OpenAI o3 vs o1: Which Reasoning Model Is Actually Better and When to Use Each

o3 beats o1 on every benchmark OpenAI has published — 87.7% vs 78.0% on GPQA Diamond, 96.7% vs 83.3% on AIME 2024, and 2,727 vs 1,891 Codeforces Elo — and costs 33% less per token.

That's the short answer. The longer answer is more interesting — because the latency, cost-per-task math, and tool support change which model you actually want in production.

OpenAI o3 vs o1: The Full Benchmark and Pricing Comparison

OpenAI o3 outperforms o1 on every public benchmark by 8 to 50+ percentage points, costs roughly 33% less per million tokens ($10 input / $40 output vs $15 / $60), and adds image input support that o1 lacks. The trade-off is latency: o3's default reasoning effort burns 25% more thinking tokens than o1, pushing typical response times from 9 seconds to 12 seconds. For most production use cases, that's an easy trade.

Source: OpenAI o3 system card (December 2024), o1 system card (December 2024), and OpenAI API pricing page as of June 2026.

Where o3 Wins Decisively Over o1

The headline benchmark numbers undersell what o3 actually does differently in production. Four areas matter the most.

The Real Cost: o3 vs o1 Per-Task Math

Token pricing is misleading because reasoning models charge for invisible chain-of-thought tokens. On a typical medium-complexity prompt — a SWE-Bench-style bug fix or a 5-paragraph research question — here's what an end-to-end request actually costs.

o3 costs 19% less per request than o1 at medium reasoning effort. But both reasoning models cost 33–40x more than GPT-4o on the same prompt. The right architecture is almost always: route easy queries to GPT-4o, escalate hard ones to o3, never default to o3 for everything. Tracking this build-vs-buy math is the same logic that drives AI startup valuations in 2026 — the winners route compute, they don't spray it.

When to Use o3 vs o1 vs GPT-4o

o3-mini vs o1-mini: The Cheaper Reasoning Models

Both o3 and o1 ship with mini variants. o3-mini is priced at $1.10 per million input tokens and $4.40 per million output tokens — about 9x cheaper than full o3 and competitive with GPT-4o-mini on price. o1-mini sits at $3.00 / $12.00 per million tokens. The mini tiers are where the cost math actually works for high-volume production reasoning.

The interesting line is o3-mini high — it beats full o1 on GPQA (79.7% vs 78.0%) and AIME (87.3% vs 83.3%) while costing 14x less. For most production reasoning workloads, o3-mini high is the right starting point. Only escalate to full o3 when accuracy on the hardest 5–10% of queries actually moves a business metric.

How OpenAI o3 Compares to Claude and Gemini Reasoning

Three reasoning model families are competitive at the frontier in 2026: OpenAI o3, Anthropic Claude 3.7 Sonnet with extended thinking, and Google Gemini 2.5 Pro. Here's how they actually stack up on the benchmarks buyers care about.

o3 leads on raw benchmarks but Claude 3.7 Sonnet costs 3.3x less on input and 2.7x less on output for nearly identical SWE-Bench performance. Gemini 2.5 Pro is the cheapest reasoning model at the frontier and has the largest context window (1M+ tokens), making it the right pick for long-document analysis. For pure coding agents, the right architecture in 2026 is usually o3 for hard plans and Claude Sonnet for execution — the same way AI landscape buyers are routing across providers, not standardizing on one.

The Migration Path: o1 to o3 in Production

If you're already running o1 in production, the migration to o3 is mechanically simple but evaluatively non-trivial. Three things to know: