Ling

Ling 3.1 Flash

01.AI’s flash-tier frontier model — high throughput at minimal per-token cost.

#25dirilis 12 Okt 2026·Proprietary·Not disclosed
text131K ctx
Penyedia APIJalankan Model →Peringkat
Inteligensi
88.5 #25
AAQI 56.0
Kecepatan output
175 t/s
ITL 5.7 ms/tok
Harga gabungan (3:1)
$0.31 /1M
Input $0.15/M · Output $0.8/M
Konteks
131K
≈ 328 halaman A4 · output maks 8,192

Quality Domain Radar

6-Axis Frontier Evaluation vs Category Average & Flagship

Ling 3.1 Flash
Claude Mythos Preview (#1)
Average (68)
Coding (50)Mathematics (78)Hard Science (GPQA) (85)Agentic & Tool Use (58)Knowledge (MMLU) (42)Vision / Multimodal (40)

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

Kelebihan & Trade-off

Di mana ia menang

  • 175 tok/s — top-tier flash throughput
  • $0.31 blended — among the cheapest frontier-adjacent models
  • Strong math/coding for a speed-tier model

Hal yang perlu diperhatikan

  • No dedicated reasoning mode
  • Text-only — no vision or audio
  • Weakest on long agentic traces

Kasus Penggunaan Killer

High-volume APIs
Cheap, fast completions for consumer-scale products.
Router fast-path
First-response tier in cascading model routers.
Batch summarization
131K context plus low cost for bulk pipelines.

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": "inclusionAI/ling-3-1-flash",
    "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
Ling 3.1 Flash — high-throughput MoE
  • Sparse MoE tuned for serving density
  • 175 tok/s sustained output
  • 131K context with cheap cache reads
  • Non-reasoning fast path — no thinking tokens burned
Tanggal rilis
12 Okt 2026
Arsitektur
Ling 3.1 Flash — high-throughput MoE
Parameter
Not disclosed
Lisensi
Proprietary
Jendela konteks
131,000 tokens
Output maks
8,192 tokens
Modalitas
text
Harga cache
baca $0.02 · tulis $0.19 /1M
Penalaran
—
Harga input
$0.15/M
Harga output
$0.8/M
Kecepatan output
175 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
Loading badge markdown...
BibTeX
@misc{llmpodium2026ling31flash,
  title = {Benchmark Analysis and Intelligence Evaluation of Ling 3.1 Flash},
  author = {LLMPodium Research},
  year = {2026},
  url = {https://llmpodium.com/models/ling-3-1-flash}
}

Sematkan Lencana Skor

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

Pratinjau
Ling 3.1 Flash LLMPodium Score
Markdown
Loading badge snippet...
HTML
Loading badge snippet...

Lainnya dari Ling

Model Alternatif

Alternatif terdekat untuk Ling 3.1 Flash berdasarkan kemampuan, harga, dan skor benchmark.

0 / 4