Alternatives to Qwen3.8-2.4T-A95B

Alibaba's open-weight architecture preview of Qwen4: 125B multimodal MoE with 6B active plus a 51B N-gram table, 262K native context, at $0.16/M input.

Qwen3.8-Omni-Flash is Alibaba's omni-modal agentic model: 1M context, text, image, audio and video input, $0.15/$0.47 per 1M tokens.

Moonshot AI's open-weight 2.8T multimodal agentic model with 1M-token context, the world's first open 3T-class model rivaling closed frontier models.

Zhipu's first natively multimodal GLM (confirmed as the 'Ox Alpha' stealth model): 320B MoE with 18B active, 1M context, MIT open weights at $0.15/M input.

Xiaomi's MiMo-V2.6 open-weights series pairs a 1.02T-parameter omni MoE with a 309B Flash tier, both MIT licensed with 1M-token context.

Alibaba's open-weight 27B dense companion to Qwen3.8-Max: Apache 2.0 license, multimodal image-text input, built for local deployment and small-batch inference.

Alibaba's 2.4T-parameter open-weight flagship: 95B active MoE, text/image/video input, 1M context, at $2/$6 per million tokens.

Agnes-3.0-Flash Preview is an Apache-2.0 33B multimodal open-weight checkpoint with 262k context, not the proprietary API model.

Google DeepMind's open-weight 25.2B MoE model with 3.8B active parameters, 256K context, multimodal input, and a commercially permissive Apache 2.0 license.

OpenBMB MiniCPM5-2B is a dense 2B on-device LLM with 131k context, Apache-2.0 weights, and cookbooks for vLLM, SGLang, and llama.cpp.

BAAI's AREX-2 is a 27B open-weight Apache-2.0 agent model that improves solutions over propose, measure, reflect, and revise rounds, with 262K context.

Qwen2-VL-72B-Instruct is Alibaba's 72B vision-language model with dynamic image resolution and 20+ minute video understanding; Qwen2.5-VL is the newer version.

Qwen3-VL-Reranker is Alibaba Qwen's Apache 2.0 multimodal reranker in 2B and 8B sizes, with 32K context, 30+ languages and text, image and video input.

Swift-Qwen3.8-27B is UkisAI's reasoning-efficient Qwen3.8-27B finetune that cuts thinking tokens about 58% with under 1% score loss.