Ling
Ling 3.1 Flash
01.AI’s flash-tier frontier model — high throughput at minimal per-token cost.
Intelligenza
88.5 #25
AAQI 56.0
Velocità Uscita
175 t/s
ITL 5.7 ms/tok
Prezzo combinato (3:1)
$0.31 /1M
Input $0.15/M · Output $0.8/M
Contesto
131K
≈ 328 pagine A4 · output max 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Ling 3.1 Flash
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
- 175 tok/s — top-tier flash throughput
- $0.31 blended — among the cheapest frontier-adjacent models
- Strong math/coding for a speed-tier model
A cosa fare attenzione
- No dedicated reasoning mode
- Text-only — no vision or audio
- Weakest on long agentic traces
Casi d'uso 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 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": "inclusionAI/ling-3-1-flash",
"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="inclusionAI/ling-3-1-flash",
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: "inclusionAI/ling-3-1-flash",
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
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
Data di Rilascio
12 ott 2026
Architettura
Ling 3.1 Flash — high-throughput MoE
Parametri
Not disclosed
Licenza
Proprietary
Finestra di Contesto
131,000 tokens
Output max
8,192 tokens
Modalità
text
Prezzi cache
lettura $0.02 · scrittura $0.19 /1M
Ragionamento
—
Prezzo Ingresso
$0.15/M
Prezzo Uscita
$0.8/M
Velocità Uscita
175 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
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}
}Incorpora il badge del punteggio
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