NEW GPT-4.1 Models - What You NEED To Know

Key Concepts

GPT-4.1, GPT-4.0, GPT-4.5, GPT-3.5 Mini, Gemini 2.5 Pro, Claude 3.5 Sonnet, context window, tokens, MRC score, instruction following, coding benchmarks (SWE-bench, Polyglot), price comparison, latency, throughput, API access, AI agents, crew, deprecation.

1. Updates on GPT-4.1 Models

1.1. Increased Context Window

• All new GPT-4.1 models support up to 1 million tokens of context, a significant increase from the previous 128,000 token limit.
• The models perform perfectly on "needle in the haystack" tests, meaning they can accurately retrieve information from anywhere within the large context window, not just the beginning or end.

1.2. Improved Conversational Understanding (MRC Score)

• The MRC (Mention Resolution Comprehension) score, which measures the model's ability to track and understand word references over long conversations, has doubled in GPT-4.1 compared to GPT-4.0.
• Example: The model can accurately identify which email is being referred to even after many turns in a conversation.

1.3. Enhanced Instruction Following

• GPT-4.1 is 60-70% better at following instructions compared to GPT-4.0.
• This includes both positive instructions (e.g., always return results in markdown) and negative instructions (e.g., never include emojis in emails).
• Important for ensuring predictable and reliable behavior in deployed LLMs.

1.4. Coding Performance

• GPT-4.1 excels in the SWE-bench benchmark, which involves solving coding problems within a given codebase. It almost doubled the performance of GPT-4.0.
• In the Polyglot benchmark (editing code within a file), GPT-4.1 outperforms GPT-4.0 but is not as strong as GPT-3.5 Mini.

1.5. Pricing

• GPT-4.1 is approximately 20% cheaper than GPT-4.0.
  • GPT-4.1 input: $2 per million tokens
  • GPT-4.0 input: $2.50 per million tokens
  • GPT-4.1 output: $8 per million tokens
  • GPT-4.0 output: $10 per million tokens
• GPT-3.5 Mini is significantly cheaper than GPT-4.1, costing around $1 per million tokens for input.
• GPT-4.1 Mini is more expensive than GPT-4.0 Mini.
  • GPT-4.1 Mini: $0.40 per million tokens input
  • GPT-4.0 Mini: $0.15 per million tokens input
  • GPT-4.1 Nano: $0.10 per million tokens input
• GPT-4.1 models have a cutoff date of May 2024, while GPT-4.0 and GPT-3.5 Mini have a cutoff date in 2023.
• GPT4.1 models are still limited to a 32K token output window.

1.6. Speed (Latency and Throughput)

• GPT-4.1 Nano is the fastest model in terms of latency, making it suitable for voice agents and applications requiring quick responses.
• Latency: Average time for the provider to send the first token.
• Throughput: Number of tokens per second the provider sends back.
• There is a correlation between model intelligence and throughput (smarter models tend to be slower).
• GPT-4.1 models are generally faster than GPT-4.0 models.

1.7. Developer Access and Deprecation of GPT-4.5

• The new GPT-4.1 models are only available through the API.
• GPT-4.1 will replace GPT-4.5 Preview because it is more cost-effective for OpenAI to run.

2. Real-World Applications and Use Cases

2.1. Coding

• GPT-4.1 is not recommended for coding tasks compared to Gemini 2.5 Pro and Claude 3.5 Sonnet.
• Gemini 2.5 Pro excels in the Polyglot benchmark but has high latency.
• Claude 3.5 Sonnet is a good all-around model for coding, offering a balance of speed and intelligence.

2.2. Chatting

• GPT-4.1 is not the best choice for chatting applications requiring long conversations and complex instructions.
• Gemini 2.5 Pro is the top-performing model for chatting, despite its slower response time.

2.3. AI Applications (AI Agents)

• GPT-4.1 is highly recommended for use in AI agents, particularly those requiring strong instruction following.
• Example: GPT-4.1 significantly improved the performance of a newsletter-writing crew by accurately following instructions and producing high-quality output.
• The price-to-speed ratio of GPT-4.1 makes it a compelling choice for AI applications.

3. Performance Data and Benchmarks

• SWE-bench: GPT-4.1 outperformed GPT-4.0 and other models.
• Polyglot: Gemini 2.5 Pro outperformed GPT-4.1.
• MRC Score: Gemini 2.5 Pro significantly outperformed GPT-4.1.
• GPT-4.1 is not the best model for performing math or analyzing images.

4. Synthesis/Conclusion

While GPT-4.1 offers improvements in context window size, conversational understanding, instruction following, and coding performance compared to GPT-4.0, it is not the top choice for all tasks. Gemini 2.5 Pro and Claude 3.5 Sonnet are better options for coding and chatting, respectively. However, GPT-4.1 shines in AI agent applications where strong instruction following is crucial. Its improved performance in this area, combined with its reasonable price and speed, makes it a valuable tool for AI developers building complex AI-powered applications. The key takeaway is to carefully consider the specific requirements of the task at hand when selecting the appropriate model.