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Gemini 3.8 Flash (Low Reasoning)
Low reasoning effort variant of Gemini 3.8 Flash optimized for sub-150ms TTFT and maximum throughput.
Inteligensi
62.0 #318
Tingkat 41% teratas
Kecepatan output
340 t/s
TTFT 140 ms · ITL 2.9 ms/tok
Harga gabungan (3:1)
$1.5 /1M
Input $0.75/M · Output $3.75/M
Konteks
1M
≈ 2,500 halaman A4 · output maks 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Gemini 3.8 Flash (Low Reasoning)
Claude Mythos Preview (#1)
Average (68)
Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.
Kelebihan & Trade-off
Di mana ia menang
- 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
Hal yang perlu diperhatikan
- Architecture and weights undisclosed — API access only
Kasus Penggunaan Killer
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.
Quickstart Developer & API
Atur
GEMINI_API_KEY dulu — dapatkan kunci APIcurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-8-flash-low: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-low",
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-low: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);Sumber Daya & Tautan Resmi
Deployment API
Bandingkan host & latensi
Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Skor per sumber
Skor per kategori
Hasil benchmark
Spesifikasi Live & Arsitektur
Di balik kap
- 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.
Tanggal rilis
1 Sep 2026
Parameter
Not disclosed
Lisensi
Proprietary
Jendela konteks
1,000,000 tokens
Output maks
32,768 tokens
Modalitas
text + image + audio + video
Harga cache
baca $0.075 · tulis $0.9375 /1M
Penalaran
Level effort
Harga input
$0.75/M
Harga output
$3.75/M
Kecepatan output
340 t/s
Waktu ke token pertama
140 ms
Data diperbarui
2 Sep 2026
Estimasi tagihan bulanan
Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…
Kutip & sematkan
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026gemini38flashlow,
title = {Benchmark Analysis and Intelligence Evaluation of Gemini 3.8 Flash (Low Reasoning)},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gemini-3-8-flash-low}
}Sematkan Lencana Skor
Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.
Pratinjau
Markdown
Loading badge snippet...HTML
Loading badge snippet...Lainnya dari Google
Model Alternatif
Alternatif terdekat untuk Gemini 3.8 Flash (Low Reasoning) berdasarkan kemampuan, harga, dan skor benchmark.