GPT-4.1 Released: Benchmarks, Performance, and How to Safely Migrate to Production

Yusuf Ishola· April 15, 2025

OpenAI has just released GPT-4.1, and in typical OpenAI fashion, they've baffled everyone with their naming scheme again.

Just when we thought we were finally moving to GPT-5, they've surprised us with GPT-4.1—a model that is in many ways an upgrade from GPT-4.5.

But naming conventions aside, this release is significant. GPT-4.1 introduces a family of three models designed specifically for developers, featuring major improvements in coding, instruction following, and long context handling.

Table Of Contents

• What's New in GPT-4.1?
• GPT-4.1 Performance Benchmarks & Code Generation
• Developer Reactions & Real-World Examples
• Comparing GPT-4.1 Models
• GPT-4.1 vs. Gemini 2.5 vs Claude 3.7 Sonnet
• How to Access GPT-4.1
• Effective Prompting Techniques for GPT-4.1
• How to Safely Test GPT-4.1 in Production
• Final Thoughts

What's New in GPT-4.1?

• A family of three non-thinking models: GPT-4.1 (full-size), GPT-4.1 Mini (balanced), and GPT-4.1 Nano (small and fast), each targeting different use cases and price points.
• Massive context window: All three models support up to 1 million tokens of context, 8x more than GPT-4o's 128K limit. Perfect for processing entire codebases or lengthy documents.
• API-only release: Unlike previous models, GPT-4.1 is exclusively available through the API, not in ChatGPT's interface.
• Knowledge cutoff of June 2024: GPT-4.1 has the most recent cutoff of any OpenAI model.
• Lower pricing across the board: The main GPT-4.1 model is 26% cheaper than GPT-4o, while GPT-4.1 Mini outperforms GPT-4o on many benchmarks at 83% lower cost.
• Developer-focused: Built specifically for developer workflows with extensive real-world testing of coding capabilities.

GPT-4.1 Performance Benchmarks & Code Generation

Coding Performance

The coding improvements are substantial:

• SWE-bench Verified: One test measuring ability to solve real GitHub issues in actual codebases, GPT-4.1 scored 54.6%, far outperforming GPT-4o (33.2%) and GPT-4.5 (28%).
• Code Diff Accuracy: When asked to modify only specific parts of code instead of rewriting entire files, GPT-4.1 achieved 52.9% accuracy compared to GPT-4o's 18.3%.
• Extraneous Edits: GPT-4.1 rarely touches files it shouldn't, with unnecessary edits dropping from 9% (with GPT-4o) to just 2%.
• Real-world Testing: Windsurf, a popular coding tool, reports that GPT-4.1 scored 60% higher on their internal benchmarks and was 30% more efficient at using programming tools.

Instruction Following

• Complex Instructions: When given difficult multi-step instructions with specific formatting requirements, GPT-4.1 correctly followed them 49% of the time compared to GPT-4o's 29%.
• Multi-turn Conversations: On the MultiChallenge benchmark testing how well models maintain context through conversation, GPT-4.1 scored 38.3%, a 10.5% improvement over GPT-4o.
• Following Constraints: When explicitly told what not to do, GPT-4.1 achieved 87.4% compliance on the IFEval benchmark versus 81.0% for GPT-4o.

Long Context Performance

• Context Window: All three GPT-4.1 models can process up to 1 million tokens at once—8 times more than GPT-4o's 128K limit.
• Finding Specific Information: When challenged to locate particular information in massive documents (the "needle-in-haystack" test), GPT-4.1 achieved 100% accuracy across all context lengths.
• Video Understanding: On tests analyzing 30-60 minute videos without subtitles, GPT-4.1 scored 72.0%, improving 6.7% over GPT-4o.

Developer Reactions & Real-World Examples

The developer community's response has been enthusiastic. One developer commented:

I just worked in cursor with it [GPT 4.1] for a few site updates…holy f**k balls

Here are some real-world examples of what it can do:

FlashCard App Creation

Source: OpenAI's official blog

Ball in a Hexagon

SVG Butterfly Comparison: GPT-4.1 vs Competitors

When asked to generate an SVG butterfly, GPT-4.1's output shows impressive detail and execution compared to other leading models like Gemini 2.5 and Claude 3.7 Sonnet.

Comparing GPT-4.1 Models

GPT-4.1 vs. Gemini 2.5 vs Claude 3.7 Sonnet

When compared to other leading models, GPT-4.1 shows strong performance across the board, especially in coding tasks and long-context handling.

Source: Artificial Analysis

How to Access GPT-4.1

GPT-4.1 is available exclusively through the OpenAI API. Use the model string gpt-4.1, gpt-4.1-mini, or gpt-4.1-nano in your requests.

You can easily start testing these models in the OpenAI Playground.

Effective Prompting Techniques for GPT-4.1

Based on OpenAI's official prompting guide, here are the most effective techniques for getting the best results from GPT-4.1:

• Be extremely clear and specific: GPT-4.1 follows instructions more literally than previous models. If the output is different from your expectations, a single sentence describing your desired behavior is usually enough.
• Use delimiters effectively: Markdown sections (###), XML tags, and backticks for code help structure your prompt. For document retrieval, XML tags performed best in testing.
• For agent workflows: Include reminders about persistence ("keep going until the user's query is completely resolved"), tool-calling ("use your tools to read files, don't guess"), and planning ("plan extensively before each function call").
• For long context: Place your most important instructions at both the beginning AND end of your prompt.
• For complex reasoning: While not a reasoning model like o3, GPT-4.1 responds well to step-by-step prompting. Try adding: "First, think carefully step by step about..." to break down complex problems.
• For code editing: GPT-4.1 excels at creating code diffs rather than rewriting entire files. It's significantly better than previous models at modifying only the necessary parts of code.

How to Safely Test GPT-4.1 in Production

Want to try GPT-4.1 without disrupting your existing apps? Here's how you can use Helicone to make the transition seamless:

1. Log your current model requests: Log at least 10 logs in your Helicone dashboard. This will give you a baseline of performance.
2. Compare performance: Test GPT-4.1 against your current model with identical prompts and inputs. The Prompt Editor or Experiments feature are designed for this purpose.
3. Analyze results: Compare outputs, costs, and latency between your current model and GPT-4.1 side-by-side. Make sure GPT-4.1 meets your requirements with your actual production prompts.
4. Roll out gradually: Shift traffic from your current model to GPT-4.1 incrementally. Make sure to monitor the performance in Helicone.

You can follow this guide for more detailed instructions!

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://oai.helicone.ai/v1",
    default_headers={
      "Helicone-Auth": f"Bearer {HELICONE_API_KEY}",
    }
)

response = client.chat.completions.create(
    model="gpt-4.1", # Or "gpt-4.1-mini" or "gpt-4.1-nano"
    messages=[
        {"role": "user", "content": "Write a Python program that shows a ball bouncing inside a spinning hexagon."}
    ]
)
print(response.choices[0].message.content)

Final Thoughts

GPT-4.1 represents a significant step forward for developers using AI. Its focus on real-world utility rather than just benchmark scores makes it particularly valuable for production applications.

The introduction of GPT-4.1 Mini and Nano variants provides flexible options for different use cases and budget constraints, while the massive 1 million token context window—now quickly becoming the norm—opens new possibilities for complex application development.

OpenAI has promised two new models (o3 and o4-mini) will follow shortly, so stay tuned for those!

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• GPT-4o Mini vs. Claude 3.5 Sonnet