vision-language model

Perceptron has released Mk1.5, a perception model built for physical agents that accepts text, image, video, and audio input and returns text alongside structured spatial annotations. It succeeds Mk1 and is priced at $0.15 per 1M input tokens and $1.50 per 1M output tokens.

Liquid AI has released LFM2.5-VL-3B-DSpark, a 280M-parameter speculative decoding drafter for its LFM2.5-VL-3B vision-language model. The company claims decode speedups up to 3.13x on Apple silicon and 2.66x on H100 GPUs, with day-one support for llama.cpp, MLX-VLM, and SGLang.

InclusionAI has released Ling 3.0 Flash VL, a vision-language extension of its 124B total-parameter, 5.5B active Mixture-of-Experts model. The model adds native image and video understanding, supports a 131K token context window, and is priced at $0.06 per 1M input tokens and $0.18 per 1M output tokens via OpenRouter.

Alibaba's Qwen team has released Qwen-Drive-1.0-4B, a 4B-parameter vision-language model built on Qwen3.5 that unifies 3D perception, driving question answering, and motion planning in one framework. The model reports strong open-loop, pseudo-closed-loop, and closed-loop driving benchmark results while claiming minimal loss of general vision-language ability.

Alibaba has released Qwen-Drive 1.0, a driving model built on Qwen3.5-4B that handles spatial perception, route planning, and cockpit dialogue in a single system. Reinforcement learning cut the rate of off-road driving errors in simulation from 24 percent to 12 percent, though the model's stated reasoning doesn't always match its actual maneuvers.

Qwen has released Qwen3.8 27B, a 27-billion-parameter dense vision-language model with a 262K token context window, available now via OpenRouter at $0.45 per million input tokens and $3.20 per million output tokens.

Alibaba's Qwen team has released FP8-quantized weights for Qwen3.8-27B, a 27-billion-parameter dense vision-language model with native 262,144-token context extensible to 1 million tokens. The model claims gains over its Qwen3.6 and Qwen3.7 predecessors on coding, agentic, and multimodal benchmarks.

Alibaba's Qwen team has released Qwen3.8-27B, a 27-billion-parameter dense vision-language model with 262,144-token native context extensible to 1 million tokens. The model shows gains over Qwen3.6-27B and Qwen3.7-Plus across coding, agentic, and multimodal benchmarks, according to Alibaba.

Liquid AI has released LFM2.5-VL-3B, a multimodal upgrade to its LFM2-VL-3B model built for on-device grounding, object detection, and document OCR. The model runs at 228 tokens/sec on an Apple M5 Max and 116 tokens/sec on an AMD Ryzen AI Max+ 395, using under 3.3 GB of memory.

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.

Microsoft has released Mage-VL, a codec-native multimodal foundation model built on a from-scratch 4B-parameter visual encoder paired with Qwen3-4B-Instruct-2507. The model claims up to 3.5x inference speedup over uniform frame sampling and outperforms Qwen3-VL-4B on video and temporal-grounding benchmarks, according to Microsoft.

Alibaba has released Qwen3.7 Flash, a vision-language reasoning model with a 1 million token context window aimed at multimodal agents, visual coding, and computer-use tasks. The model is priced at $0.03 per 1M input tokens and $0.13 per 1M output tokens and is available through OpenRouter.

NASA's Jet Propulsion Laboratory ran a 4-bit quantized version of Google's Gemma 3 4B on board a Loft Orbital YAM-9 satellite, marking the first in-orbit demonstration of a vision-language model analyzing imagery from a satellite's own sensor. The unmodified open-weights model hit 88 percent accuracy on a 7,960-image benchmark and ran live capture tests over Toulouse, France and coastal Argentina.