Begin constructing with Gemini 2.5 Flash

At the moment we’re rolling out an early model of Gemini 2.5 Flash in preview by way of the Gemini API by way of Google AI Studio and Vertex AI. Constructing upon the favored basis of two.0 Flash, this new model delivers a significant improve in reasoning capabilities, whereas nonetheless prioritizing velocity and price. Gemini 2.5 Flash is our first absolutely hybrid reasoning mannequin, giving builders the power to show considering on or off. The mannequin additionally permits builders to set considering budgets to seek out the proper tradeoff between high quality, value, and latency. Even with considering off, builders can preserve the quick speeds of two.0 Flash, and enhance efficiency.

Our Gemini 2.5 fashions are considering fashions, able to reasoning by way of their ideas earlier than responding. As a substitute of instantly producing an output, the mannequin can carry out a “considering” course of to higher perceive the immediate, break down complicated duties, and plan a response. On complicated duties that require a number of steps of reasoning (like fixing math issues or analyzing analysis questions), the considering course of permits the mannequin to reach at extra correct and complete solutions. Actually, Gemini 2.5 Flash performs strongly on Arduous Prompts in LMArena, second solely to 2.5 Professional.

Our most cost-efficient considering mannequin

2.5 Flash continues to steer because the mannequin with one of the best price-to-performance ratio.

Nice-grained controls to handle considering

We all know that completely different use instances have completely different tradeoffs in high quality, value, and latency. To offer builders flexibility, we’ve enabled setting a considering finances that provides fine-grained management over the utmost variety of tokens a mannequin can generate whereas considering. A better finances permits the mannequin to purpose additional to enhance high quality. Importantly, although, the finances units a cap on how a lot 2.5 Flash can suppose, however the mannequin doesn’t use the total finances if the immediate doesn’t require it.

The mannequin is educated to know the way lengthy to suppose for a given immediate, and due to this fact robotically decides how a lot to suppose based mostly on the perceived process complexity.

If you wish to preserve the bottom value and latency whereas nonetheless bettering efficiency over 2.0 Flash, set the considering finances to 0. You may as well select to set a particular token finances for the considering section utilizing a parameter within the API or the slider in Google AI Studio and in Vertex AI. The finances can vary from 0 to 24576 tokens for two.5 Flash.

The next prompts display how a lot reasoning could also be used within the 2.5 Flash’s default mode.

Prompts requiring low reasoning:

Instance 1: “Thanks” in Spanish

Instance 2: What number of provinces does Canada have?

Prompts requiring medium reasoning:

Instance 1: You roll two cube. What’s the likelihood they add as much as 7?

Instance 2: My fitness center has pickup hours for basketball between 9-3pm on MWF and between 2-8pm on Tuesday and Saturday. If I work 9-6pm 5 days every week and need to play 5 hours of basketball on weekdays, create a schedule for me to make all of it work.

Prompts requiring excessive reasoning:

Instance 1: A cantilever beam of size L=3m has an oblong cross-section (width b=0.1m, top h=0.2m) and is fabricated from metal (E=200 GPa). It’s subjected to a uniformly distributed load w=5 kN/m alongside its whole size and some extent load P=10 kN at its free finish. Calculate the utmost bending stress (σ_max).

Instance 2: Write a perform evaluate_cells(cells: Dict[str, str]) -> Dict[str, float] that computes the values of spreadsheet cells.

Every cell accommodates:

• Or a method like "=A1 + B1 * 2" utilizing +, -, *,/ and different cells.

Necessities:

• Resolve dependencies between cells.

• Deal with operator priority (*/ earlier than +-).

• Detect cycles and lift ValueError("Cycle detected at ").

• No eval(). Use solely built-in libraries.

Begin constructing with Gemini 2.5 Flash at present

Gemini 2.5 Flash with considering capabilities is now out there in preview by way of the Gemini API in Google AI Studio and in Vertex AI, and in a devoted dropdown within the Gemini app. We encourage you to experiment with the thinking_budget parameter and discover how controllable reasoning will help you remedy extra complicated issues.

from google import genai

consumer = genai.Shopper(api_key="GEMINI_API_KEY")

response = consumer.fashions.generate_content(
  mannequin="gemini-2.5-flash-preview-04-17",
  contents="You roll two cube. What’s the likelihood they add as much as 7?",
  config=genai.varieties.GenerateContentConfig(
    thinking_config=genai.varieties.ThinkingConfig(
      thinking_budget=1024
    )
  )
)

print(response.textual content)

Python

Discover detailed API references and considering guides in our developer docs or get began with code examples from the Gemini Cookbook.

We are going to proceed to enhance Gemini 2.5 Flash, with extra coming quickly, earlier than we make it typically out there for full manufacturing use.

^{*_{Mannequin pricing is sourced from Synthetic Evaluation & Firm Documentation}}