DeepSeek Review: Is It The Right Large Language Models (LLMs) For Your Team?
Best for SMB teams · Mid-market · Enterprise
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Starts from $0.15 / million input tokens
Overview
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What is DeepSeek?
DeepSeek trains open-weight reasoning and general models and runs a priced API alongside the weight releases, so the same family can be called over HTTP or hosted on your own hardware. The rate card is unusual in two ways. It prices context cache hits separately from cache misses, and it charges less outside peak hours, with peak defined as 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays. On the card read on 2026-11-08, deepseek-flash costs 0.15 dollars per million input tokens on a cache miss off-peak and 0.60 per million output tokens, rising to 0.30 and 1.20 at peak, with cache hits at 0.003 off-peak. deepseek-v4-pro costs 0.66 and 1.98 off-peak and 1.32 and 3.96 at peak, with cache hits at 0.022. Teams that can shift batch work into off-peak hours halve the bill without changing a line of code.
Pricing
Starts from $0.15 / million input tokens
Best For
Suited for small teams, SMBs, and enterprise
Platform
Web-based — no mobile app
Browser Based (Cloud)
DeepSeek was reviewed internally using user feedback, in-house testing, and market research to assess its performance, reliability, and user experience. Learn how we review products and our evaluation process.
Who should consider DeepSeek
- Team types
- Small Business, Medium Business
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What is the pricing of DeepSeek?
DeepSeek Pricing Plans
deepseek-flash
$0.15
per million input tokens
Off-peak input 0.15 dollars per 1M tokens on a cache miss
Off-peak output 0.60 dollars per 1M tokens
Peak input 0.30 and output 1.20
Cache hit 0.003 off-peak and 0.006 at peak
deepseek-v4-pro
$0.66
per million input tokens
Off-peak input 0.66 dollars per 1M tokens on a cache miss
Off-peak output 1.98 dollars per 1M tokens
Peak input 1.32 and output 3.96
Cache hit 0.022 off-peak and 0.044 at peak
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What are the features of DeepSeek?
Code generation produces source code from a higher-level input such as a natural language description, a specification, or an existing schem…
The context window is how much text, code or other input the model can hold in one request, counted in tokens, together with everything it h…
A language model is given descriptions of the functions or tools available to it and, when a request needs one, returns a structured call na…
The model parameters are published for download, so the same family can be run on hardware you control instead of only through the vendor AP…
Storing the processed form of a prompt's repeated content, such as a long system instruction or a reference document, so that it does not ha…
The vendor prints a rate per million tokens on its own site, separately for the tokens a request reads and the tokens the model writes, so t…
The model can be asked to work through a problem step by step before answering, spending extra generated tokens on the working rather than t…
The vendor supplies the software to run on infrastructure the customer controls, whether physical servers, a private data centre, or the cus…
An interface that returns a model's output incrementally as it is produced, rather than waiting for the full response and sending it in one…
DeepSeek Support Options
Frequently Asked Questions About DeepSeek
Common questions buyers ask before choosing DeepSeek.
DeepSeek is a Large Language Models (LLMs). DeepSeek offers Published Token Pricing, Open Weights, Context Window, Function Calling, Streaming API and many more functionalities.
DeepSeek offers Subscription pricing model
The starting price of DeepSeek is $0.15/million input tokens
Ready to try it?
Get started with DeepSeek
Get connected with the team for a personalised demo.
About the reviewer
Rajat Gupta is the founder of Spotsaas, where he has built software research covering 25,000+ products across CRM, HR, AI, and finance. He applies hands-on product research and a background in commerce and the CFA program to evaluate software through a business and ROI lens. His goal: help teams make software decisions they won't regret.
Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].