Capability · Comparison
Gemini 1.5 Pro vs Gemini 2.5 Pro
Gemini 1.5 Pro was the model that normalized 1M-token context windows and made long-document AI practical. Gemini 2.5 Pro doubles that to 2M and adds meaningfully better reasoning, coding, and tool use. For new work in 2026, 2.5 Pro is the default; 1.5 Pro remains relevant for pinned production deployments and legacy Vertex AI contracts.
Side-by-side
| Criterion | Gemini 1.5 Pro | Gemini 2.5 Pro |
|---|---|---|
| Context window | 1,000,000 tokens | 2,000,000 tokens |
| MMLU | ~82% | ~87% |
| Coding (HumanEval) | ~84% | ~92% |
| Reasoning (GPQA Diamond) | ~59% | ~84% |
| Pricing ($/M input, up to 200k) As of 2026-04; both scale up past 200k tokens. | $1.25 | $1.25 |
| Pricing ($/M output) | $5 | $10 |
| Multimodal | Text, vision, audio, video | Text, vision, audio, video |
| Tool-use reliability | Decent | Much stronger |
| Status in 2026 | Legacy, still supported | Current flagship |
Verdict
Gemini 2.5 Pro is a cleaner, stronger model than 1.5 Pro in nearly every respect — better reasoning, better coding, better tool use, longer context. Input pricing is unchanged at the bulk tier; output pricing is higher, reflecting the quality uplift. For new work always start on 2.5 Pro. For existing 1.5 Pro deployments, the migration is worth scheduling but not urgent — unless you're blocked on reasoning quality.
When to choose each
Choose Gemini 1.5 Pro if…
- You have a pinned production deployment on 1.5 Pro.
- You're on a legacy Vertex AI contract.
- Your output volume is very high and the lower output price matters.
- You've validated 1.5 Pro's long-context behavior for your specific task.
Choose Gemini 2.5 Pro if…
- You're starting new work — this is the default.
- You need stronger reasoning or coding than 1.5 Pro provides.
- You need a 2M-token context window.
- You need stronger tool-use reliability for agentic workflows.
Frequently asked questions
Should I migrate from 1.5 Pro to 2.5 Pro?
Yes, eventually. 2.5 Pro is better on every axis except raw output pricing. Don't force-migrate in the middle of a release — benchmark first.
Is 2M context real in 2.5 Pro?
Yes, with respectable recall, but reasoning quality still degrades past 500k-1M tokens for all long-context models. Don't treat the full 2M as a quality cliff-free zone.
What about Gemini 3?
In preview as of early 2026, not broadly available at the Pro tier yet. This comparison covers the models you can actually deploy today.
Sources
- Google — Gemini 1.5 technical report — accessed 2026-04-20
- Google — Gemini 2.5 — accessed 2026-04-20