Capability · Comparison
OpenAI o1 vs o3
OpenAI's reasoning-model line went from o1 (late 2024) to o3 (2025-2026) — same fundamental architecture, more training, smarter test-time scaling. This comparison is mostly about whether the quality delta is worth the price premium on your specific workload.
Side-by-side
| Criterion | OpenAI o1 | OpenAI o3 |
|---|---|---|
| Context window | 200,000 tokens | 200,000 tokens |
| Math (AIME 2024) As published by OpenAI. | ~83% | ~96% |
| Competition code (Codeforces Elo) Near-top human grandmaster. | ~1891 | ~2727 |
| Pricing ($/M input) As of 2026-04; o3 is cheaper because OpenAI continues to push reasoning models down. | $15 | $2 |
| Pricing ($/M output) Both bill reasoning tokens as output. | $60 | $8 |
| Multimodal | Text, vision | Text, vision |
| Tool use | Limited in early versions | Full tool use + web + code execution |
| Latency | Slow | Slow (configurable reasoning effort) |
| Status | Legacy | Current flagship reasoning model |
Verdict
o3 is a clean upgrade over o1 on every axis — higher quality on hard reasoning, full tool-use support, and cheaper at the API. The only reason to still be on o1 in 2026 is a pinned deployment you don't want to touch. For new work, always start with o3 and only fall back to o1 if you have a specific reproducibility reason.
When to choose each
Choose OpenAI o1 if…
- You have an existing production deployment on o1 with validated behavior.
- You need a stable, pinned model for regression testing.
- You're on a legacy Azure OpenAI contract that hasn't rolled forward.
- Your reasoning workload is light enough that o1 already exceeds quality needs.
Choose OpenAI o3 if…
- You're starting new reasoning-model work.
- You need full tool use (web browsing, code execution) in a reasoning loop.
- You need best-available math or competition-code performance.
- You want lower cost per task at higher quality.
Frequently asked questions
Is o3 strictly better than o1?
For new work, yes — higher quality, more features, lower price. The only reason to stay on o1 is an existing deployment you can't risk changing.
Are reasoning tokens billed separately?
They're billed as output tokens on both models. A single o-series call can emit tens of thousands of reasoning tokens, so monitor output spend carefully.
Can o3 do what Claude Opus 4.7 does?
For agent loops, not quite — Opus has stronger tool-call reliability over long chains. For deep single-step deliberation on hard problems, o3 wins.
Sources
- OpenAI — o1 announcement — accessed 2026-04-20
- OpenAI — o3 model page — accessed 2026-04-20