Gemini 2.5 Pro
by Google DeepMind·USA·Released
Google's deep-thinking flagship with a 1M-token context window.
About this model
Gemini 2.5 Pro (March 2025) was Google's first 'thinking' model — like OpenAI's o-series, it spends additional compute on internal reasoning before responding. Unlike o1 / o3-mini, the reasoning trace is visible to the user, which Google argues helps debug agent workflows.
The model ships with a 1M-token context window (2M in some configurations) and tops several reasoning benchmarks at release — 84% on GPQA Diamond, 86.7% on MATH. It also brings the strongest video understanding of any frontier model, courtesy of the multimodal-from-scratch Gemini architecture.
Pricing is tiered by context length: $1.25/M input for ≤200K tokens, $2.50/M for >200K. Output is $10/M (or $15/M past 200K). Google offers a generous free tier via AI Studio for prototyping.
Strengths
- •1M-token context (2M in some configs) at competitive pricing
- •Visible reasoning traces — easier to debug than OpenAI's o-series
- •Top-of-leaderboard at launch on GPQA Diamond (84%)
- •Strongest video understanding of any frontier model
- •Generous AI Studio free tier
Limitations
- •Tool-call format is Google-specific, not MCP
- •Coding scores trail Claude 4 family on SWE-bench Verified
- •Pricing structure complicates capacity planning (tier change at 200K tokens)
When to use it
- →Whole-corpus document analysis (1M+ token inputs)
- →Video analysis and content moderation at scale
- →Multi-step reasoning where chain-of-thought visibility matters
- →Workspace-native assistants (Docs, Gmail, Sheets)
Architecture & training
DeepMind has confirmed Gemini 2.5 uses a sparse Mixture-of-Experts architecture trained natively on interleaved text/image/audio/video tokens. The thinking capability was added in post-training via a process Google calls 'Gemini Thinking' — a variant of large-scale RL on chain-of-thought generation. Training infrastructure is Google's TPU v5p superpods.
Benchmarks
| Benchmark | Score | Bar |
|---|---|---|
| GPQA | 84.0 | |
| MATH | 86.7 | |
| MMLU | 85.8 | |
| SWE-bench Verified | 63.8 |