Gemini Native Format Guide

Call the official Gemini generateContent format through APIYI: google-genai SDK setup, streaming, thinking_level controls, and thought signatures.

APIYI fully supports the official Gemini native format (the /v1beta generateContent endpoint): point the base_url at https://api.apiyi.com and your existing Gemini code and official SDKs migrate seamlessly — no format conversion needed.

This page is based on the official Google documentation (ai.google.dev/gemini-api/docs, as of June 2026). All examples are copy-paste ready.

Why the Native Format

The OpenAI-compatible format can call Gemini too, but the following are native-only:

• Full thinking controls: thinking_level (Gemini 3 series) / thinking_budget (2.5 series), thought summaries, thought signatures
• Native multimodal Parts: inline images / audio / video, with media_resolution cost control — see Multimodal & Code Execution
• Code execution tool: code_execution runs Python in a sandbox
• Fine-grained usage fields: thoughts_token_count, cached_content_token_count, and more

For plain text chat, or one codebase across multiple vendors, use OpenAI Compatible Mode instead.

Quick Start

Use Google’s official unified SDK google-genai (the legacy google-generative-ai was sunset on November 30, 2025 (UTC)):

pip install google-genai

Python

from google import genai

client = genai.Client(
    api_key="YOUR_API_KEY",  # your APIYI key
    http_options={"base_url": "https://api.apiyi.com"}
)

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Introduce yourself in one sentence"
)
print(response.text)

The base_url is https://api.apiyi.com (without /v1) — different from the OpenAI-compatible format’s https://api.apiyi.com/v1. Use your APIYI key, not a Google AI Studio key.

Streaming

stream = client.models.generate_content_stream(
    model="gemini-3.5-flash",
    contents="Write a short essay on quantum computing"
)

for chunk in stream:
    print(chunk.text, end="", flush=True)

Thinking Controls

Gemini models think by default, and the two generations use different parameters — mixing them raises an error:

Model series	Parameter	Values
Gemini 3 / 3.1 / 3.5	thinking_level	minimal (Flash family only) / low / high (default)
Gemini 2.5	thinking_budget	token cap (e.g. 0–8192); model auto-controls if unset

Passing both thinking_level and thinking_budget to a Gemini 3 series model returns an error — pick one (use thinking_level for the 3 series).

from google.genai import types

# Gemini 3 series: level-based control
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Prove that the square root of 2 is irrational",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    )
)

# Gemini 2.5 series: token-budget control
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Simple question, be quick",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=0)  # thinking off
    )
)

Choosing a level: minimal for low-latency simple tasks (classification, extraction); low for routine chat; high for complex reasoning and code. Thinking tokens bill at the output rate — higher levels cost more.

Thought summaries and thought signatures

• Thought summaries: include_thoughts=True returns a summary of the reasoning (parts where part.thought is True)
• Thought signatures: encrypted reasoning state introduced with Gemini 3. In multi-turn conversations (especially function calling), pass the thought_signature from the response back unchanged so the model can continue its reasoning chain. Official SDKs handle this automatically; don’t strip the field in hand-written REST calls — see Function Calling

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Analyze the time complexity of: def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high", include_thoughts=True)
    )
)

for part in response.candidates[0].content.parts:
    if getattr(part, "thought", False):
        print(f"[Thought summary] {part.text}")
    else:
        print(f"[Final answer] {part.text}")

Common Config Parameters

Passed via config (GenerateContentConfig):

Parameter	Description
system_instruction	System prompt
temperature	0–2. Google recommends keeping the default 1.0 for Gemini 3 series — lowering it can hurt reasoning quality
max_output_tokens	Output cap (includes thinking tokens)
thinking_config	Thinking controls, see above
response_mime_type	Set application/json to force JSON output
response_schema	Schema constraint for structured JSON output
tools	Function declarations / code_execution and other tools
media_resolution	Multimodal input cost control, see Multimodal page

Usage Fields (usage_metadata)

usage = response.usage_metadata
print(f"Input: {usage.prompt_token_count}")
print(f"Output: {usage.candidates_token_count}")
print(f"Thinking: {usage.thoughts_token_count}")
print(f"Cache hits: {usage.cached_content_token_count}")

Field	Description	Billing
prompt_token_count	Input tokens	Input rate
candidates_token_count	Output tokens	Output rate
thoughts_token_count	Thinking tokens	Output rate — tune the level to save
cached_content_token_count	Cached tokens	Official discount, see Cache Billing
total_token_count	Total	—

Supported Models and Pricing

Model	Input (per 1M tokens)	Output (per 1M tokens)	Notes
gemini-3.5-flash	$1.50	$9.00	Current workhorse — beats 3.1 Pro on several benchmarks, 1M context
gemini-3.1-pro-preview	$1.80	$10.80	Pro flagship
gemini-3-pro-preview	$1.80	$10.80	Previous Pro
gemini-3-flash-preview	$0.44	$2.64	Light and fast
gemini-3.1-flash-lite	$0.25	$1.50	Ultra-budget
gemini-2.5-pro	$1.25	$10.00	2.5-series Pro
gemini-2.5-flash	$0.30	$2.40	2.5-series workhorse
gemini-2.5-flash-lite	$0.10	$0.40	Cheapest

Some models have -thinking / -nothinking alias variants (e.g. gemini-3-flash-preview-nothinking) that pin thinking on/off — handy for clients where you can’t change request parameters. Full list: Models & Pricing.

Native vs OpenAI-Compatible

Feature	Gemini native	OpenAI-compatible
base_url	https://api.apiyi.com	https://api.apiyi.com/v1
SDK	google-genai	openai
Thinking control	thinking_level / thinking_budget	reasoning_effort
Thought summaries / signatures	✅	❌
Code execution tool	✅	❌
Media input	Native inline Parts (PIL / bytes)	Base64 image_url
Cache hit field	cached_content_token_count	cached_tokens

Notes

• The Files API is not supported (client.files.upload()); media must be passed inline and each file must stay under 20MB — see Multimodal & Code Execution
• Cache discounts and hit-rate expectations: Cache Billing

Related Links

• This group: Multimodal & Code Execution · Cache Billing · Function Calling
• Get / manage tokens: https://api.apiyi.com/token
• Official Google docs: ai.google.dev/gemini-api/docs