InclusionAI
Ling 3.0 Flash
InclusionAI's cost-focused Flash tier for high-frequency hybrid reasoning and agent loops at low list rates.
Catalog record checked August 13, 2026; individual provider fields may change.
| Specification | Ling 3.0 Flash |
|---|---|
| Provider | InclusionAI |
| Tier | Budget |
| Context window | 262K |
| Max output | Not verified |
| Input / 1M tokens | $0.075 |
| Output / 1M tokens | $0.22 |
| Weights | Open |
| Parameters | 124B total / 5.1B active (MoE) |
| Modalities | text |
| Released | July 24, 2026 |
Verified evidence
Published benchmark results
Each result keeps its source and measurement date visible. A missing benchmark is not treated as a zero.
Artificial Analysis Intelligence Index
2026-08-08 · Artificial Analysis
Best for
- High-volume agent loops
- Cost-sensitive chat and extraction
- Fast hybrid reasoning
Watch out
Common questions
Ling 3.0 Flash
Answered from the verified figures on this page rather than general guidance.
How much does Ling 3.0 Flash cost per million tokens?
Ling 3.0 Flash is listed at $0.075 per million input tokens and $0.22 per million output tokens at standard rates. Output tokens usually dominate real bills, so weigh the output rate more heavily than the input rate.
What is Ling 3.0 Flash's context window?
Ling 3.0 Flash accepts about 262K tokens of context. That only matters if you routinely send very long documents, large codebases, or multi-turn histories that approach that limit.
What is Ling 3.0 Flash best for?
Ling 3.0 Flash is a budget tier from InclusionAI. It suits high-volume agent loops, cost-sensitive chat and extraction, fast hybrid reasoning. Native context is 256Ki tokens (262,144). Confirm serving provider and current list before committing volume.
Can I self-host Ling 3.0 Flash?
Ling 3.0 Flash publishes open weights, but self-hosting depends on the licence, hardware footprint, quantisation quality, and serving stack. A hosted API is often cheaper until you have measured throughput and concurrency on your own hardware.
Compare it
Head-to-head model comparisons
These are the published pairings that put this model against a plausible alternative.