gemini-2.5-pro

Gemini 2.5 Pro is Google's most capable model in the Gemini 2.5 generation. It features a "thinking" mode — extended chain-of-thought reasoning that applies additional compute before producing a final answer — and achieves top scores on coding benchmarks. It supports a 1M-token context window (with 2M available in preview), vision inputs, audio understanding, video analysis, tool use, and streaming. It is available on Google AI Studio and Vertex AI.

Gemini 2.5 Pro is priced at $1.25 per million input tokens (for prompts up to 200K tokens) and $10.00 per million output tokens, with thinking tokens billed at $3.50/1M. For prompts over 200K tokens, input pricing rises to $2.50/1M. At 1,000 input + 500 output tokens per call, it costs approximately $0.0063 per 1,000 API calls. The pricing structure makes it particularly cost-effective for long-context workloads where the 1M token window is genuinely needed, as the per-token input rate is lower than most competitors at comparable context lengths.

Gemini 2.5 Pro's 1M-token context window is the largest among widely available production models, enabling analysis of entire codebases, book-length documents, hours of video transcripts, or very long multi-turn conversations in a single API call. Median latency in standard mode is approximately 500–800ms TTFT; thinking mode adds 10–120 seconds depending on complexity. Throughput is approximately 100 tokens per second for visible output. Maximum output is 65,536 tokens in standard mode and higher in thinking mode. The large context window is genuinely useful for a class of tasks — full-repository code analysis, long legal document review, multi-document research synthesis — that require chunking with smaller-context models.

Gemini 2.5 Pro is best suited to tasks requiring large context windows (its primary differentiator), coding, long-document analysis, multimodal workflows involving video or audio alongside text, and reasoning tasks where thinking mode is enabled. Its video understanding capability — analyzing video frames and audio together — is more mature than most competitors and useful for content moderation, video summarization, and multimedia analysis pipelines. Tool use is reliable and supports parallel function calls. For pure text reasoning without large context requirements, o3 or Claude 3.7 Sonnet may offer better quality-to-cost ratios depending on the specific task.

Gemini 2.5 Pro sits at the top of Google's model lineup, above Gemini 2.5 Flash and Gemini 2.0 Flash. Its main competitors are extended-reasoning models from OpenAI and Anthropic, and its own smaller sibling Gemini 2.5 Flash (much cheaper, 80–85% of the quality for most tasks). On coding benchmarks, Gemini 2.5 Pro ranks among the top models available. For teams with large-context requirements, it is often the most cost-effective frontier option. Its intelligence score of approximately 92/100 reflects top-tier capability, particularly on coding and long-context tasks.