Ling 3.0 Flash
Ling 3.0 Flash is worth evaluating for rag, agents, and long context when its provider route and context window match the workload.
Use it for
- Teams evaluating rag, agents, and long context
- Workloads that can use a 262k context window
- Buyers comparing 1 tracked provider route
Do not use it for
- Vision or document-understanding workloads
InclusionAI is Ant Group's artificial general intelligence research lab, responsible for developing the Ling series of l
Cheapest of 1 route · OpenRouter · cache read $0.0042
About
Ling-3.0-flash is InclusionAI's next-generation hybrid-linear MoE instruct model with 124B total and 5.1B active parameters per token, 262K context, and agentic training across coding, research, and tool-use environments. MIT-licensed weights on Hugging Face (inclusionAI/Ling-3.0-flash); available on OpenRouter as inclusionai/ling-3.0-flash.
Top use-case fit: coding, agents, and build tasks
RAG
Included by capability and metadata signals in the decision map.
Agents
Included by capability and metadata signals in the decision map.
Long context
Included by capability and metadata signals in the decision map.
Provider price ladder
Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.
| Provider | Input / 1M | Output / 1M | Cache | Route |
|---|---|---|---|---|
| OpenRouter | $0.021 | $0.063 | read $0.0042 | Serverless |
Capabilities
Benchmark peer barsfor RAG
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
Rankings & picks(1)
InclusionAI is Ant Group's artificial general intelligence research lab, responsible for developing the Ling series of l
Cheapest of 1 route · OpenRouter · cache read $0.0042