OpenAI
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o3 (batch)

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Headline reasoning model in the o-series. Trades latency for depth on math, science and code benchmarks.

o3 (batch) is a multimodal AI model from OpenAI. It costs $1.000 per million input tokens and $4.000 per million output tokens (blended $1.900/M), with a 200,000-token context window.

Profile inherited from upstream o3 ↗ — this is a hosted variant of the same open-weights model.

INPUT
$1.000/M
per million input tokens
OUTPUT
$4.000/M
per million output tokens
BLENDED 70/30
$1.900/M
unchanged since 3 May
CONTEXT
200,000
tokens
What it is good at
  • Best-in-class on math & science benchmarks
  • Multi-step planning
  • Tool use
Typical use cases
  • Hard math/science questions
  • Complex coding
  • Research assistants

Benchmarks

vs. best public score
Scores inherited from o3 — this is a hosted variant of the same open-weights model, so the underlying benchmark scores are identical.
MMLU90%
Multitask academic knowledge across 57 subjects.
Graduate-level science questions, "Google-proof".
MATH96%
High-school competition math problems.
Python function synthesis from docstrings.
Real GitHub issues solved end-to-end.
LMArena Elo1380 Elo
Crowd-sourced head-to-head preference Elo rating.
Hand-curated from each provider's published reports and public leaderboards. Methodology varies across sources, treat as directional rather than authoritative.

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Frequently asked questions

How much does o3 (batch) cost?

o3 (batch) costs $1.000 per million input tokens and $4.000 per million output tokens, for a blended reference rate of $1.900 per million tokens.

What is o3 (batch)'s context window?

o3 (batch) supports up to 200,000 tokens of context in a single request.

What is o3 (batch) best for?

o3 (batch) is well suited to Best-in-class on math & science benchmarks, Multi-step planning and Tool use.

Who makes o3 (batch)?

o3 (batch) is developed and served by OpenAI.

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