# Ant Ling — Ling-2.6-flash Release: Faster Response, Stronger Execution, Higher Token Efficiency

- Company: Ant Ling (ant-ling.com)
- Announced: 2026-04-23
- Category: new-model
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://developer.ant-ling.com/en/blogs/ling-2.6-flash-release
- Record: https://forck.live/items/22331-ling-2-6-flash-release-faster-response-stronger-execution-higher-token
- Subject: Ling / Ring / Ming
- Models affected: Ling-2.6-flash

Ant Ling released Ling-2.6-flash, an Instruct model with 10.4 billion total parameters and 7.4 billion active parameters. The model uses a hybrid linear attention architecture (MLA + Lightning Linear in a 1:7 ratio) to achieve inference speeds up to 340 tokens/s on 4-H20 cards and prefill throughput 2.2 times that of Nemotron-3-Super. It consumed only 15 million tokens in an Artificial Analysis evaluation, roughly one-tenth the tokens used by Nemotron-3-Super, while achieving performance comparable to or state-of-the-art on agent benchmarks such as BFCL-V4, TAU2-bench, SWE-bench Verified, Claw-Eval, and PinchBench.

## Evidence

Verbatim from https://developer.ant-ling.com/en/blogs/ling-2.6-flash-release:

> In a comprehensive evaluation by Artificial Analysis, Ling-2.6-flash consumed only 15M tokens , roughly one-tenth of the tokens used by models such as Nemotron-3-Super—yet it accomplished the task with a significantly higher "intelligence-to-effectiveness ratio."

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