DeepSeek API Pricing Calculator
Estimate your monthly and annual token and context-caching costs for DeepSeek-V3 and DeepSeek-R1 models in 2026. Custom configure automatic prompt caching rates and live search grounding.
Token & Workload Parameterizer
Configure model endpoints, context thresholds, and caching efficiencies
Endpoints represent parsing configurations optimized for general generative chat, structured math, and deep code reasonings.
Your expected monthly prompt, history metadata, and structural system instructions.
DeepSeek’s automated context caching hits are billed at a heavily discounted rate (60%+ discount on input costs).
Defines the ratio of generated output tokens (or R1 thinking tokens) relative to input queries.
Enabling these elements guarantees immediate relevance and massive concurrent latency optimizations.
Calculated Cost Summary
Annual & Monthly API token expenditure matrix
Select your active model endpoint, adjust your monthly token volume, configure caching hit percentages, and hit Calculate to evaluate your costs.
DeepSeek API Specifications
Detailed pricing breakdowns per 1,000,000 tokens across core text and reasoning generation feeds for 2026.
| Model Endpoint / Integration Feed | Cache Miss Rate (Per Million) | Cache Hit Rate (Per Million) | Output Rate (Per Million) |
|---|---|---|---|
| DeepSeek-V3 (Standard Chat/Code) | $0.14 | $0.055 | $0.28 |
| DeepSeek-R1 (Advanced Reasoning) | $0.55 | $0.14 | $2.19 |
| DeepSeek-Coder-V2 (Legacy Code) | $0.14 | $0.055 | $0.28 |
| DeepSeek-V2.5 (Legacy Unified) | $0.14 | $0.055 | $0.28 |
DeepSeek API FAQ
Answers to critical questions regarding context caching, thinking token calculations, and platform rate-limiting.
The DeepSeek API provides access to state-of-the-art open models, including the flagship DeepSeek-V3 (efficient Mixture-of-Experts chat/code model) and DeepSeek-R1 (advanced reasoning/thinking model designed for multi-step math and coding analysis).
Pricing operates under a Cost-Per-Million (CPM) tokens model. Pricing is split into input tokens (with discounts for cached prompts) and output tokens. DeepSeek is highly cost-effective, typically starting at only $0.14/M tokens for V3 input cache misses, and dropping to $0.055/M tokens on cache hits.
DeepSeek automatically caches active context from your API calls (system prompts, history logs, heavy RAG materials). Input tokens that match the cache hit criteria receive massive discounts (approximately 60% cheaper), dramatically cutting down active operational bills.
DeepSeek-R1 generates reasoning or ‘thinking’ tokens prior to spitting out standard conversational outputs. These reasoning tokens are billed at standard output token rates ($2.19 per million) and ensure high-accuracy responses on challenging engineering problems.
Yes. DeepSeek models deliver competitive output quality compared to other frontier models while costing up to 90-95% less, making it the preferred choice for hyperscale production runs and automated task chains.
Search Grounding enables models to query real-time search directories for facts before generating responses. This adds a minor overhead of $0.50 per million input tokens due to parsing web context and query execution loops.