Independent

Simba 3.2

Frontier reasoning model tuned for agentic tool use and long-horizon task execution.

#31dirilis 5 Okt 2026·Proprietary·Not disclosed
text262K ctxpenalaran
Penyedia APIJalankan Model →Peringkat
Inteligensi
87.8 #31
AAQI 62.0
Kecepatan output
62 t/s
ITL 16.1 ms/tok
Harga gabungan (3:1)
$6.5 /1M
Input $2/M · Output $10/M
Konteks
262K
≈ 655 halaman A4 · output maks 32,768

Quality Domain Radar

6-Axis Frontier Evaluation vs Category Average & Flagship

Simba 3.2
Claude Mythos Preview (#1)
Average (68)
Coding (62)Mathematics (84)Hard Science (GPQA) (88)Agentic & Tool Use (70)Knowledge (MMLU) (50)Vision / Multimodal (55)

Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.

Kelebihan & Trade-off

Di mana ia menang

  • Strong reasoning-per-dollar vs. flagship tiers
  • Reliable multi-step tool calling (τ²-class)
  • 262K context at mid-tier pricing

Hal yang perlu diperhatikan

  • Smaller ecosystem — fewer SDKs and guides
  • 62 tok/s mid-pack speed
  • Text-only

Kasus Penggunaan 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 Developer & API

Atur OPENROUTER_API_KEY dulu — dapatkan kunci API
curl 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."}]
  }'

OpenRouter-compatible endpoint — one API for 300+ models.

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
Lihat matriks penyedia →

Skor per sumber

Spesifikasi Live & Arsitektur

Di balik kap
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
Tanggal rilis
5 Okt 2026
Arsitektur
Simba 3.2 — reasoning frontier model
Parameter
Not disclosed
Lisensi
Proprietary
Jendela konteks
262,000 tokens
Output maks
32,768 tokens
Modalitas
text
Harga cache
baca $0.2 · tulis $2.5 /1M
Penalaran
Level effort
Harga input
$2/M
Harga output
$10/M
Kecepatan output
62 t/s
Waktu ke token pertama
—
Data diperbarui
22 Okt 2026

Estimasi tagihan bulanan

Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…

Kutip & sematkan

Badge Markdown GitHub
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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}
}

Sematkan Lencana Skor

Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.

Pratinjau
Simba 3.2 LLMPodium Score
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