HomeAnt Group unveils open-source Ling-2.6-flash, new addition to Baoling model family
Ant Group's Baoling large model series received a major update today, with Ling-2.6-flash now officially available to developers worldwide. To accommodate different hardware environments and lower the deployment barrier, this model also launched multiple precision versions including BF16, FP8, and INT4, offering developers more flexible inference options.
As an Instruct model with 104B total parameters and 7.4B activated parameters, Ling-2.6-flash was previously tested under the alias "Elephant Alpha" on the OpenRouter platform. Over a two-week trial, the development team gathered extensive real-world feedback and made targeted optimizations, notably enhancing the fluidity of Chinese-English code-switching and improving compatibility with mainstream programming frameworks.
Technical Highlights: Hybrid Architecture and Superior Efficiency
Ling-2.6-flash 's core strength lies in its unique architecture and high operational efficiency:
Hybrid Linear Architecture: Through low-level computational optimization, the model achieves excellent inference speed. With 4 H20 cards, it reaches up to 340 tokens/s. In Prefill throughput, it delivers 2.2x that of Nemotron-3-Super, significantly reducing response latency.
Remarkable Token Efficiency Ratio: The team meticulously calibrated token efficiency during training. Evaluation data shows that for tasks of equivalent quality, Ling-2.6-flash consumes only about 15M tokens—roughly one-tenth of comparable competitors—greatly lowering commercial costs.
Scenario Deepening: Targeted Agent Capability Enhancements
For agent scenarios—one of the most common use cases for large models—Ling-2.6-flash has been specifically enhanced. Whether handling complex tool calls, multi-step planning, or final task execution, the model performs reliably. In several industry-standard evaluations such as BFCL-V4 and SWE-bench, even when compared with models featuring larger activated parameter counts, Ling-2.6-flash maintains comparable or even state-of-the-art (SOTA) performance.
Developers can now access the model's open-source resources via Hugging Face and ModelScope (Moba Community), opening up further exploration of its potential across various industry applications.
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Ant Group's Baoling large model series received a major update today, with
As an Instruct model with 104B total parameters and 7.4B activated parameters,
Technical Highlights: Hybrid Architecture and Superior Efficiency
Hybrid Linear Architecture: Through low-level computational optimization, the model achieves excellent inference speed. With 4 H20 cards, it reaches up to 340 tokens/s. In Prefill throughput, it delivers 2.2x that of Nemotron-3-Super, significantly reducing response latency.
Remarkable Token Efficiency Ratio: The team meticulously calibrated token efficiency during training. Evaluation data shows that for tasks of equivalent quality,
Scenario Deepening: Targeted Agent Capability Enhancements
For agent scenarios—one of the most common use cases for large models—
Developers can now access the model's open-source resources via Hugging Face and ModelScope (Moba Community), opening up further exploration of its potential across various industry applications.
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