Articles Tagged "Apache 2.0"

Qwen3.5-27B Claude Opus Distilled

Qwen3.5-27B Claude Opus Distilled

Community fine-tune that distills Claude Opus 4.6 reasoning into Qwen3.5-27B via LoRA. 28B parameters, Apache 2.0, no published benchmarks.

Qwen3.5-0.8B

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.

Qwen3.5-2B

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

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

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.

Mistral Large 3

Mistral Large 3

Mistral Large 3 is a 675B-parameter MoE model activating 41B per token with native multimodal support, a 256K context window, and Apache 2.0 licensing - Europe's first frontier-class open-weight model.

Mistral Small 3.2

Mistral Small 3.2

Mistral Small 3.2 is a 24B dense model with strong function calling, multimodal vision, and 128K context under Apache 2.0 - optimized for production tool-use pipelines and EU-compliant deployments.

Qwen3.5-122B-A10B

Qwen3.5-122B-A10B

Qwen3.5-122B-A10B is a 122B-parameter MoE model activating 10B parameters per token, narrowing the gap between medium and frontier models with top scores in GPQA Diamond (86.6), MMMU (83.9), and OCRBench (92.1). Apache 2.0 licensed.

Qwen3.5-27B

Qwen3.5-27B

Qwen3.5-27B is a 27B dense model that matches GPT-5-mini on SWE-bench (72.4) and posts the best coding and instruction-following scores in the Qwen 3.5 medium lineup. Apache 2.0 licensed.