Calculate DeepSeek-V3.2 costs dynamically. With rates up to 90% lower than competitors, see exactly how much you save on Chat and Thinking modes.
Calculated based on DeepSeek 2026 pricing. 1M tokens = $0.28 (Miss) / $0.028 (Hit) / $0.42 (Output).
Optimized for speed and high-throughput conversational tasks. Best for standard chat, translation, and general assistance.
Thinking mode enabled. Excels at complex math, logical reasoning, and deep code analysis. Supports up to 64K output.
Prior versions specialized in code generation, now superseded by the multimodal V3.2 flagship.
DeepSeek uses an aggressive prefix caching system. If you reuse the same prompt prefix, any tokens served from the cache are billed at just **$0.028 per 1M tokens**—a massive reduction from the already industry-low $0.28 price.
Both the chat and reasoner models support a 128K token input window. DeepSeek Reasoner specifically allows for up to **64K of reasoning output**, enabling extremely complex chain-of-thought processing for long-running tasks.
DeepSeek charges strictly based on token usage. Fees are deducted directly from your topped-up balance (granted balance is prioritized). This pay-as-you-go model with no monthly minimums makes it the go-to for lean development.
DeepSeek V3.2 includes experimental support for **FIM Completion** (Fill-In-the-Middle) and **Chat Prefix Completion**, giving developers low-level control over model responses for advanced UX patterns.
No! According to the 2026 pricing schedule, both **deepseek-chat** and **deepseek-reasoner** share the exact same price window: $0.28/M input tokens and $0.42/M output tokens. However, the reasoner consumes more output tokens because it generates internal "thinking" tokens.
DeepSeek's API automatically detects reused prefixes across requests. There's no extra header to send; the API simply bills those tokens at the 90% discounted "Cache Hit" rate on your usage logs.
The API version (V3.2) is a developer-optimized flagship supporting 128K context and tool calls. The web/app versions may use different optimization layers or quantization depending on the regional server load.