependency for regulated industries. V4 Flash at 284 billion parameters and 13 billion active is the practical self-hosting target; V4 Pro requires a cluster.
Cost-efficient Closed Model Replacement
Teams switching from GPT-5 or Claude Opus use DeepSeek V3.2 as a drop-in replacement via the OpenAI-compatible endpoint on Atlas Cloud. V3.2 is priced at approximately $0.27 per million input tokens while matching GPT-5-level performance across most reasoning benchmarks. The same SDK code routes to DeepSeek with a single base URL change, making migration low-risk.
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Contact SalesFrequently Asked Questions about DeepSeek AI Models
DeepSeek V4 is the current generation flagship, released April 24, 2026, covering both general-purpose and reasoning workflows in a single model. R1 was a standalone reasoning model, but V4's thinking mode replaces it with the same chain-of-thought capability built directly in. The legacy deepseek-reasoner alias retires July 24, 2026, so new integrations should use V4 Pro with thinking mode enabled.
Engram Memory is an external knowledge retrieval system in DeepSeek V4, inspired by how the human brain's hippocampus stores and retrieves information. It uses locality-sensitive hashing to retrieve relevant knowledge at O(1) speed, rather than forcing the model to store all facts in its weights. This contributed to V4's multi-query Needle in a Haystack accuracy jumping from 84.2% in V3.2 to 97.0%.
Yes. DeepSeek V3.2, V4 Flash, and V4 Pro are all released under the MIT license, which permits commercial use, modification, and distribution. V4 Flash is practical to self-host on capable hardware. V4 Pro requires a cluster given its 1.6 trillion parameter size, so most teams use API access on Atlas Cloud instead.
V4 Pro is a 1.6 trillion parameter MoE model with 49 billion active parameters, built for complex reasoning, coding, and agentic tasks. V4 Flash is a 284 billion parameter model with 13 billion active, optimized for speed and cost efficiency on less demanding tasks. Both share the 1M token context window and the Engram Memory architecture.
DeepSeek V4 supports a native 1 million token context window for both Pro and Flash variants, with a maximum output of 393K tokens per response. DeepSeek V3.2 has a 128K context window. The 1M context in V4 makes it practical for full codebase analysis, large document processing, and extended agentic sessions in a single call.
Yes. DeepSeek V3.2 remains available on Atlas Cloud, priced at approximately $0.27 per million input tokens. It is a 685 billion parameter MoE model with 37 billion active parameters and a 128K context window, released under MIT license. It is a cost-effective choice for tasks that do not require V4's 1M context or Engram Memory.
DeepSeek V4 Pro resolves over 80.9% of real-world coding issues on SWE-Bench, targeting GPT-5-class performance. Multi-query long-context accuracy improved to 97.0% on Needle in a Haystack, up from 84.2% in V3.2. The V3.2 Speciale variant on Atlas Cloud additionally achieved gold-medal performance in IMO 2025 and IOI 2025 competition math.
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