Comprehensive side-by-side LLM comparison
GPT-4.1 nano supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
OpenAI
GPT-4.1 nano is OpenAI's smallest member of the GPT-4.1 family, released in April 2025 alongside GPT-4.1 and GPT-4.1 mini as the latency-optimized, cost-minimized option for high-throughput applications. Positioned below GPT-4.1 mini in both size and cost, it was designed for use cases where speed and affordability dominate over raw capability — including tool calling, intent classification, short-form instruction following, and retrieval-augmented lookup tasks. Unlike its larger siblings, it supports fine-tuning, making it a practical candidate for task-specific customization at scale without incurring the cost of fine-tuning larger models.
Alibaba / Qwen
Qwen2.5-32B-Instruct is a 32-billion-parameter open-weight model from Alibaba's Qwen team, released in September 2024 as part of the Qwen2.5 series trained on 18 trillion tokens. The model is positioned as a high-capability option for developers with access to multi-GPU setups or high-VRAM hardware, offering strong performance on coding, structured reasoning, and multilingual tasks while remaining fully open under Apache 2.0. Its 128K context window and support for structured output generation made it a popular choice for document processing and agentic workflows in the open-source community.
6 months newer
Qwen2.5 32B Instruct
Alibaba / Qwen
2024-09-19

GPT-4.1 nano
OpenAI
2025-04-14
Context window and performance specifications
GPT-4.1 nano
2024-06
Available providers and their performance metrics
GPT-4.1 nano
OpenAI
Qwen2.5 32B Instruct
GPT-4.1 nano
Qwen2.5 32B Instruct
GPT-4.1 nano
Qwen2.5 32B Instruct