GPT-4.1 is HERE! The ultimate coding model

Introduction to GPT-4.1

Overview of GPT-4.1 Release

• GPT-4.1 has been announced as a new model, significantly better and cheaper than its predecessor, GPT-4.0.

• It is exclusively available via API, tailored for developers rather than through the ChatGPT interface.

Model Variants

• The GPT-4.1 family includes three models: 4.1, 4.1 Mini, and 4.1 Nano.

• Notably, the 4.1 Nano offers extremely fast performance with a context window of 1 million tokens.

Performance Improvements

Context Window and Utilization

• The new models outperform previous versions (GPT-4.0 and 4.5), especially in coding tasks and instruction following.

Benchmark Scores

• In coding benchmarks, GPT-4.1 scores 54.6 on SWE verified tests—an improvement of over 21% from GPT-4.0.

• For instruction following, it achieves a score of 38.3 on multi-challenge benchmarks—a notable increase over prior models.

Multimodal Capabilities

Long Context Understanding

• The model sets a new state-of-the-art result in multimodal long context understanding with a score of 72% on video benchmarks.

Deprecation of Previous Models

Transition from GPT-4.5

• OpenAI plans to deprecate GPT-4.5 due to GPU resource needs for the more efficient API-based model (GPT-4.1).

Future Developments

• There are expectations that improvements will continue for the larger model despite its current deprecation status.

Real-world Utility Focus

Developer Collaboration

• OpenAI collaborated with developers to ensure real-world utility in the design of these models.

Latency and Performance Metrics

Speed vs Intelligence Trade-off

• While latency metrics were not clearly labeled in graphs presented during the live stream, it was noted that:

• The GPT 4.1 Mini shows significant improvements in intelligence while maintaining similar latency compared to earlier models.

• Overall performance enhancements include reduced latency by nearly half while cutting costs by up to 83%.

Cost Structure and Token Context

Pricing Strategy

• All variants come with a generous context window of one million tokens without additional charges—unlike many competitors who impose extra fees for extended contexts.

First Look at GPT-4.1 and Box AI Studio

Overview of GPT-4.1 Capabilities

• The discussion highlights the impressive capabilities of GPT-4.1, particularly its ability to handle long context windows and multimodal understanding, which is beneficial for enterprise use cases.

Benchmark Comparisons

• A benchmark comparison between GPT-4.1 (in purple) and GPT-4.0 (in yellow) showcases significant improvements in data extraction from various document types.

Document Extraction Performance

• In tests involving secure data escrow, GPT-4.1 nearly doubles the performance with a perfect score, demonstrating enhanced abilities in extracting relevant information from complex documents like insurance documentation.

Practical Demonstration

• A live demo using NVIDIA's earnings report illustrates how effectively GPT-4.1 identifies key trends such as record revenue growth and specific comparisons to previous quarters.

Model Specifications and Improvements

• The nano version of GPT-4.1 features a 1 million token context window with notable scores on various benchmarks: 80.1% on MMLU, 50.3% on GPTQA, and 9.8% on Ader Polyglot coding.

Instruction Following Enhancements

• Improvements in instruction-following reliability make the new models more effective for powering agentic systems like Crew AI or Vibe Coding.

API Availability Changes

• Notably, GPT-4.1 will only be accessible via API in ChatGPT; many enhancements have been gradually integrated into this latest version.

Transition from Previous Versions

• Developers are informed that the deprecation of the 4.5 preview will occur by July 14th, 2025, causing potential challenges for those who recently adopted it.

Future Prospects of Models

• Despite concerns about deprecating model versions, it's suggested that insights gained from 4.5 may lead to future iterations or smaller models derived from it.

Specific Benchmarks Analysis

Sweepbench Performance

• The performance metrics reveal that GPT 4.1 outperforms other models like gpt03 mini high significantly while being faster and cheaper.

Real-world Coding Benchmark Insights

• Sweepbench verified accuracy indicates substantial improvement in generating code patches based on GitHub repositories compared to previous models.

Code Editing Efficiency

Diff vs Whole File Generation

• The model excels at editing specific portions of code rather than rewriting entire files, enhancing efficiency by focusing only on necessary changes.

Front-end Development Capabilities

• An example demonstrates how well the model performs in front-end coding tasks while also considering aesthetic aspects during development processes.

Improving 3D Animation and Instruction Following in GPT-4.1

Enhancements in 3D Animation and User Experience

• The speaker discusses the specific request for a 3D animation feature when clicking on flashcards, highlighting that while GPT-4.0 partially meets this need, improvements are evident in GPT-4.1.

• The new model demonstrates better color discovery and improved front-end coding capabilities, although further beautification is still needed.

Performance Metrics of GPT-4.1

• GPT-4.1 scores 60% higher than its predecessor on Windsurf's internal coding benchmark, indicating significant advancements in code acceptance rates during reviews.

• Users report that GPT-4.1 is 30% more efficient with tool calling and shows a 50% reduction in unnecessary edits or overly narrow code revisions.

Precision and Comprehensiveness

• Notably, GPT-4.1 excels at precision by knowing when not to make suggestions while also providing comprehensive analyses when necessary.

• Verun mentions that unlike other models which can be verbose, "other models tend to get very blabby," whereas GPT-4.1 maintains conciseness.

Instruction Following Improvements

• A demo showcased the enhanced instruction-following capabilities of the model, emphasizing its ability to adhere strictly to user-provided instructions.

• The internal evaluation categorizes instructions into various types (e.g., formatting), assessing performance across difficulty levels from easy to hard.

Benchmarking Against Previous Models

• In an internal OpenAI evaluation, GPT-4.1 achieved a 49% accuracy rate on hard instruction sets compared to only 29% for version 40.

• Multi-turn instruction handling has also seen substantial improvement; however, it still trails behind advanced thinking models.

Context Utilization Capabilities

• The importance of effectively utilizing a large context window (up to one million tokens) is emphasized as crucial for performance.

Demonstration of Log File Analysis

• A live demo illustrated the model's capability to identify anomalies within extensive log files without prior specification of what constitutes an anomaly.

Performance Benchmarks of GPT Models

Model Comparisons and Accuracy

• The performance of various models is discussed, highlighting that model 4.1 outperforms model 40 by approximately 6% in MMU accuracy, while still trailing behind model 01 high.

• Model 4.1 shows comparable efficiency to models 01 and 4.5 in terms of reasoning accuracy, indicating significant advancements in the newer models without sacrificing quality.

Pricing Structure

• The pricing for GPT model 4.1 is set at $2 per million tokens for input, with a total blended cost of $1.84 per million when considering output costs.

• Model 4.1 mini offers a more economical option at $0.40 for input and a total blended price of $0.42 per million tokens, suggesting it may be the standout choice from recent announcements.