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-Coder-32B-Instruct is a 32-billion-parameter code-specialized model from Alibaba, released in November 2024 and trained on a large corpus spanning 92 programming languages including C, Python, Java, Rust, and domain-specific languages. The model was designed to provide competitive code generation, repair, and reasoning capabilities as an open-weight alternative for developers building code assistant tools and automated review pipelines. Its 128K context window enables whole-file and multi-file code comprehension, making it particularly suited for complex repository-level tasks.
5 months newer
Qwen2.5-Coder 32B Instruct
Alibaba / Qwen
2024-11-12

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-Coder 32B Instruct
GPT-4.1 nano
Qwen2.5-Coder 32B Instruct
GPT-4.1 nano
Qwen2.5-Coder 32B Instruct