Ling-2.6-flash — benchmark results
InclusionAI's open 104B MoE (7.4B active) with hybrid linear+MLA attention, optimized for inference efficiency and agent workloads (April 2026). Provider: InclusionAI. Released 2026-04-22. Access: Open.
Unified ELO 1527 ± 35, rank #696 of 1776 rated models, from 50 benchmark results.
Strongest benchmark results
| Benchmark | Score | Metric | Percentile |
|---|---|---|---|
| AA TAU-2 Bench | 85.96 | Accuracy (%) | 78.2 |
| AA IFBench | 57.41 | Accuracy (%) | 68.8 |
| AA Terminal-Bench Hard | 21.21 | Accuracy (%) | 60.9 |
| AA Omniscience - Software Engineering (SWE) - R | 14 | Accuracy (%) | 57 |
| AA Omniscience - Software Engineering (SWE) - Java | 18 | Accuracy (%) | 54.5 |
| AA Omniscience - Software Engineering (SWE) - Python | 22 | Accuracy (%) | 52.7 |
| AA Omniscience - Software Engineering (SWE) - Rust | 50 | Accuracy (%) | 50.1 |
| AI Chess Leaderboard (Continuation) | 571 | Elo | 50 |
| AA Omniscience - Software Engineering (SWE) - C | 36 | Accuracy (%) | 49.3 |
| AI Chess Leaderboard (Reasoning) | 650 | Elo | 48 |
| AA Omniscience - Software Engineering (SWE) | 23.2 | Accuracy (%) | 47.9 |
| Wolfram LLM Benchmarking Project | 38.6 | Correct Functionality (%) | 47 |
Interactive version: theaggregate.ai/model?slug=ling-2-6-flash · How the rankings work · Data refreshed daily, snapshot 2026-07-22.