small language models

Cactus Compute has released Needle 3, a foundation model compressed to 8-29 MB that runs entirely on-device for tool calling, structured data extraction, and text embedding. The company claims it beats models 10x its size on mobile tool calls while running on hardware as small as microcontrollers.

Inference.net has released Schematron V2 Turbo, a 3-billion-parameter model built specifically for high-volume HTML-to-JSON extraction. The model supports a 128K context window and is priced at $0.03 per 1M input tokens and $0.15 per 1M output tokens.

Inference.net has released Schematron V2 Small, a 3B-parameter model specialized in converting HTML pages into structured JSON output. The model supports a 128K context window and requires extraction schemas to be passed via response_format rather than standard prompts.

Liquid AI has released LFM2.5-VL-3B, a 3.1-billion-parameter vision-language model built for on-device deployment. The model claims leading performance in its size class on grounding, screen understanding, and tool use, while running at 228 tokens/s on an Apple M5 Max.

Nvidia's Nemotron 3.5 Lightning, a 31.6B-parameter open-weight model with only 3.6B active parameters, matches OpenAI's gpt-oss-120b on the Artificial Analysis Intelligence Index while delivering the fastest throughput in its class at nearly 670 tokens per second. The model posts especially large gains on agentic benchmarks, beating both gpt-oss-120b and the larger Nemotron 3 Super.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model trained on 34 trillion tokens with a 128K context window, built for on-device agentic workloads. The company claims it is competitive with models four times its size on tool use and instruction following.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model designed to run capable tool-calling agents locally on laptops and phones. The company claims it matches or beats models up to 4x its size on instruction-following and tool-use benchmarks while running under 2.5GB of memory.

Liquid AI has released LFM2.5-230M, a 230M parameter hybrid model trained on 19 trillion tokens with a 32,768 token context window. The model achieves 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on Raspberry Pi 5, with support for function calling and data extraction tasks.