OpenAI: o3

OpenAI · Mid-tier · Context 200K

openai/o3

Data as of:

LLM API list prices change frequently (new models and price cuts are common) and vary by tier, region, batch / cache usage and time. These are list prices captured at the time shown; always verify the current price with the provider before relying on it.

Price summary

Input $/1M $2

per 1M input tokens

Output $/1M $8

per 1M output tokens

Blended $/1M $3.5

0.75×input + 0.25×output (factual)

Cache read $/1M $0.5

per 1M cached-input tokens

Blended $/1M is a published convenience figure: 0.75 × input + 0.25 × output (a stated 3:1 input:output mix). It is descriptive arithmetic, not a value verdict.

Specifications

Model
OpenAI: o3
Provider
OpenAI
Input $/1M
$2
Output $/1M
$8
In+Out $/1M
$10
Context
200K tokens
Max output
100K tokens
Cache read $/1M
$0.5
Modalities
image, text, file → text
Cross-checked
Yes

Capability

Capability score
MMLU-PRO
GPQA

Capability values are the published per-model score from Open LLM Leaderboard (Hugging Face), shown as-is with no edit and no “best” verdict. The leaderboard evaluates open-weight models only and lags the newest releases, so many models (including closed/proprietary APIs) have no value and show “—”. Different benchmarks rank models differently; treat this as one signal among many. As of 2026-05-25. Open LLM Leaderboard (Hugging Face) (Apache-2.0).

Try it / official references

External links open the provider's own pages; list prices and availability there are authoritative.

Estimated cost per use case

Use caseinput tokensoutput tokensCost (per 1,000 requests)
Chat / assistant 1,000 500 $6
RAG / Q&A 8,000 800 $22.4
Coding agent 6,000 2,000 $28
Summarization 12,000 600 $28.8

Each row is (input_tokens/1M)×input_price + (output_tokens/1M)×output_price, scaled to 1,000 requests. Assumptions are as shown in the table. Not a recommendation.