In the rapidly evolving landscape of AI, choosing the right OpenAI model can significantly impact your project's performance, cost, and efficiency. As of May 2025, OpenAI offers a diverse range of models with varying capabilities, context windows, and price points.

This comprehensive guide breaks down each available model's strengths, weaknesses, and ideal use cases to help you make informed decisions for your specific needs.

The Current OpenAI Model Lineup (May 2025)

GPT-4.1 Series: The Coding & Instruction Following Powerhouse

Released in April 2025, the GPT-4.1 family represents OpenAI's focus on developer-oriented models with exceptional coding capabilities and instruction following.

GPT-4.1 (Flagship)

• Context Window: 1 million tokens (approximately 750,000 words)

• Knowledge Cutoff: June 2024

• Pricing: $2 per million input tokens, $8 per million output tokens

• Max Output Tokens: 32,768 (2x GPT-4o's capacity)

Key Strengths:

• Coding Excellence: Scores 54.6% on SWE-bench Verified, representing a massive 21.4 percentage point improvement over GPT-4o

• Instruction Following: Achieves 38.3% on Scale's MultiChallenge benchmark, outperforming GPT-4o by 10.5 percentage points

• Long Context Processing: True million-token context window with maintained coherence throughout

• Multimodal Capabilities: Processes images with strong vision capabilities

Best For: Software development, complex coding tasks, processing large codebases, and applications requiring precise instruction following.

GPT-4.1 Mini

• Context Window: 1 million tokens

• Pricing: $0.40 per million input tokens, $1.60 per million output tokens

• Performance: Matches or exceeds GPT-4o in many benchmarks with nearly half the latency and 83% lower cost

Best For: Balanced applications requiring good performance and cost efficiency, particularly for multimodal or image processing tasks.

GPT-4.1 Nano

• Context Window: 1 million tokens

• Pricing: $0.10 per million input tokens, $0.40 per million output tokens

• Performance: OpenAI's fastest and most cost-effective model, still achieving 80.1% on the MMLU benchmark

Best For: High-speed, low-cost applications like classification, autocomplete, and basic text generation.

GPT-4o Series: The Multimodal Champions

The "o" (omni) series represents OpenAI's multimodal models that excel at processing text, images, and other data types simultaneously.

GPT-4o

• Context Window: 128,000 tokens

• Knowledge Cutoff: October 2023

• Pricing: Not explicitly stated in search results, but reportedly more expensive than the GPT-4.1 series

Key Strengths:

• Multimodal Processing: Seamlessly handles text and images

• Balanced Performance: Strong general-purpose model for text, code, and visual understanding

• Language Capability: Superior performance on non-English languages

Best For: Applications requiring strong visual and textual understanding, multilingual tasks, and balanced generalist capabilities.

GPT-4o Mini

• Context Window: 128,000 tokens

• Key Feature: Most cost-efficient small model with vision capabilities

• Best For: Budget-conscious applications requiring some visual processing capabilities

O-Series: The Reasoning Specialists

The "o" numbered models (o1, o3, etc.) are specialized for reasoning and problem-solving tasks.

o4-mini

• Context Window: 200,000 tokens

• Knowledge Cutoff: June 2024

• Key Strengths: Excels in math, coding, and visual tasks

o3-mini

• Context Window: 200,000 tokens

• Knowledge Cutoff: January 2025 (estimated)

• Key Strengths: Enhanced reasoning abilities

o1

• Context Window: 200,000 tokens

• Knowledge Cutoff: October 2023

• Key Strengths: Specialized for complex reasoning tasks

GPT-4.5: Being Phased Out

GPT-4.5, while impressive, is being deprecated in the API by July 14, 2025. OpenAI is replacing it with the more efficient GPT-4.1 series.

• Context Window: 128,000 tokens

• Pricing: $75 per million input tokens, $150 per million output tokens

• Key Strengths: High emotional intelligence, creative content, natural conversational abilities

• Status: Being phased out of the API (will remain available in ChatGPT for now)

Performance Comparison: Benchmark Analysis

The latest benchmarks reveal significant performance differences between models:

Coding Performance

• SWE-bench Verified: GPT-4.1 (54.6%) > GPT-4.5 (28%) > GPT-4o (33.2%)

• Aider Polyglot: GPT-4.1 (52%) < Google Gemini 2.5 (73%)

Reasoning & General Intelligence

• MMLU (Massive Multitask Language Understanding):

  • GPT-4.1: Not explicitly stated in search results, but competitive

  • GPT-4.1 Mini: Matches or exceeds GPT-4o

  • GPT-4.1 Nano: 80.1%

Instruction Following

• MultiChallenge: GPT-4.1 (38.3%) > GPT-4o (27.8%)

• IFEval: GPT-4.1 (87.4%) > GPT-4o (81.0%)

Long Context Understanding

• Video-MME: GPT-4.1 achieves 72.0% on long, no subtitles category, a 6.7 percentage point improvement over GPT-4o

Cost Analysis: Dollars per Million Tokens

For developers concerned about budget, here's a direct cost comparison:

To put this in practical terms, processing a typical 10,000-token document and generating a 2,000-token response would cost:

• GPT-4.1: $0.036 ($0.02 input + $0.016 output)

• GPT-4.1 Mini: $0.0072 ($0.004 input + $0.0032 output)

• GPT-4.1 Nano: $0.0018 ($0.001 input + $0.0008 output)

• GPT-4.5: $1.05 ($0.75 input + $0.30 output)

Model Selection Cheat Sheet

Choose GPT-4.1 when:

• You're building complex software applications

• You need precise instruction following

• You're processing very large documents (up to 1M tokens)

• Cost efficiency matters, but you need top-tier performance

Choose GPT-4.1 Mini when:

• You want a balance of performance and cost efficiency

• You're handling image processing tasks

• You need reasonable speed with good reasoning capabilities

Choose GPT-4.1 Nano when:

• Speed is critical

• You're performing simple tasks like classification, autocomplete

• You're operating at high volume where costs can add up quickly

Choose GPT-4o when:

• You need strong multimodal understanding

• Visual processing with text integration is key

• Non-English language capabilities are important

Choose an O-Series model (o1, o3-mini, etc.) when:

• Complex reasoning is the primary requirement

• Mathematical or scientific problem-solving is needed

• Step-by-step logical thinking is essential

Key Considerations When Choosing a Model

1. Context Window Requirements: If you need to process entire codebases or very lengthy documents, GPT-4.1's 1M token context window is unmatched.

2. Response Length Needs: GPT-4.1 can generate 32,768 tokens in a single response (2x GPT-4o's capacity), making it ideal for long-form content.

3. Budget Constraints: The GPT-4.1 Nano model offers exceptional value for simple tasks at just $0.10/$0.40 per million tokens.

4. Specific Task Type:

   • Coding: GPT-4.1 is the clear leader

   • Multimodal Processing: GPT-4o family excels

   • Complex Reasoning: O-series models are specialized for this

5. Speed Requirements: GPT-4.1 Nano is OpenAI's fastest model, with GPT-4.1 Mini offering a good balance of speed and capability.

OpenAI Model Comparison (May 2025)

OpenAI Models Comparison (May 2025)

Compact comparison of current OpenAI models with verified data.

* Estimated pricing based on available information.
"Live Bench" refers to SWE-bench Verified scores, measuring coding capabilities.

Conclusion: Matching Models to Your Needs

The OpenAI model landscape has evolved significantly in 2025, moving away from monolithic models toward specialized variants optimized for specific tasks. The GPT-4.1 family represents a significant shift toward developer-friendly, coding-oriented models with massive context windows and improved instruction following.

For most applications, GPT-4.1 Mini likely offers the best balance of capability and cost, matching or exceeding GPT-4o's performance at a fraction of the price. For high-volume, simple applications, GPT-4.1 Nano provides unprecedented speed and economy.

As model selection becomes increasingly nuanced, the key is to align your choice with your specific requirements rather than defaulting to the latest or most powerful option. By considering context window needs, task complexity, budget constraints, and performance requirements, you can select the optimal model to drive your AI-powered applications in 2025 and beyond.

Remember: the right model isn't always the most powerful or expensive one—it's the one that best fits your specific use case and budget.