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Qwen2.5-1M-7|14B logo

Qwen2.5-1M-7|14B

Free

Deploy Your Own Qwen with Context Length up to 1M Tokens

FreeFree tier
Inputs: text, codeOutputs: text
Type
Open Source
Company
Qwen Team
Visit Website GitHub (38 stars)

About Qwen2.5-1M-7|14B

Qwen2.5-1M is a series of open-source large language models developed by the Qwen Team, designed to handle context lengths of up to 1 million tokens. The release includes two model sizes: Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M. These models are fine-tuned for instruction following and support both long-context and short-context tasks. To facilitate efficient deployment, the team also released an inference framework based on vLLM that integrates sparse attention, achieving 3x to 7x speed improvements on 1M-token inputs. The models maintain strong performance on standard short-text benchmarks while significantly outperforming their 128K context counterparts on long-sequence tasks, and the 14B variant consistently beats GPT-4o-mini across multiple long-context datasets. The models are available on Hugging Face and ModelScope, with online demos and integration into Qwen Chat.

Key Features

Support for context lengths up to 1M tokens
Two open-source model sizes: 7B and 14B parameters, instruction-tuned
Inference framework based on vLLM with sparse attention for 3x-7x speedup on long inputs
Progressive multi-stage long-context training approach
Strong performance on both long and short text tasks
Outperforms Qwen2.5-Turbo and GPT-4o-mini on long-context benchmarks
Available on Hugging Face, ModelScope, and through Qwen Chat demo

Pros & Cons

Pros
  • Fully open-source models and inference framework
  • Handles up to 1M tokens without major performance degradation
  • Inference speed boosted 3x-7x via sparse attention
  • Maintains strong performance on short-context tasks
  • Open-source alternative that competes with GPT-4o-mini on long contexts
  • Supported by active community and official demos
Cons
  • The 7B model shows minor errors on 1M-token passkey retrieval tasks
  • Large context processing still requires significant GPU memory and compute
  • Relatively new release (January 2025) with potentially limited third-party tooling
  • No official hosted API; requires self-deployment or use of third-party services

Best For

Long-document question answering and summarizationCode analysis over large codebasesResearch paper comprehension and synthesisMulti-turn conversations with extensive historyProcessing and reasoning over entire books or lengthy reports

FAQ

What context length does Qwen2.5-1M support?
Qwen2.5-1M models support context lengths up to 1 million tokens, making them suitable for processing very long documents.
What model sizes are available?
Two sizes are released: Qwen2.5-7B-Instruct-1M (7 billion parameters) and Qwen2.5-14B-Instruct-1M (14 billion parameters), both instruction-tuned.
How does the inference framework improve speed?
The inference framework is based on vLLM and integrates sparse attention techniques, resulting in 3x to 7x faster processing of 1M-token inputs compared to standard implementations.
Is Qwen2.5-1M open-source?
Yes, both the models and the inference framework are fully open-source, available on Hugging Face, ModelScope, and GitHub.
How does Qwen2.5-1M compare to GPT-4o-mini?
Qwen2.5-14B-Instruct-1M consistently outperforms GPT-4o-mini on multiple long-context datasets, while the 14B and Turbo models achieve similar short-text performance with 8x longer context support.