Google
Gemini 3.8 Flash (Medium Reasoning)
Balanced reasoning effort variant of Gemini 3.8 Flash for agentic workflows.
智商指数
65.2 #248
前 32% 梯队
输出速度
315 t/s
TTFT 160 ms · ITL 3.2 ms/tok
混合价格 (3:1)
$1.5 /1M
输入 $0.75/M · 输出 $3.75/M
上下文
1M
≈ 2,500 页 A4 · 最大输出 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Gemini 3.8 Flash (Medium Reasoning)
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
- 1M context — swallows whole repos, codebases or corpora in one pass
- Up to 65,536 output tokens per response
- 302 tok/s output — fast enough for real-time agents
- Sub-200ms time-to-first-token
需要注意的权衡
- Architecture and weights undisclosed — API access only
杀手级用例
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
Math & proof work
Step-verified reasoning for olympiad math, theorem proving and formal verification.
Real-time agents
Low-latency loops for voice, gaming and interactive copilots.
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-3-8-flash-medium: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-3-8-flash-medium",
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-3-8-flash-medium: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
实时规格与架构
底层解析
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.075/1M tokens.
- Native multimodality: text, image, audio, video in a single model.
发布日期
2026年9月1日
参数量
Not disclosed
开源协议
Proprietary
上下文窗口
1,000,000 tokens
最大输出
65,536 tokens
模态
text + image + audio + video
缓存价格
读 $0.075 · 写 $0.9375 /1M
推理
思考力度档位
输入价格
$0.75/M
输出价格
$3.75/M
输出速度
315 t/s
首Token延迟
160 ms
数据更新
2026年9月2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{llmpodium2026gemini38flashmedium,
title = {Benchmark Analysis and Intelligence Evaluation of Gemini 3.8 Flash (Medium Reasoning)},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gemini-3-8-flash-medium}
}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。
预览
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替代模型推荐
基于能力、定价和基准测试得分与 Gemini 3.8 Flash (Medium Reasoning) 最接近的替代模型。