FIREWORKS AI MODEL TOKEN COUNTER

deepseek-coder-v2-lite-instruct Token Counter

Count tokens for deepseek-coder-v2-lite-instruct and estimate API cost before you send a request. Fireworks AI does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count deepseek-coder-v2-lite-instruct tokens.

Tokens

0

Words

0

Characters

0

Input cost

$0.00

Output cost

$0.00

Total request

$0.00

163,840 token context window 0%

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deepseek-coder-v2-lite-instruct at a glance

Provider. Fireworks AI

Context window. 163,840 tokens (about 123,000 English words).

API pricing. $0.50 input / $0.50 output per 1M tokens.

Tokenizer. Not public; counts on this page are calibrated estimates.

Pricing source. The community-maintained LiteLLM pricing dataset, refreshed 2026-07-01. Community-tracked rates can lag provider changes; confirm with the provider before budgeting.

FAQ

deepseek-coder-v2-lite-instruct token counter FAQ

How do I count tokens for deepseek-coder-v2-lite-instruct?

Paste your prompt into the counter on this page. Fireworks AI does not publish the deepseek-coder-v2-lite-instruct tokenizer, so the count is a calibrated estimate based on text length, language, and structure. Treat it as planning guidance rather than an exact billing number.

How much does deepseek-coder-v2-lite-instruct cost per token?

deepseek-coder-v2-lite-instruct costs $0.50 per 1 million input tokens and $0.50 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of deepseek-coder-v2-lite-instruct?

deepseek-coder-v2-lite-instruct has a context window of 163,840 tokens, which is roughly 123,000 English words of combined prompt and response. The counter above shows how much of that window your text consumes.

Is this deepseek-coder-v2-lite-instruct token count exact?

No. It is a clearly labeled estimate, because Fireworks AI has not released a public tokenizer for deepseek-coder-v2-lite-instruct. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.