O1 PRO vs O3 - OpenAI Models comparaison

TL;DR — When to reach for each model

• o3 is OpenAI’s newest flagship reasoning model. It costs 5–15 × less than o1‑pro, keeps the same giant 200 k‑token context window, adds native image‑in‑the‑loop reasoning and full ChatGPT tool‑use, and is available through the familiar Chat Completions and new Responses endpoints. It’s the sweet spot for most production workloads that need very strong analysis, code or math with occasional vision.

• o1‑pro is the “think harder” edition of the older o1 line. It uses extra compute under the hood, is only accessible through the Responses API, and is dramatically more expensive. You pay the premium when microscopic reliability on very knotty, long documents or codebases justifies the bill—and when latency is less important.

1. Spec sheet at a glance

*Data is from o3‑mini benchmarks but reflects the shared architecture improvement.

2. Capability highlights

2.1 Image‑in‑the‑loop reasoning (o3 only)

OpenAI’s new “think with images” pipeline lets o3 crop, zoom and rotate pictures as part of its chain‑of‑thought, so you can hand it a white‑board photo or a chart and get structured analytical answers.

2.2 Extended tool ecosystem

Because o3 ships in ChatGPT with the full tool picker, the same model can browse the web, run Python code, search files, and generate images—all in one call.
o1‑pro’s Responses‑only interface omits those helpers; you’d need to orchestrate them yourself.

2.3 Reasoning depth vs. cost

• o1‑pro spends extra GFLOPS internally (“high‑effort” mode) before emitting tokens, which can yield more deterministic multi‑step proofs or refactors.

• o3 relies on architectural gains rather than raw compute; in real‑world coding and math benchmarks it already surpasses o1‑pro for many tasks while costing a fraction per request.

2.4 Performance caveats

• o1‑pro’s per‑request latency can be 2–3 × o3’s, frustrating interactive UX.

• Early testers noted that o1‑pro sometimes mis‑counts objects in simple vision queries despite image input support.

• The Arc Prize Foundation estimates o3’s raw compute can climb sharply on exhaustive tasks, though still far cheaper than o1‑pro

3. Pricing & budgeting tips

1. Prototype with o3; escalate selectively to o1‑pro. At $40 → $600 per 1 M output tokens, even a medium‑length completion (3 k tokens) costs ~$0.12 on o3 and ~$1.80 on o1‑pro.

2. Cap reasoning effort in the Responses API when using o1‑pro to avoid runaway “thinking” tokens.

3. For long‑context summarization or embedding pipelines, the cheaper o3‑mini may already suffice—keep o1‑pro for the rare 100 k‑token deep‑dive.

4. Choosing the right model

5. Bottom line

For virtually every modern SaaS or research workflow—especially those that benefit from image understanding—o3 is the new sensible default. Reserve o1‑pro for the rare, ultra‑high‑stakes jobs where its slower, pricier, but slightly more meticulous reasoning pays off.