DeepSeek Token Counter
DeepSeek models use a byte-level BPE tokenizer tuned for code and math. TokenMath counts DeepSeek tokens with a calibrated estimate, typically within ±3%.
Universal AI Token Counter
Zero-latency, client-side BPE tokenization with real-time multi-model benchmarks.
DeepSeek Models: Context Windows & Token Prices
| Model | Context Window | Input / 1M | Output / 1M | Token Count Accuracy |
|---|---|---|---|---|
| DeepSeek-V4.1-Flash | 1.05M tokens | $0.3 | $1.2 | Calibrated DeepSeek Tokenizer (±3%) |
| DeepSeek-V4.1-Reasoner | 256k tokens | $0.28 | $0.42 | Calibrated DeepSeek Tokenizer (±3%) |
How DeepSeek Tokenization Works
DeepSeek models split text with the DeepSeek byte-level BPE tokenizer, a Byte-level BPE scheme with a ~102,000 tokens vocabulary. Common words map to one token, while rare words, identifiers and non-Latin scripts are split into several subword pieces. Read how BPE and SentencePiece tokenization work for the details.
- DeepSeek's off-peak and cache-hit pricing make the same token count much cheaper at the right time.
- Reasoner models bill their chain-of-thought as output tokens.
Once you know your token count, estimate a full monthly bill with the LLM cost calculator, or see every DeepSeek model price.
DeepSeek Token Counter FAQ
How do I count DeepSeek tokens?
Paste your prompt into the counter above with a DeepSeek model selected. TokenMath tokenizes the text in your browser and shows the token count, context window usage and input cost. Accuracy: Calibrated DeepSeek Tokenizer (±3%).
What tokenizer does DeepSeek use?
DeepSeek models are counted with the DeepSeek byte-level BPE tokenizer (Byte-level BPE, ~102,000 tokens vocabulary). DeepSeek models use a byte-level BPE tokenizer tuned for code and math.
How many words is 1,000 DeepSeek tokens?
For typical English prose, 1,000 tokens is roughly 700–750 words. Code, JSON and non-English text use more tokens per word, so count the actual text when budgeting.
How much does DeepSeek-V4.1-Flash cost per token?
DeepSeek-V4.1-Flash costs $0.3 per 1M input tokens and $1.2 per 1M output tokens. That's $0.00030 per 1,000 input tokens.