OpenAI o1 vs o3: What the Reasoning Models Actually Do in 2026

OpenAI’s o-series started with o1 in late 2024, shipped o3 in 2025, and as of mid-2026 sits alongside o4-mini as the current lineup. The question isn’t which is newer — it’s which one actually fits your workload and your budget.

Here’s a direct comparison of what o1 and o3 do differently, with the benchmark numbers and pricing that matter for real use.

> **Pricing verified:** OpenAI official model pages (openai.com/api, accessed 2026-10-19). Benchmark scores from OpenAI model cards and third-party aggregation (aicomparison.ai, accessed 2026-09-01).

What They Are and How They Stack Up

Both are “thinking” models — they reason through problems step-by-step before answering, rather than going straight from prompt to output. That reasoning process is visible as “thinking tokens” in the API response, and it’s billed as output tokens. The architecture is fundamentally the same; what changed between o1 and o3 is scale: more training, more reasoning compute, more capability at every price point.

o1 is the legacy model in 2026. o3 is the current flagship.

Benchmarks

o3 leads on every public benchmark, often by double-digit margins. The SWE-bench gap (71.7% vs 48.9%) is the most practical signal: o3 fixes about 72% of verified real-world repository issues versus o1’s 49%.

Pricing

o3 is about 13% of o1’s output price while scoring higher everywhere. If you’re still paying $60/M output tokens for o1, switching to o3 cuts per-call cost significantly.

Where o3 Actually Wins

Competition math and hard formal reasoning. AIME, Codeforces, FrontierMath — these are where o3’s compute budget shows up most visibly. If your work involves mathematical proofs, competitive programming, or formal logic, o3 is a different class of tool.

Complex multi-step coding. o3 handles systems-level programming with many interacting constraints better than o1. The SWE-bench gap is real.

Tool use during reasoning. o3 runs web search, Python, and image tools while it reasons — it can pull a current figure or test a code snippet mid-reasoning. o1 cannot; it only reasons from its training data and the prompt. For any task where current information matters, this is a significant functional difference.

Visual reasoning. o3 treats images as reasoning objects — it can zoom into a chart, read a diagram, or rotate a floor plan mid-solution. o1 accepts images but doesn’t manipulate them during reasoning.

Where o1 Still Makes Sense

Existing pinned production deployments. If you have o1 running in a production pipeline with validated behavior and you don’t want to risk a behavioral change, stay on o1.

Reproduction testing. When you need stable, fixed outputs for regression testing or comparisons, o1’s behavior is more predictable.

o1 at 83.3% on AIME still beats most humans and most models. For most practical coding tasks — CRUD apps, scripts, straightforward features — o1 and o3 are both overkill, and GPT-4o or Claude Sonnet are faster and cheaper.

When to Use Which

Use o3 when:

Use o1 when:

Use o3-mini for: Most practical reasoning tasks where full o3 is overkill — it matches o1 on most benchmarks at roughly GPT-4o cost.

Don’t use either for: Simple Q&A, drafting, summarization, or any task where speed matters more than depth. Use GPT-4o or Claude instead.

Both models are slow. o3 exposes low/medium/high reasoning-effort settings, letting you trade latency for depth. Watch output token spend carefully — both can emit tens of thousands of thinking tokens per query, and those are billed as output.

Bottom Line

o3 is a clean upgrade over o1 on every axis: higher quality, more features, lower cost. The only reason to stay on o1 in 2026 is a pinned production deployment you can’t risk changing.

For new work, always start with o3. Use o3-mini for everyday reasoning tasks where full o3 is overkill. Use GPT-4o or Claude for anything that doesn’t need step-by-step deliberation.