Qwen2.5 7B Instruct 1M

Name: Qwen2.5 7B Instruct 1M
Author: Qwen (Alibaba)

by Qwen (Alibaba)Qwen2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released January 20251010K context≈ 757,500 words7.6B params

A compact model that punches above its weight on instruction-following tasks, with the headline feature being its 1 million token context window — enough to hold entire codebases or lengthy documents in a single conversation. At 7.6B parameters it makes trade-offs in raw reasoning depth compared to larger siblings, but handles structured tasks, summarization, and multilingual text with solid consistency.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Exceptional

Instruction Following

Qwen2.5 7B Instruct 1M

by Qwen (Alibaba)Qwen2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released January 20251010K context≈ 757,500 words7.6B params

A compact model that punches above its weight on instruction-following tasks, with the headline feature being its 1 million token context window — enough to hold entire codebases or lengthy documents in a single conversation. At 7.6B parameters it makes trade-offs in raw reasoning depth compared to larger siblings, but handles structured tasks, summarization, and multilingual text with solid consistency.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Exceptional

Instruction Following

Glossary

Compact ModelA smaller language model designed to use fewer computational resources while still performing useful tasks.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.MultilingualA model trained to understand and generate text in multiple languages, not just English.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Qwen2.5 7B Instruct 1M

Capabilities

Qwen2.5 7B Instruct 1M

Capabilities

Benchmark Scores

Use Case Fit

Glossary