Gemini 2.5 Pro vs o3

The practical split starts with context. Gemini 2.5 Pro exposes a 1M-token window, so it can take long documents, large codebases, PDFs, image-heavy reports, and video or audio transcripts without as much chunking. o3 caps at 200K tokens, which is still large but becomes a hard ceiling for million-token analysis.

Pricing favors Gemini on the routes most likely to matter for high-volume evaluation. Google AI Studio lists Gemini 2.5 Pro at $1.25/M input and $10/M output up to 200K tokens, with batch at $0.625/M input and $5/M output and cache reads at $0.125/M. OpenAI's o3 docs list $2/M input, $8/M output, and $0.50/M cached input; the same official page lists Batch API prices of $1/M input, $4/M output, and $0.25/M cached input.

Gemini's long-context pricing needs a visible caveat. Above 200K input tokens, Gemini 2.5 Pro moves to $2.50/M input and $15/M output, with higher cache-read and batch rates. That higher tier is still useful because o3 cannot accept those long inputs directly, but teams should estimate true million-token request cost instead of extrapolating from the standard tier.

The benchmark view favors o3 for reasoning and coding. o3 leads SWE-bench Verified by 7.9 points, GPQA Diamond by 1.3 points, AIME 2025 by 2.2 points, LiveCodeBench by roughly 10 points in the researched variants, MMMU by 1.2 points, and Humanity's Last Exam by 1.5 points. Treat those as task-specific signals, not a reason to ignore Gemini when context, media input, or cost dominate.

Terminal-Bench is intentionally asymmetric in the seed. Gemini 2.5 Pro has a public Terminal-Bench 2.0 row at 32.6% with a Terminus 2 agent; o3 does not have a public submission in the researched sources. The page should mention the absence instead of fabricating a comparable o3 row.