Comprehensive side-by-side LLM comparison
GPT-4.1 nano offers 876.2K more tokens in context window than Minimax M 2.5. GPT-4.1 nano is $1.00 cheaper per million tokens. 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.
MiniMax
MiniMax M2.5 is a large language model from MiniMax extensively trained with reinforcement learning across hundreds of thousands of complex real-world environments. It targets agentic tool use, coding automation, and office productivity tasks, with strong results on software engineering and web browsing benchmarks. M2.5 represents the next generation of MiniMax's M-series models optimized for production agentic workloads.
10 months newer

GPT-4.1 nano
OpenAI
2025-04-14
Minimax M 2.5
MiniMax
2026-02-13
Cost per million tokens (USD)
GPT-4.1 nano
Minimax M 2.5
Context window and performance specifications
GPT-4.1 nano
2024-06
Available providers and their performance metrics
GPT-4.1 nano
OpenAI
Minimax M 2.5
GPT-4.1 nano
Minimax M 2.5
GPT-4.1 nano
Minimax M 2.5
MiniMax