How Many Tokens?

← All models

o3: token counter & pricing

OpenAI · exact (uses official tokenizer) · pricing as of 2026-04-27.

Provider
OpenAI
API model ID
o3
Context window
200,000 tokens
Input price
$2.00 per 1M tokens
Output price
$8.00 per 1M tokens
Tokenizer accuracy
exact (uses official tokenizer)
Pricing as of
2026-04-27

Open the counter to count tokens for o3 in real time.

What is o3?

o3 is OpenAI's reasoning-tier model — designed to think longer before responding, which produces measurably better answers on hard logic, math, and code problems. Not a chat model in the GPT-5 sense. Use o3 when the answer matters more than the response time.

How tokens are counted here

o3 uses OpenAI's o200k_base tokenizer (same as the GPT-4o and GPT-5 families). Counts in your browser via js-tiktoken. Exact.

But: the o-series produces a lot of "reasoning tokens" — internal chain-of-thought tokens that count toward your output bill but don't appear in the final response. A 200-token reply might consume 1,500-3,000 output tokens of internal reasoning. The calculator above shows the visible output count; actual billed output is typically 5-15× higher.

Pricing notes

$2.00 input / $8.00 output per 1M tokens. Cached input $0.50/M.

If you're estimating total cost on o3, plan for the reasoning-token multiplier. A workload that looks like 1,000 input + 500 visible output tokens might bill closer to 1,000 input + 4,000 output tokens.

When to use o3

When not to use it:

Common questions

Should I use o3 or o3-pro?

o3 ($2/$8) covers most reasoning workloads. o3-pro ($20/$80) is positioned for the hardest problems where 10× spend is justified by single-shot accuracy. Don't reach for o3-pro before measuring o3 falling short.

How does o3 compare to o4-mini?

o4-mini ($1.10/$4.40) is roughly half the price of o3 with most of the reasoning capability. Try o4-mini first for cost-sensitive reasoning workloads — it's often the right choice.

Does o3 work with prompt caching?

Yes — cached input is $0.50/M (25% of standard rate). Useful for agent loops with stable system prompts.

Compare o3 to other models