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.
NVIDIA
Llama-3.1-Nemotron-Ultra-253B-v1 is a 253-billion-parameter model from NVIDIA, derived from Meta's Llama 3.1 405B using neural architecture search (NAS) compression combined with NVIDIA's Nemotron post-training pipeline, which recovers and exceeds the base model's capability after structural compression. Released in April 2025, it supports toggling between a standard instruction mode and an extended reasoning mode via system prompt, allowing the same model to handle both rapid responses and deliberate chain-of-thought tasks. It is the flagship of the Nemotron family, available open-weight on HuggingFace and through NVIDIA NIM for enterprise inference.
7 days newer

Llama-3.1 Nemotron Ultra 253B
NVIDIA
2025-04-07

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
Llama-3.1 Nemotron Ultra 253B
GPT-4.1 nano
Llama-3.1 Nemotron Ultra 253B
GPT-4.1 nano
Llama-3.1 Nemotron Ultra 253B