Qwen2.5-Turbo Extends Context Length to 1M Tokens
Alibaba's Qwen team has released Qwen2.5-Turbo, extending the model's context window from 128K to 1 million tokens (approximately 1 million English words). The update includes optimizations for both model capabilities and inference performance at extreme context lengths. The model is available via API and through HuggingFace and ModelScope demos.
Related guides (3)
Related events (8)
Qwen2.5-1M: Open-Source Models with 1M Token Context Window Released
Alibaba's Qwen team has released two open-source models, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, extending context length to 1 million tokens. This follows the earlier upgrade of the proprietary Qwen2.5-Turbo to 1M context two months prior. The release includes inference framework support for deployment, marking the first time Qwen's open-weight models have reached this context length.
Qwen2 Model Family Released: Five Sizes, 128K Context, Multilingual
Alibaba's Qwen team has released Qwen2, an evolution from Qwen1.5, comprising five pretrained and instruction-tuned models ranging from 0.5B to 72B parameters, including a 57B mixture-of-experts variant (57B-A14B). The release highlights training on 27 additional languages beyond English and Chinese, significantly improved coding and mathematics performance, and extended context support up to 128K tokens for the 7B and 72B instruct variants. Benchmark results are claimed to be state-of-the-art across a large number of evaluations.
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
Alibaba's Qwen team describes an agent built on Qwen2 (8k native context) that processes documents up to 1M tokens by decomposing retrieval and reasoning tasks, reportedly outperforming both RAG pipelines and native long-context models. The agent framework was also used to generate synthetic training data for fine-tuning new long-context Qwen models, creating a self-improvement loop. This positions agent-based context extension as a practical alternative to architectural long-context training.
Qwen-MT Turbo: Alibaba Releases Specialized Translation Model Supporting 92 Languages
Alibaba's Qwen team has released qwen-mt-turbo, a specialized machine translation model built on Qwen3 and trained on trillions of multilingual and translation tokens. The model supports 92 languages and dialects covering over 95% of the global population. It incorporates reinforcement learning techniques to improve translation accuracy and linguistic fluency, and is available via the Qwen API.
Qwen1.5-32B: Alibaba's 30B-Parameter Capstone for the Qwen1.5 Series
Alibaba's Qwen team released Qwen1.5-32B, a ~30 billion parameter open-weights language model positioned as the capstone of the Qwen1.5 series. The model targets the emerging consensus around 30B parameters as an optimal balance between performance, memory footprint, and inference efficiency. It is released alongside code on GitHub, weights on HuggingFace and ModelScope, and an interactive demo.
Qwen2.5-LLM: Alibaba releases open-weight language models from 0.5B to 72B
Alibaba's Qwen team releases the Qwen2.5 series of decoder-only dense language models, open-sourcing seven variants spanning 0.5B to 72B parameters. The release targets production use cases in the 10-30B range and mobile deployments at 3B scale. This represents a significant expansion of the open-weights frontier from a Tier 1 Chinese AI lab.
Qwen releases Qwen3.5-2B multimodal model on Hugging Face
Alibaba's Qwen team released Qwen3.5-2B, a 2-billion-parameter image-text-to-text model, on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With nearly 2 million downloads, it has seen substantial community uptake.
Qwen releases Qwen3.5-0.8B multimodal model on Hugging Face
Alibaba's Qwen team released Qwen3.5-0.8B, a small-scale image-text-to-text model, on Hugging Face. The model supports conversational use and is compatible with Azure deployment endpoints. With over 2.7 million downloads and 562 likes, it has seen substantial community uptake for a sub-1B parameter multimodal model.