Independent
Simba 3.2
Frontier reasoning model tuned for agentic tool use and long-horizon task execution.
Intelligenza
87.8 #31
AAQI 62.0
Velocità Uscita
62 t/s
ITL 16.1 ms/tok
Prezzo combinato (3:1)
$6.5 /1M
Input $2/M · Output $10/M
Contesto
262K
≈ 655 pagine A4 · output max 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Simba 3.2
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
- Strong reasoning-per-dollar vs. flagship tiers
- Reliable multi-step tool calling (τ²-class)
- 262K context at mid-tier pricing
A cosa fare attenzione
- Smaller ecosystem — fewer SDKs and guides
- 62 tok/s mid-pack speed
- Text-only
Casi d'uso killer
Autonomous research agents
Multi-step search-and-synthesize workflows.
Tool orchestration
Function calling over large API surfaces.
Analyst copilots
Reasoned synthesis across 262K-token briefs.
Quickstart per sviluppatori e API
Imposta prima
OPENROUTER_API_KEY — ottieni una chiave APIcurl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "simba/simba-3-2",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
)
resp = client.chat.completions.create(
model="simba/simba-3-2",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "simba/simba-3-2",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);OpenRouter-compatible endpoint — one API for 300+ models.
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
Simba 3.2 — reasoning frontier model
- Adjustable reasoning budget with verifier-guided decoding
- 262K context for multi-document tasks
- Tool-use fine-tune targeting multi-step agents
- 32K max output for long structured answers
Data di Rilascio
5 ott 2026
Architettura
Simba 3.2 — reasoning frontier model
Parametri
Not disclosed
Licenza
Proprietary
Finestra di Contesto
262,000 tokens
Output max
32,768 tokens
Modalità
text
Prezzi cache
lettura $0.2 · scrittura $2.5 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$2/M
Prezzo Uscita
$10/M
Velocità Uscita
62 t/s
Tempo al Primo Token
—
Dati aggiornati
22 ott 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
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Loading badge markdown...BibTeX
@misc{llmpodium2026simba32,
title = {Benchmark Analysis and Intelligence Evaluation of Simba 3.2},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/simba-3-2}
}Incorpora il badge del punteggio
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