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 axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- 175 tok/s — top-tier flash throughput
- $0.31 blended — among the cheapest frontier-adjacent models
- Strong math/coding for a speed-tier model
Trade-offs to watch
- No dedicated reasoning mode
- Text-only — no vision or audio
- Weakest on long agentic traces
Killer Use Cases
Developer Quickstart & API
OPENROUTER_API_KEY first — get an API keycurl 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.
Official Resources & Links
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
Scores by Source
Category Scores
Benchmark Results
Head-to-head Live Matchups
Live Specs & Architecture
- 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
Estimated monthly bill
Cite & embed
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}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
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