Google
Gemini 4 Argon预览
Google deep reasoning frontier research model featuring native multimodal chain-of-thought, sub-second video analysis, and 2M token active context.
智商指数
93.6 #6
AAQI 94.0
输出速度
88 t/s
ITL 11.4 ms/tok
混合价格 (3:1)
$5 /1M
输入 $2.5/M · 输出 $10/M
上下文
2M
≈ 5,000 页 A4 · 最大输出 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Gemini 4 Argon
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Largest context window in production — 2M tokens
- All four modalities native in a single architecture
- Frontier coding, financial research and legal drafting
- Purpose-built for autonomous vulnerability research
需要注意的权衡
- Staged rollout — full API access still ramping
- Deep Think adds latency and token spend
- Proprietary; no open weights
杀手级用例
Cyber defense triage
Autonomous vuln discovery and remediation — the Fairwind program's headline workload.
2M-token corpus analysis
Entire legal archives, monorepos or video libraries in one context.
Multimodal research
Cross-referencing text, audio calls and video feeds natively.
开发者快速上手与 API
请先设置
GEMINI_API_KEY — 获取 API 密钥curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-4-argon:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "content-type: application/json" \
-d '{
"contents": [{"parts": [{"text": "Refactor this function for O(n) time and explain the change."}]}]
}'from google import genai
client = genai.Client() # reads GEMINI_API_KEY
resp = client.models.generate_content(
model="gemini-4-argon",
contents="Refactor this function for O(n) time and explain the change.",
)
print(resp.text)const res = await fetch(
`https://generativelanguage.googleapis.com/v1beta/models/gemini-4-argon:generateContent`,
{
method: "POST",
headers: {
"x-goog-api-key": process.env.GEMINI_API_KEY,
"content-type": "application/json",
},
body: JSON.stringify({ contents: [{ parts: [{ text: "Refactor this function for O(n) time and explain the change." }] }] }),
}
);
console.log((await res.json()).candidates[0].content.parts[0].text);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时对决
实时规格与架构
底层解析
Gemini 4 Argon — native multimodal transformer
- Native multimodal chain-of-thought — reasons over text, image, audio and video in one pass
- 2M-token context — the largest production window tracked
- Deep Think mode trades latency for accuracy on hard problems
- First Gemini 4-generation model; rolled out to trusted cyber defenders via the Fairwind program before GA
发布日期
2026年10月5日
架构
Gemini 4 Argon — native multimodal transformer
参数量
Not disclosed
开源协议
Proprietary
上下文窗口
2,000,000 tokens
最大输出
65,536 tokens
模态
text + image + audio + video
缓存价格
读 $0.25 · 写 $2.5 /1M
推理
思考力度档位
输入价格
$2.5/M
输出价格
$10/M
输出速度
88 t/s
首Token延迟
—
数据更新
2026年10月10日
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026gemini4argon,
title = {Benchmark Analysis and Intelligence Evaluation of Gemini 4 Argon},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gemini-4-argon}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
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基于能力、定价和基准测试得分与 Gemini 4 Argon 最接近的替代模型。