Gemini 1.5 Flash (May) (non-reasoning)

The most direct way to control costs is to control your token count. This applies to both input and output.

• Be Concise: Write the shortest, clearest prompt possible. Remove filler words and redundant examples.
• Give Clear Instructions: Explicitly ask the model for a brief response (e.g., "Answer in one sentence," or "Provide a bulleted list of 5 items.").
• Iterate and Refine: Test different prompt variations to see which one gives you the desired output with the fewest tokens.

Many applications receive identical or highly similar user requests. Sending the same prompt to the API repeatedly is inefficient and costly.

• Cache Responses: Store the responses to common prompts in a database (like Redis or Memcached). Before calling the API, check if the prompt already exists in your cache.
• Benefits: Caching dramatically reduces API calls, lowers costs, and provides instantaneous responses for cached queries, improving user experience.

Before sending a massive document to the model's expensive long-context tier, see if you can reduce its size first.

• Summarize in Chunks: If you need to find themes in a book, first use the cheap, short-context mode to summarize each chapter. Then, combine the summaries and feed that much smaller text into a final prompt.
• Keyword Filtering: Use a simple algorithm or a cheaper model to scan a document for keywords or relevant sections. Only send these extracted sections to 1.5 Flash for detailed analysis.

The max_tokens parameter in an API call is a critical safety net. It puts a hard cap on the number of tokens the model can generate in its response.

• Prevent Runaway Costs: By setting a reasonable limit (e.g., 500 tokens for a summarization task), you prevent the model from generating an unexpectedly long and expensive response.
• Enforce Brevity: This parameter works hand-in-hand with prompt engineering to ensure outputs are both concise and cost-effective.

Gemini 1.5 Flash is a lightweight, fast, and multimodal large language model from Google. It is optimized for speed and high-volume tasks, serving as a more cost-effective and rapid alternative to the more powerful Gemini 1.5 Pro.

Flash is faster and significantly cheaper for most tasks, but it has a lower intelligence score. Pro is more capable for complex reasoning, nuance, and difficult instruction-following. Both models share the same groundbreaking 1 million token context window and multimodal (text and image) capabilities. The choice depends on whether your application prioritizes speed and cost or raw intellectual power.

It means the model can process information from more than one type of input, or 'modality'. Specifically, Gemini 1.5 Flash can understand and analyze both text and images (including individual frames from videos) within the same prompt. You can upload an image and ask the model questions about it.

While the model's architecture supports a 1M token context, there is a significant pricing consideration. The cost per token increases substantially for prompts longer than 128,000 tokens. Therefore, while technically possible, using the full context window is a deliberate choice for high-value tasks that justify the higher cost, rather than a default for all queries.

It excels at high-volume, low-latency applications where cost is a major factor. Ideal use cases include: interactive chatbots, real-time content summarization, data extraction, document classification, visual Q&A, and large-scale analysis of documents or codebases where top-tier reasoning is not the primary requirement.

Based on its launch pricing, it has a very generous free tier for contexts under 128,000 tokens, which covers a wide range of common tasks. For contexts longer than that, a metered pricing model applies, which can become costly. It is crucial to always check the official Google Cloud pricing page for the most current and detailed information, as promotional pricing can change.