GPT-4.1: Everything You Should Know About OpenAI's Latest AI Model

OpenAI introduced its GPT-4.1 models, a major step forward in AI technology, emphasizing coding and technical precision.

Available solely via OpenAI's API, the lineup—GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano—offers developers powerful tools with a massive context window, multimodal inputs, and competitive pricing.

Availability

OpenAI rolled out the GPT-4.1 models on April 14, 2025, granting instant API access.

This launch aligns with plans to retire older models like GPT-4, signaling a shift toward specialized offerings.

Unlike prior releases, these models skip ChatGPT integration, focusing squarely on developer needs.

 

Pricing

The GPT-4.1 models cater to diverse budgets with tiered pricing:

Model	Input ($/1M tokens)	Cached Input ($/1M tokens)	Output ($/1M tokens)
GPT-4.1	2.00	0.50	8.00
GPT-4.1 mini	0.40	0.10	1.60
GPT-4.1 nano	0.100	0.025	0.400

Batch API usage slashes costs by 50%, while fine-tuning and web tools add flexibility.

The mini and nano variants ensure affordability for smaller projects, with the full model tackling heavy-duty tasks.

Key Features

Multimodal Inputs and Huge Context

These models handle text and image inputs, outputting text, with a groundbreaking 1,047,576-token context window—roughly 750,000 words.

This extended capacity is particularly well-suited for large-scale coding tasks and comprehensive document processing.

Also, the GPT-4.1 series is more up-to-date, having been trained on data up to May 31, 2024, providing more current and relevant insights.

Coding-Focused Design

Tailored for developers, GPT-4.1 excels in coding tasks, offering chat completions, function calling, and structured outputs.

Available in regions like East US2 and Sweden Central, per Azure docs, they meet global technical demands.

Model Comparisons

GPT-4.1 vs. GPT-4o

Compared to GPT-4o (May 2024), GPT-4.1 boasts a far larger context window (1,047,576 vs. 128,000 tokens), and has a better performance across multiple benchmarks:

Model	SWE-bench Verified (Coding)	Scale's MultiChallenge (Instruction following)	Video-MME (Long context)
GPT-4.1	54.6%	38.3%	72.0%
GPT-4o	33.2%	27.8%	65.3%

In addition to improvements in coding, instruction following, and long-context understanding, GPT-4.1 demonstrates enhanced reasoning capabilities, more consistent output formatting, and improved handling of complex, multi-step tasks.

These advancements make GPT-4.1 a more powerful and reliable model for enterprise applications, research, and real-world problem-solving scenarios.

Its expanded context window enables deeper comprehension of large documents and extended conversations, while its refined instruction-following abilities support more accurate and context-aware responses.

(Source: OpenAI)

Comparing GPT-4.1 with Competitors

GPT-4.1 challenges models like Google's Gemini 2.5 Pro, Anthropic's Claude 3.7 Sonnet, and DeepSeek's V3, all with million-token windows.

Despite similar context window sizes, GPT-4.1 and its variants (mini and nano) offer a compelling balance of performance, cost-efficiency, and speed.

While Gemini 2.5 Pro leads in raw intelligence score, its significantly higher latency (35.88s) makes it less suitable for real-time applications. In contrast, GPT-4.1 maintains competitive intelligence with much lower latency (0.40s) and faster output speeds.

The GPT-4.1 mini and nano versions further optimize for cost and speed, offering viable alternatives for budget-sensitive or high-throughput use cases. For example, GPT-4.1 nano delivers an impressive 279.9 tokens per second at just $0.17 per million tokens—ideal for lightweight tasks at scale.

Meanwhile, Meta's Llama 4 models provide ultra-low pricing, especially the Llama 4 Scout with a 10M token context window, although they trail behind in intelligence score and performance consistency.

Here's a detailed comparison:

Model	Provider	Context Window	Intelligence Score	Price ($/M tokens)	Output Speed (tokens/s)	Latency (s)
GPT-4.1	OpenAI	1M	52	$3.50	133.4	0.40
GPT-4.1 mini	OpenAI	1M	53	$0.70	238.2	0.42
GPT-4.1 nano	OpenAI	1M	41	$0.17	279.9	0.89
GPT-4o	OpenAI	128K	50	7.50	212.2	0.55
Llama 4 Maverick	Meta	1M	49	0.40	127.2	0.36
Llama 4 Scout	Meta	10M	36	0.26	104.1	0.33
Gemini 2.5 Pro	Google	1M	68	3.44	157.9	35.88
Gemini 2.0 Flash	Google	1M	48	0.17	248.3	0.30
Claude 3.7 Sonnet	Anthropic	200k	48	$6.00	77.1	0.90
DeepSeek V3	DeepSeek	128k	53	$0.48	24.5	3.37

Editor's Comments

OpenAI's GPT-4.1 feels like a smart, calculated move—not a flashy stunt.

Instead of chasing every possible feature, they've doubled down on what matters most to developers: great code.

Let's be real—devs don't need an AI that sings and dances. They want a reliable coding partner.

By dropping audio input and focusing on text and image, GPT-4.1 shows it knows exactly who it's for—and what they actually need.

Speed and Price: A Shot Across the Bow

At $0.17 per million tokens and 279.9 tokens per second, GPT-4.1 nano isn't just fast and cheap—it's making a statement.

It's like OpenAI is saying to Google and Meta: "Catch us if you can." GPT-4.1 is built to get things done—efficient, practical, no nonsense.

Welcome to the Era of Specialization

What really stands out is the shift from "do-it-all" AI to focused, expert tools.

GPT-4.1 goes all-in on programming, and that could seriously change the game for software development.

If it ends up outperforming tools like GitHub Copilot, we're not just talking about better tech—we're talking about a whole new way of working.

A Bold Bet—and What's Next

Is focusing so much on code a risk? Maybe. Not everyone's a developer. But OpenAI seems confident: master one domain first, then expand. And honestly, this feels like just the beginning.

If GPT-4.1 is the appetizer, GPT-5 might be the main course. One thing's clear—OpenAI isn't just building smarter tools; they're moving fast and thinking big.