Ling

Ling 3.1 Flash

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

#25released Oct 12, 2026·Proprietary·Not disclosed
text131K ctx
Intelligence
88.5 #25
AAQI 56.0
Output Speed
175 t/s
ITL 5.7 ms/tok
Blended price (3:1)
$0.31 /1M
In $0.15/M · Out $0.8/M
Context
131K
≈ 328 A4 pages · max out 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)

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

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.

Developer Quickstart & API

Set OPENROUTER_API_KEY first — get an API key
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.

Official Resources & Links

API deployment

Compare hosts & latency

Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
View providers matrix →

Scores by Source

Head-to-head Live Matchups

Live Specs & Architecture

Under the hood
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
Release date
Oct 12, 2026
Architecture
Ling 3.1 Flash — high-throughput MoE
Parameters
Not disclosed
License
Proprietary
Context Window
131,000 tokens
Max Output
8,192 tokens
Modalities
text
Cache Pricing
read $0.02 · write $0.19 /1M
Reasoning
—
Input Price
$0.15/M
Output Price
$0.8/M
Output Speed
175 t/s
Time to First Token
—
Data Updated
Oct 22, 2026

Estimated monthly bill

Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...

Cite & embed

GitHub Markdown badge
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}
}

Embed Score Badge

Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.

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

More from Ling

Alternative Models

Closest alternatives to Ling 3.1 Flash based on capabilities, pricing, and benchmark scores.

0 / 4