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Google: Gemini 4 ArgonNEW
google/gemini-4-argontextVisionFilesReasoningVideoAudio
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.
Price in / out
Cache, read / write
Context / Max output
1.05M / 1.05M
Released
Oct 13, 2026
Additional rates
Flex, in / out
Thinking
Web search
Parameters
messagesrequiredreasoning_effortlow · medium · highmax_tokensoptional · alias max_completion_tokensstreamtrue · falsetoolsfunction calling (OpenAI format)tool_choiceauto · none · requiredresponse_formatjson_object · json_schemaweb_search · · · providerrouting: order · only · ignore · sortservice_tierflex · prioritystream_options{"include_usage": true}image_urlimage (data: URL)filePDF · DOCX · TXT · CSV · XLSX · PPTX (data: URL)Fine-tuning · 4
temperature0–2stopoptionalseedoptionaltop_p0–1How 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-argon",
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-argon",
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-argon",
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-argon",
messages=[{"role": "user", "content": [
{"type": "text", "text": "Кратко перескажи документ"},
{"type": "file", "file": {
"filename": "doc.pdf",
"file_data": "data:application/pdf;base64,JVBERi0...",
}},
]}],
)
