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Google: Gemini 4 CarbonNEW

google/gemini-4-carbon
Gemini 4 Carbon is a frontier Gemini 4 model from Google for coding, agentic and complex reasoning work. It takes text, image, audio, video and file input, supports long decode of up to 1M output tokens through continuation, and controls thinking depth with low, medium and high reasoning effort.
Modalities
Price in / out
Cache, read / write
Context / Max output
1.05M / 944K
Released
Nov 10, 2026

Additional rates

Flex, in / out

Parameters

messagesrequired
reasoning_effortlow · medium · high
max_tokensoptional · alias max_completion_tokens
streamtrue · false
toolsfunction calling (OpenAI format)
tool_choiceauto · none · required
response_formatjson_object · json_schema
web_search · · ·
providerrouting: order · only · ignore · sort
service_tierflex · priority
stream_options{"include_usage": true}
image_urlimage (data: URL)
filePDF · DOCX · TXT · CSV · XLSX · PPTX (data: URL)
Fine-tuning · 4
temperature0–2
stopoptional
seedoptional
top_p0–1

How to use via API

from openai import OpenAI

client = OpenAI(base_url="https://api.mixen.ai/v1", api_key="mxn-...")
resp = client.chat.completions.create(
    model="google/gemini-4-carbon",
    messages=[{"role": "user", "content": "Привет"}],
    reasoning_effort="low",  # low · medium · high
    temperature=0.7,
    max_tokens=1024,
    stream=False,
)
print(resp.choices[0].message.content)

# Стриминг: stream=True — ответ приходит чанками
for chunk in client.chat.completions.create(
    model="google/gemini-4-carbon",
    messages=[{"role": "user", "content": "Привет"}],
    stream=True,
):
    print(chunk.choices[0].delta.content or "", end="", flush=True)

# эконом-тир: тот же ответ вдвое дешевле, но ниже приоритет в очереди
cheap = client.chat.completions.create(
    model="google/gemini-4-carbon",
    messages=[{"role": "user", "content": "Привет"}],
    extra_body={"service_tier": "flex"},
)

# с файлом (PDF · DOCX · TXT · CSV · XLSX · PPTX)
doc = client.chat.completions.create(
    model="google/gemini-4-carbon",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Кратко перескажи документ"},
        {"type": "file", "file": {
            "filename": "doc.pdf",
            "file_data": "data:application/pdf;base64,JVBERi0...",
        }},
    ]}],
)
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