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Liquid AI released d1 with image support, a decision model designed for yes/no and classification tasks. It matches or beats GPT-6.1 Sol on most benchmarks while costing 19-200x less and running 200-300ms per decision.
- d1 processes images and text to output probabilities for binary, choice, or scoring questions in a single forward pass without token generation
- Benchmarked against GPT-6.1 Sol and Claude Opus 5.5 on six real applications (support ticket filtering, circuit board inspection, etc.), matching or beating GPT-6.1 Sol on four of them
- Costs 19-200x less than comparable frontier models with significantly faster inference speed
Liquid AI released Pipette, an open-source benchmarking suite for measuring AI model performance on phones, laptops, and embedded devices, plus DSpark draft models that speed up inference by 2-3x through speculative decoding. Both tools come with public datasets and are designed to let developers test the exact hardware-model-runtime combinations they plan to ship.
- Pipette provides a standardized way to benchmark 35+ model classes across different quantization levels, runtimes, and devices (phones, laptops, PCs, embedded hardware), with 10k+ verified results already in the warehouse and an interactive dashboard to compare configurations.
- DSpark draft models achieve 2.27x to 3.18x throughput gains on everything from M4 Max MacBooks to H100 GPUs by using lightweight draft models to propose token blocks that a target model verifies in one pass, without changing output quality.
- Both projects are open-source with native iOS and Android apps, public datasets, and submission workflows that let developers contribute benchmarks for their own hardware and model combinations.
Liquid AI has launched the LFM2.5-350M, an enhanced version of its 350M model, featuring 28 trillion tokens of pre-training and improved performance in data extraction and tool use. The model runs efficiently on various hardware, making it suitable for large-scale data pipelines and edge deployments.
- Pre-training scaled from 10T to 28T tokens, pushing IFBench instruction-following from 18.20 to 40.69 and CaseReportBench data extraction from 11.67 to 32.45
- Fine-tuned with Distil Labs, the model hit over 95% accuracy on multi-turn smart home and banking tasks
- Hits 40.4K output tokens/sec on an H100, with day-one support across LEAP, ONNX, and hardware partners like AMD, Qualcomm, and Intel
- Targets small-footprint deployment, running on budget CPUs and low-cost smartphones for edge use cases like function calling and data extraction