Ling 3.1 Flash
01.AI’s flash-tier frontier model — high throughput at minimal per-token cost.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Radar-Achsen sind aus Kategorie-Scores approximiert, wo direkte Benchmark-Messungen fehlen — siehe Methodik.
Stärken & Trade-offs
Wo das Modell gewinnt
- 175 tok/s — top-tier flash throughput
- $0.31 blended — among the cheapest frontier-adjacent models
- Strong math/coding for a speed-tier model
Worauf achten
- No dedicated reasoning mode
- Text-only — no vision or audio
- Weakest on long agentic traces
Killer-Anwendungsfälle
Developer-Quickstart & API
OPENROUTER_API_KEY — API-Schlüssel holencurl 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.
Offizielle Ressourcen & Links
Hosts & Latenz vergleichen
Durchsatz, TTFT und Cache-Preise bei Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock und Azure.
Ergebnisse nach Quelle
Kategorie-Punkte
Benchmark-Ergebnisse
Live-Spezifikationen & Architektur
- 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
Geschätzte Monatsrechnung
Zitieren & Einbetten
Loading badge markdown...@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}
}Score-Badge einbetten
Fügen Sie Ihrem GitHub- oder Hugging-Face-README ein Live-Podium-Score-Badge hinzu — es verlinkt auf das Live-Ranking.
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