Qwen3.5-0.8B
Qwen3.5-0.8B is the smallest natively multimodal model in the Qwen 3.5 family - 0.8B parameters handling text, images, and video with 262K context. MathVista 62.2, OCRBench 74.5. Apache 2.0.
Articles Tagged "Multimodal"
Qwen3.5-2B
Qwen3.5-2B is a 2B dense multimodal model with 262K context, thinking mode, and native vision including video understanding. OCRBench 84.5, VideoMME 75.6. Apache 2.0 licensed.
Qwen3.5-4B
Qwen3.5-4B is a 4B dense multimodal model that matches Qwen3-30B on MMLU-Pro and beats GPT-5-Nano on vision benchmarks. Runs on 8GB VRAM, Apache 2.0 licensed, 262K-1M context.
Qwen3.5-9B
Qwen3.5-9B is a 9B dense model that outperforms Qwen3-30B on most benchmarks and beats GPT-5-Nano on vision tasks. Natively multimodal with 262K-1M context, Apache 2.0 licensed.
DeepSeek V4 vs Kimi K2.5 - China's Trillion-Parameter MoE Duel
Two Chinese open-weight trillion-parameter MoE models with ~32B active parameters each - DeepSeek V4 bets on cost and context, Kimi K2.5 bets on Agent Swarm and verified benchmarks.
DeepSeek V3.2 vs V4 - What Changes With a Trillion Parameters
A pre-release comparison of DeepSeek V3.2 and V4 - examining the generational leap from 671B text-only to a trillion-parameter natively multimodal model with 1M context.
GPT-5.2
GPT-5.2 is OpenAI's most capable model with three modes, 400K context, and record-setting professional benchmarks - but speed and pricing raise questions.
DeepSeek V4 Drops Next Week - 1 Trillion Parameters on Chinese Chips
DeepSeek will release V4, a natively multimodal trillion-parameter model with a 1M token context window, in the first week of March - optimized for Huawei Ascend chips, not Nvidia.
Nano Banana 2 (Gemini 3.1 Flash Image)
Google DeepMind's natively multimodal image generation and editing model built on Gemini 3.1 Flash - Pro-level quality at Flash speed, free for all Gemini users.
DeepSeek V4 Lite Leaks Under NDA - 1M Context, Natively Multimodal, Codenamed Sealion-Lite
DeepSeek's V4 Lite model has leaked through inference provider testing under strict NDAs, revealing a 1M token context window, native multimodal capabilities, and the internal codename sealion-lite.
Kimi K2.5
Moonshot AI's Kimi K2.5 is a 1T-parameter MoE model activating 32B per token with native multimodal vision via MoonViT-3D, Agent Swarm coordination of up to 100 sub-agents via PARL, and top-tier math and coding benchmarks under a modified MIT license.
Kimi K2.5 vs Gemma 3 27B: Trillion-Parameter Frontier vs Google's Accessible Multimodal Model
Comparing Moonshot AI's 1T-parameter Kimi K2.5 with Google DeepMind's Gemma 3 27B - two multimodal open-weight models separated by 37x in parameter count but sharing a vision-first design philosophy.
