Tell Flash to slow down and think—quality jumps, cost rises.
Price per 1 million tokens
per 1M tokens you send
per 1M tokens you receive
Input tokens represent the text you send to the model for processing. Output tokens represent the model's generated response. Pricing is set by Google DeepMind and reflects current API rates as of October 2025.
per month
per month
per month
Gemini 2.5 Flash (Thinking) by Google DeepMind is a multimodal AI model for chat, writing, and understanding images or audio.
Google DeepMind
Multimodal
Supports text, images, and audio
2000000 tokens
Maximum tokens per request
$0.15
per 1M input tokens
$3.50
per 1M output tokens
Gemini 2.5 Flash (Thinking) is a multimodal large language model from Google DeepMind designed for real‑world applications where speed, quality and cost all matter. It’s priced at $0.15 per million input tokens and $3.50 per million output tokens, so teams can estimate usage‑based spend with simple math.
The model supports a context window of about 2,000,000 tokens, which is enough for long chats, multi‑document prompts, or passing rich system instructions without constant truncation.
Because it accepts images (and in some stacks, audio), developers can build experiences like visual question‑answering, document parsing with screenshots, or voice chat that feels instant.
Customer support, education, and creative tools benefit from the faster response times and broader modality coverage.
In stack choices, many teams compare Gemini 2.5 Flash (Thinking) against ChatGPT 4o. The trade‑off usually comes down to latency tolerance, budget per request, and whether you need image understanding or deeper chain‑of‑thought style reasoning. If you’re cost‑sensitive, keep prompts concise, cache system instructions, and stream outputs so users perceive faster response. If quality is the priority, add brief exemplars and explicit success criteria in the prompt; small guidance often yields outsized gains.
For production, pair the model with guardrails (content filters, schema validators) and log prompts/responses for offline evaluation. Finally, create simple comparison tests—five to ten representative tasks from your app—to verify this model’s answers, latency and cost against your alternatives before you commit. To control spend, consider tiering workloads: route routine queries to a cheaper sibling and reserve this model for complex or customer‑visible moments. Add retries with temperature control, and prefer JSON‑mode or tool calling for structured outputs that slot directly into your pipeline without brittle parsing.
Gemini 2.5 Flash (Thinking) is a multimodal AI model from Google DeepMind. Pricing is $0.15 per 1M input tokens and $3.50 per 1M output tokens. The context window is roughly 2,000,000 tokens, allowing long prompts and documents. Common uses include chat assistants, summarization, knowledge search, report drafting, and, when supported, image understanding or tool use. It integrates well into web apps, backends, and automation pipelines where latency and reliability matter.
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