Capability · Comparison
Gemini 2.5 Pro vs OpenAI o3
Gemini 2.5 Pro and OpenAI o3 are the two most capable reasoning-first frontier models as of 2026-04. 2.5 Pro pairs reasoning with a 2M-token context and native multimodal — it's excellent at understanding whole codebases or hour-long video. o3 is more reasoning-dense and tends to win on the hardest math and research-level problems. Pick based on whether you need breadth of input or depth of thought.
Side-by-side
| Criterion | Gemini 2.5 Pro | OpenAI o3 |
|---|---|---|
| Context window | 2,000,000 tokens | 200,000 tokens |
| Reasoning depth (GPQA Diamond, as of 2026-04) | ≈84% | ≈88% |
| Math (AIME 2024) | ≈88% | ≈96% |
| Coding (SWE-bench Verified) | ≈64% | ≈72% |
| Multimodal | Text, image, audio, video (native) | Text, image |
| Pricing ($/M input) | $1.25 | $10 |
| Pricing ($/M output) | $10 | $40 |
| Thinking token visibility | Summary thoughts available | Reasoning summary via API |
| Interactive latency | Moderate-to-slow with thinking | Slow — reasoning dominates |
Verdict
For hard reasoning work — research math, complex coding agents, novel problem-solving — o3 still has the edge and usually justifies the higher cost. For long-context reasoning over a whole codebase, many hours of video, or a giant document, Gemini 2.5 Pro's 2M window and native multimodal are irreplaceable. Both are deliberately slow; if you need reasoning at interactive latency, look at the Flash/mini tiers of each family instead.
When to choose each
Choose Gemini 2.5 Pro if…
- You need to reason across 200k+ tokens or entire codebases.
- Multimodal reasoning matters (video, audio, technical diagrams).
- Cost per million tokens is a hard constraint.
- You're already deployed on Vertex or Google Cloud.
Choose OpenAI o3 if…
- You need the strongest reasoning on frontier math or research problems.
- Task is narrow but deep (single hard problem, not a big document).
- You need peak coding agent reliability under hard problems.
- You're on Azure OpenAI or the OpenAI ecosystem.
Frequently asked questions
Which is smarter for coding agents?
On SWE-bench Verified, o3 is ahead in most 2026 evaluations. In practice Gemini 2.5 Pro catches up when the task requires reading a large codebase because of its context advantage.
Why is o3 so much more expensive?
o3 uses substantially more thinking tokens per answer. You pay for the reasoning itself, which for frontier problems is often worth it — for routine tasks it's overkill.
Can I stream thinking tokens?
Both models expose reasoning summaries via their APIs. Full raw chain-of-thought is restricted on o3; 2.5 Pro returns structured thought summaries.
Sources
- Google DeepMind — Gemini 2.5 Pro — accessed 2026-04-20
- OpenAI — o3 — accessed 2026-04-20