Gemini 3.8 Flash
Gemini 3.8 Flash (high reasoning) by Google. High intelligence index (59), output throughput of 302 TPS, and native 1M context window with 90% prompt caching discount.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Radar-Achsen sind aus Kategorie-Scores approximiert, wo direkte Benchmark-Messungen fehlen — siehe Methodik.
Stärken & Trade-offs
Wo das Modell gewinnt
- 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
Worauf achten
- Architecture and weights undisclosed — API access only
Killer-Anwendungsfälle
Developer-Quickstart & API
GEMINI_API_KEY — API-Schlüssel holencurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-8-flash: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",
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: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);Offizielle Ressourcen & Links
Hosts & Latenz vergleichen
Durchsatz, TTFT und Cache-Preise bei Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock und Azure.
Ergebnisse nach Quelle
Kategorie-Punkte
Benchmark-Ergebnisse
Live-Spezifikationen & Architektur
- 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.
Geschätzte Monatsrechnung
Zitieren & Einbetten
Loading badge markdown...@misc{llmpodium2026gemini38flash,
title = {Benchmark Analysis and Intelligence Evaluation of Gemini 3.8 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gemini-3-8-flash}
}Score-Badge einbetten
Fügen Sie Ihrem GitHub- oder Hugging-Face-README ein Live-Podium-Score-Badge hinzu — es verlinkt auf das Live-Ranking.
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