Simba 3.2
Frontier reasoning model tuned for agentic tool use and long-horizon task execution.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Os eixos do radar são aproximados a partir dos scores de categoria quando faltam medições diretas — veja Metodologia.
Pontos fortes e trade-offs
Onde vence
- Strong reasoning-per-dollar vs. flagship tiers
- Reliable multi-step tool calling (τ²-class)
- 262K context at mid-tier pricing
Pontos de atenção
- Smaller ecosystem — fewer SDKs and guides
- 62 tok/s mid-pack speed
- Text-only
Casos de uso matadores
Quickstart do desenvolvedor e API
OPENROUTER_API_KEY — obter uma chave de 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.
Recursos e links oficiais
Comparar hosts e latência
Throughput, TTFT e preços de cache na Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
Pontuações por fonte
Pontuações por categoria
Resultados de benchmarks
Especificações ao vivo e arquitetura
- 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
Fatura mensal estimada
Citar e incorporar
Loading badge markdown...@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}
}Incorporar selo de pontuação
Adicione um selo de Podium Score ao seu README no GitHub ou Hugging Face — ele aponta para o ranking ao vivo.
Loading badge snippet...Loading badge snippet...Mais de Independent
Modelos alternativos
Principais alternativas para Simba 3.2 com base em recursos, preço e benchmarks.