Google
Gemini 3.8 Flash (Medium Reasoning)
Balanced reasoning effort variant of Gemini 3.8 Flash for agentic workflows.
Intelligenza
65.2 #248
Fascia top 32%
Velocità Uscita
315 t/s
TTFT 160 ms · ITL 3.2 ms/tok
Prezzo combinato (3:1)
$1.5 /1M
Input $0.75/M · Output $3.75/M
Contesto
1M
≈ 2,500 pagine A4 · output max 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)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- 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
A cosa fare attenzione
- Architecture and weights undisclosed — API access only
Casi d'uso 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 per sviluppatori e API
Imposta prima
GEMINI_API_KEY — ottieni una chiave APIcurl "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);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
- 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.
Data di Rilascio
1 set 2026
Parametri
Not disclosed
Licenza
Proprietary
Finestra di Contesto
1,000,000 tokens
Output max
65,536 tokens
Modalità
text + image + audio + video
Prezzi cache
lettura $0.075 · scrittura $0.9375 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$0.75/M
Prezzo Uscita
$3.75/M
Velocità Uscita
315 t/s
Tempo al Primo Token
160 ms
Dati aggiornati
2 set 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
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}
}Incorpora il badge del punteggio
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Modelli alternativi
Migliori alternative a Gemini 3.8 Flash (Medium Reasoning) in base a capacità, prezzi e benchmark.