Comparison of GPT-4o and GPT-4.1 | Generated by AI

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Executive Summary

This report provides a comprehensive analysis of OpenAI’s GPT-4o and GPT-4.1 models, specifically examining their capabilities, performance, and integration within GitHub Copilot for Visual Studio Code (VS Code) and JetBrains IDEA environments. The analysis reveals that GPT-4.1 represents a substantial architectural and performance leap over its predecessor, GPT-4o, establishing itself as the new standard for AI-assisted development within GitHub Copilot. Its superior proficiency in coding, enhanced instruction following, and significantly expanded context window directly translate to heightened developer productivity and the enablement of more reliable AI agent workflows.
A key distinction lies in GPT-4.1’s marked improvements across critical benchmarks. For instance, it achieves a 54.6% success rate on SWE-bench Verified, demonstrating a substantial 21.4% absolute improvement over GPT-4o’s 33.2%.1 Furthermore, GPT-4.1 more than doubles GPT-4o’s score on Aider’s polyglot diff benchmark, indicating superior accuracy in generating code changes.1 The model’s massive 1 million token context window 1 dramatically expands its understanding of entire codebases, a significant upgrade from GPT-4o’s 128K tokens.3 Concurrently, its instruction following reliability has been notably enhanced.1
GitHub Copilot has strategically transitioned GPT-4.1 to be the new default model for Copilot Chat, Edits, and Agent mode, with a clear plan to deprecate GPT-4o for these functionalities within 90 days.12 While GPT-4o Copilot, a fine-tuned GPT-4o mini, currently remains the default for code completion 14, the overarching trend indicates GPT-4.1’s imminent dominance across Copilot’s entire feature set. Both models are accessible within VS Code and JetBrains IDEs through the Copilot extension.14 However, it is observed that feature parity and the speed of new model rollouts may vary slightly between IDEs, with VS Code often receiving updates and preview features earlier than JetBrains IDEs.14

1. Introduction to GitHub Copilot’s AI Models

GitHub Copilot operates as an advanced AI pair programmer, seamlessly integrated into contemporary software development workflows. Its primary function is to enhance developer productivity by providing real-time code suggestions, offering conversational assistance through Copilot Chat, and supporting sophisticated functionalities such as code refactoring, debugging, and project scaffolding directly within Integrated Development Environments (IDEs) like Visual Studio Code and JetBrains IDEA.14 The tool’s core value proposition lies in its ability to accelerate development cycles, automate repetitive coding tasks, and assist with complex problem-solving, thereby significantly boosting overall developer efficiency.
The efficacy and capabilities of GitHub Copilot are intrinsically linked to the performance and characteristics of the underlying Large Language Models (LLMs) it leverages. These foundational models dictate the quality and relevance of code generation, the depth of contextual understanding, response speed, and the associated operational costs. GitHub Copilot provides users with the flexibility to select from a range of these underlying AI models, enabling developers to optimize the AI assistance for specific tasks or individual preferences.14 This adaptability is crucial for tailoring the AI’s behavior to diverse development needs, ranging from rapid prototyping to intricate, multi-file refactoring operations.
The landscape of AI models is characterized by continuous and rapid innovation. OpenAI’s consistent advancements in its GPT series directly influence the evolution of tools like GitHub Copilot. Each new generation of models introduces substantial performance enhancements, efficiency gains, and expanded capabilities, consistently pushing the boundaries of what AI can achieve within a developer’s environment. This dynamic and iterative improvement necessitates a thorough and ongoing understanding of the distinctions between successive models to effectively harness Copilot’s full potential and maintain a competitive edge in software development.

2. GPT-4o: Baseline Capabilities and Initial Role

GPT-4o, where the “o” stands for “omni,” was introduced as a groundbreaking multimodal AI model, signifying a major architectural shift. This model possessed the native capability to seamlessly process and generate content across text, images, audio, and video modalities within a single neural network.9 This unified multimodal support represented a significant technological leap, enabling more intuitive human-computer interactions, exemplified by features like real-time audio conversations and direct visual question answering.22 The introduction of GPT-4o marked a notable strategic shift for OpenAI, emphasizing a balance of multimodal capabilities, real-time performance, and cost reduction. This was not merely an incremental improvement in intelligence but a fundamental change in AI design, reflecting the industry’s growing demand for more versatile and efficient AI tools.
A key advantage of GPT-4o was its reported speed, demonstrating the ability to generate tokens twice as fast as its predecessor, GPT-4 Turbo.24 Furthermore, it offered a notable reduction in operational costs, approximately 50% lower than GPT-4.9 Its remarkable ability to respond to audio input in a mere 320 milliseconds, closely mirroring typical human response times, marked a substantial improvement in real-time latency for conversational AI.22 This emphasis on lightning-fast speed and near-instant responses highlighted that perceived responsiveness is a critical factor in the adoption of AI models for interactive tools like Copilot. For a tool that provides real-time suggestions and chat, immediate responsiveness is paramount to maintaining developer flow and productivity. A model that is technically superior but introduces noticeable delays would hinder adoption and user satisfaction, underscoring OpenAI’s and GitHub’s prioritization of user experience metrics.
In terms of intellectual capabilities, GPT-4o showcased improved reasoning, coupled with advanced memory and context handling, which facilitated complex problem-solving.9 It was proficient in tasks such as auto-generating code, debugging, and documenting 9, and demonstrated enhanced performance in multilingual contexts and when interpreting visual content.10 The model featured a context window of 128K tokens 3, which, at its release, was a considerable improvement over earlier models.
Within GitHub Copilot, GPT-4o played a prominent role following its release. A fine-tuned variant, specifically referred to as “GPT-4o Copilot” (based on GPT-4o mini), was established as the default model for code completions for all Copilot users, replacing the previous GPT-3.5 Turbo-based model.14 This specialized model benefited from extensive training on a vast dataset of high-quality public GitHub repositories, providing comprehensive coverage across more than 30 programming languages.14 This integration into Copilot as the default code completion model suggested GitHub’s initial priority was broad, efficient, and affordable code generation for common scenarios, setting a strong baseline for performance and user experience within the IDE. Additionally, GPT-4o was available for selection within Copilot Chat, proving effective for lightweight development tasks and general conversational prompts.16 The simultaneous release of GPT-4o, GPT-4o mini, and GPT-4o nano also highlighted a deliberate strategy by OpenAI to cater to diverse performance and cost requirements, allowing for broader accessibility and integration into various applications, from high-demand real-time systems to cost-sensitive scenarios.

3. GPT-4.1: Architectural Advancements and Current Status

GPT-4.1, released on April 14, 2025 5, is heralded as the “latest flagship” model 11 and a “revamped version of OpenAI’s GPT-4o model”.21 It builds upon GPT-4o’s foundation with substantial “structural improvements” 8, signifying a continuous and rapid iteration in AI model development. This swift progression, with GPT-4.1 following GPT-4o’s general availability for Copilot, demonstrates OpenAI’s commitment to delivering cutting-edge capabilities and a developer-first strategy. The explicit optimization based on “direct developer feedback” 1 underscores a deep understanding of developer pain points and the need for more precise, reliable AI assistance.
The core architectural improvements in GPT-4.1 are primarily focused on enhancing its utility for software development tasks.

Current Status and Strategic Shift in GitHub Copilot:
GPT-4.1 is rapidly becoming the new standard within GitHub Copilot, marking a significant strategic shift. As of May 8, 2025, GPT-4.1 is being rolled out as the new default model for Copilot Chat, Edits, and Agent mode.12 This transition is explicitly positioned as a direct upgrade from GPT-4o.12 GitHub has announced that GPT-4o will remain available in the model picker for 90 days following the rollout of GPT-4.1 as default, after which it will be deprecated from these roles.12 This signals a clear strategic pivot by GitHub towards GPT-4.1 as the primary and preferred model across most Copilot functionalities. The explicit engineering of GPT-4.1 for “coding and instruction following” 1 demonstrates a deep understanding of developer pain points and the need for more precise, reliable AI assistance, moving towards models purpose-built for software engineering tasks.
Regarding code completion, the default model was “GPT-4o Copilot” (a fine-tuned GPT-4o mini) as of March 27, 2025.14 However, GPT-4.1 is already available for manual selection in code completion within the latest VS Code and JetBrains IDEs.14 Given its superior coding benchmarks 1, it is highly anticipated that GPT-4.1 will soon become the universal default for code completion as well. GPT-4.1 is accessible across all GitHub Copilot Plans, including the Copilot Free tier 26, ensuring broad access to its enhanced capabilities. This rapid pace of innovation means developers need to stay agile and continuously adapt their workflows to leverage the latest model capabilities.
The significant gains in “instruction following” and “long-context understanding” 1 are explicitly linked to GPT-4.1’s effectiveness at “powering agents” or “agentic workflows”.1 The ability to follow multi-step instructions, maintain coherence in long conversations, and process entire codebases 1 is fundamental for AI agents that can independently accomplish complex tasks. This signifies a shift beyond simple code completion or chat toward more autonomous AI assistants that can tackle multi-faceted software engineering problems, potentially revolutionizing how features are built and bugs are fixed.

4. Comprehensive Performance Comparison: GPT-4o vs. GPT-4.1

This section provides a detailed, data-driven comparison of GPT-4o and GPT-4.1, leveraging available benchmarks and qualitative observations to highlight GPT-4.1’s superior performance across key metrics.
Table 1: GPT-4o vs. GPT-4.1 Core Capabilities & Benchmarks
This table serves as a crucial reference, offering a concise, at-a-glance comparison of the most critical performance metrics. It allows developers to rapidly grasp the magnitude of improvement GPT-4.1 offers over GPT-4o by consolidating scattered benchmark data into an easily digestible format. This direct comparison is essential for informed decision-making regarding model selection.

Feature/Metric GPT-4o GPT-4.1 Significance
Release Date May 13, 2024 (Approx.) April 14, 2025 5 GPT-4.1 is a newer, more advanced iteration.
SWE-bench Verified Score (Coding) 33.2% 1 54.6% 1 21.4% absolute improvement; measures real-world software engineering skills.
Aider Polyglot Diff Score (Coding Accuracy) ~25% (Inferred) 1 52.9% 1 More than doubles GPT-4o’s score; indicates superior reliability in generating precise code diffs.
Extraneous Code Edits 9% 1 2% 1 Drastic reduction in unnecessary modifications, leading to cleaner code and faster reviews.
MultiChallenge Score (Instruction Following) 27.8% 1 38.3% 1 10.5% absolute improvement; measures ability to follow multi-turn instructions.
IFEval Score (Instruction Following) 81.0% 1 87.4% 1 Improved compliance with verifiable instructions and formatting rules.
Context Window 128K tokens 3 1 Million tokens 1 8x increase; enables understanding of entire codebases (approx. 3,000 pages).
Relative Cost (API) More affordable than GPT-4 Turbo 24, ~50% lower than GPT-4.9 “Lower cost” 1, “cheaper than GPT-4o” 2, “80% lower input costs compared to earlier models”.8 Optimized for performance at a reduced operational expense.
Relative Speed/Latency Twice as fast as GPT-4 Turbo 24, “Lightning-Fast” 9, “near-instant responses”.9 “Up to 40% faster than GPT-4o” 4, “Fastest” 11, “similar speed” to GPT-4o.3 Maintains or improves responsiveness while increasing intelligence.
Multimodality Text, Image, Audio, Video 9 Advanced Text, Image, Audio, Video 3 Both are multimodal; GPT-4.1 shows enhanced understanding of complex visual data.
Knowledge Cutoff Not explicitly stated, assumed earlier than GPT-4.1 June 2024 2 More up-to-date training data for GPT-4.1.

Note: The Aider Polyglot Diff Score for GPT-4o is inferred from GPT-4.1’s score and the statement that it “more than doubles GPT-4o’s score.”

4.1. Coding Performance

GPT-4.1 consistently demonstrates a significant lead in coding-specific benchmarks, positioning it as a superior tool for developers. On SWE-bench Verified, a benchmark measuring real-world software engineering skills, GPT-4.1 achieves a 54.6% success rate, representing a substantial 21.4% absolute improvement over GPT-4o’s 33.2%.1 This indicates GPT-4.1’s enhanced ability to explore code repositories, complete tasks, and produce runnable, test-passing code. For code diff generation, GPT-4.1 scores 52.9% on Aider’s polyglot diff benchmark, which is more than double GPT-4o’s estimated performance.1 This metric is crucial for its reliability in producing precise code changes across various programming languages and formats, enabling developers to save cost and latency by outputting only changed lines.
Beyond raw scores, GPT-4.1 exhibits critical qualitative improvements in code generation. It makes “extraneous edits less frequently,” with the rate dropping significantly from 9% with GPT-4o to a mere 2%.1 This reduction in unnecessary modifications directly translates to cleaner, more maintainable code and faster review cycles. GPT-4.1 is also “much more reliable at code diffs” across formats.1 For frontend coding, human graders expressed a preference for GPT-4.1’s generated web applications over GPT-4o’s 80% of the time, citing more functional and aesthetically pleasing results.1 Internal evaluations by developers reported GPT-4.1 to be “60% better than GPT-4o” in internal coding benchmarks, which correlates strongly with how often code changes are accepted on the first review.1 User feedback further corroborates this, with reports of GPT-4.1 successfully refactoring “1000- to 1200-line React components” into modular structures in agent mode, a task GPT-4o previously struggled with.27 This higher level of reliability and precision means developers spend significantly less time correcting or refining AI-generated code, leading to genuine and substantial productivity gains. It allows developers to confidently delegate more complex, multi-file, and architectural tasks to the AI, thereby freeing up human developers for higher-level architectural design, complex problem-solving, and creative innovation.

4.2. Instruction Following & Steerability

GPT-4.1 demonstrates notable gains in instruction following, a critical capability for AI assistants. It scores 38.3% on the MultiChallenge benchmark, representing a 10.5% absolute increase over GPT-4o’s 27.8%.1 This benchmark measures the model’s ability to follow multi-turn instructions and maintain coherence deep into a conversation, picking out information from past messages.1 On IFEval, which assesses compliance with verifiable instructions, such as specifying content length or avoiding certain terms or formats, GPT-4.1 achieves 87.4%, up from 81% for GPT-4o.1
OpenAI explicitly trained GPT-4.1 to “follow instructions more literally, making the model more steerable”.1 Early testers confirmed this, noting it “can be more literal” 1, and user feedback praises its ability to follow instructions precisely and states it “doesn’t do more than I ask it to”.27 This enhanced literal adherence is crucial for building reliable and predictable AI agents and automated workflows.1 The explicit emphasis on “literal” instruction following and the improved scores on benchmarks like IFEval directly address a common challenge with many LLMs: their tendency to hallucinate or deviate from explicit, multi-step instructions. For developers building automated workflows, AI agents, or relying on AI for precise, rule-based tasks, trust in the AI’s ability to follow instructions exactly as given is paramount. GPT-4.1’s enhanced steerability cultivates this trust, enabling the creation of more robust, predictable, and dependable AI-driven processes, which is an essential prerequisite for truly effective agentic capabilities in software engineering.

4.3. Context Window & Long-Context Understanding

GPT-4.1 features an industry-leading 1 million token context window.1 This represents an 8x increase over GPT-4o’s 128K tokens 3, allowing it to process an equivalent of “more than 750,000 words of text - about 3,000 pages”.2 This is not merely a quantitative increase; it represents a qualitative leap in the AI’s ability to comprehend large-scale information, allowing the model to process “entire codebases, long documents, or multiple files at once”.2 This directly addresses a traditional limitation of AI assistants, where context awareness often concentrated on the active file or a small window of recent code.28
The model incorporates “better attention mechanisms to correctly find and retrieve information from these long contexts”.8 Its performance on long-context benchmarks reflects this, with Video-MME (long, no subtitles) improving to 72.0% for GPT-4.1 from 65.3% for GPT-4o.1 On Graphwalks, a benchmark for multi-hop reasoning within long contexts, GPT-4.1 achieves 61.7% compared to GPT-4o’s 41.7%.3 This dramatically expanded context enables AI assistants to understand the broader architecture, inter-dependencies, coding conventions, and implicit knowledge of an entire software project or large sub-system. This is profoundly crucial for complex tasks such as large-scale refactoring, migrating legacy projects, generating comprehensive test suites, or performing security analysis that spans multiple files and modules, effectively transforming Copilot from a “snippet generator” to a “project-aware architect” capable of holistic problem-solving.

4.4. Speed, Latency, and Cost Efficiency

GPT-4.1 is strategically positioned as a “smarter (and cheaper) model at a similar speed” compared to GPT-4o.3 While GPT-4o was lauded for its speed, generating tokens twice as fast as GPT-4 Turbo and offering “lightning-fast” near-instant responses 9, GPT-4.1 is also noted as being “up to 40% faster than its predecessors, GPT-4o and GPT-4.5”.4 This indicates a continuous drive for performance optimization, ensuring that increased intelligence does not come at the expense of responsiveness.
In terms of cost, GPT-4.1 is designed to offer “exceptional performance at a lower cost” 1 and achieves “80% lower input costs compared to earlier models”.8 The introduction of the GPT-4.1 mini and nano variants further underscores this focus, as they are explicitly engineered for even lower latency and cost, making advanced AI capabilities more economically viable for a wider range of applications.1 This relentless focus on efficiency makes the more powerful and capable AI models economically viable for high-volume, real-time developer workflows. It effectively democratizes access to cutting-edge AI capabilities by making them more affordable, thereby accelerating the widespread integration of advanced AI into everyday development practices for a broader user base and enabling new applications that were previously cost-prohibitive.

4.5. Multimodal Capabilities

GPT-4.1 maintains its full multimodal support, akin to GPT-4o, capable of handling and integrating text, images, and other modalities, with the benefit of “advanced embedding techniques” for improved processing.8 While GPT-4o natively handled audio and video 9, GPT-4.1 demonstrates continued progress on multimodal benchmarks, scoring 72.0% on Video-MME and 74.8% on MMMU.3
Models with visual input capabilities, including GPT-4 variants, are valuable for processing images like screenshots for contextual understanding. This is particularly useful for tasks such as applying design changes from mockups or debugging visual discrepancies in user interfaces.20 The continued emphasis on robust multimodal capabilities in GPT-4.1 suggests a future where developers interact with their AI assistants not solely through code and text prompts, but also visually. This opens up new and more intuitive interaction paradigms for developers, allowing for more natural and efficient communication with the AI, especially for tasks related to front-end development, UI/UX design, or debugging visual bugs. It moves Copilot towards a more holistic understanding of the entire software development process, encompassing visual and conceptual inputs alongside traditional code.

5. Integration and User Experience Across IDEs (VS Code & JetBrains IDEA)

GitHub Copilot is designed for broad compatibility across popular Integrated Development Environments, with specific integration nuances for VS Code and JetBrains IDEA.

5.1. Model Selection in Copilot Chat

Users of GitHub Copilot Chat have the flexibility to select different underlying AI models to power their interactions. In VS Code and within the immersive view of Copilot Chat on GitHub.com, developers can choose from a range of models, including GPT-4o, GPT-4.1, GPT-4.5 (preview), various Claude Sonnet models (3.5, 3.7, 3.7 Thinking, 4 preview, Opus 4 preview), and Gemini models (2.0 Flash, 2.5 Pro preview).16 This model selection can be dynamically changed during an ongoing chat session, allowing for real-time adaptation to conversational needs or task requirements.16 Similarly, in JetBrains IDEs, model selection for Copilot Chat is available through the GitHub Copilot extension, providing a consistent experience across major development environments.14 As of May 8, 2025, GPT-4.1 has been rolled out as the new default model for Copilot Chat, reflecting its superior capabilities for general development tasks.12

5.2. Model Selection for Code Completion

By default, Copilot’s inline code completion functionality utilizes “GPT-4o Copilot,” which is a fine-tuned GPT-4o mini model specifically optimized for this task.14 Developers can, however, manually switch AI models for code completion in the latest releases of VS Code, Visual Studio (version 17.14 Preview 2 and later), and JetBrains IDEs, provided they have the latest GitHub Copilot extension installed.14 In VS Code, this is typically performed via the command palette by searching for “GitHub Copilot: Change Completions Model” or through the Command Center.14 For JetBrains IDEs, the selection is made via the status bar icon, selecting “Edit Model for Completion,” and then choosing from the dropdown menu in the settings dialog for “Languages & Frameworks > GitHub Copilot”.14 It is important to note that changing the model used for Copilot Chat does not affect the model used for Copilot code completion, allowing for independent optimization of these two core functionalities.14

5.3. Observed User Experience & Community Feedback

User feedback and internal evaluations highlight significant improvements in the practical application of GPT-4.1. The model has shown exceptional performance in refactoring large and complex codebases. Developers have reported its ability to “easily refactor 1000- to 1200-line React components into hooks, services, utility files, granular components and.scss modules in one prompt in agency mode,” a task that GPT-4o previously struggled with.27 This underscores GPT-4.1’s superior long-context understanding and instruction-following capabilities for multi-file operations.
Furthermore, developers have noted a significant improvement in GPT-4.1’s reliability, specifically its capacity to “lint and fixes all the TypeScript and ESlint errors automatically” resulting in “no errors in my project at the end.” This marks a substantial enhancement compared to previous experiences with GPT-4o, where more time was often spent correcting AI-generated errors.27 User feedback also suggests GPT-4.1 is more precise and adheres more closely to the prompt’s scope, providing concise responses, in contrast to some other models that might “over code”.27 This qualitative observation aligns with the quantitative “fewer extraneous edits” benchmark.1
Regarding IDE parity between VS Code and JetBrains, while GitHub Copilot offers broad support, some users have reported that advanced features, such as multi-model support, multi-file editing, or agentic modes, tend to “lag behind” in JetBrains IDEs compared to their VS Code counterparts.19 This disparity is often attributed to GitHub’s development priorities rather than inherent limitations of JetBrains’ plugin API.19 The overall sentiment from the developer community regarding GPT-4.1 as the new base model is largely positive, with users describing it as “solid” and “perfect” for its ability to follow instructions precisely and handle larger contexts effectively.27

5.4. Contextual Comparison with JetBrains AI Assistant

For developers operating within the JetBrains ecosystem, a contextual comparison with the native JetBrains AI Assistant is pertinent. JetBrains AI Assistant is characterized by its “deep integration” within JetBrains IDEs, offering “native performance” and leveraging the IDE’s “intrinsic knowledge of your codebase” for advanced code analysis and contextual refactoring.18 This deep integration allows for seamless operation with existing JetBrains features, ensuring smooth workflows and reducing context switching.18 In contrast, while Copilot is broadly compatible across IDEs, its integration may feel “less native” within JetBrains IDEs compared to the JetBrains AI Assistant.29 JetBrains AI Assistant also excels in providing “in-depth code analysis” and maintaining “deeper project context”.18 The pricing model also differs, with JetBrains AI Assistant typically bundled with JetBrains subscriptions, potentially offering a more economical solution for teams already invested in their suite of products, whereas GitHub Copilot generally requires a separate subscription.18 The dynamic between these tools highlights a fundamental tension between deep, native integration within a single IDE ecosystem and broad, cross-platform compatibility.
Table 2: GitHub Copilot Model Availability & Default Status in IDEs
This table clarifies the current state of model availability and default settings within VS Code and JetBrains IDEA, directly addressing the user’s specific query. It provides a clear, structured overview of the current default models for different Copilot functionalities and outlines the methods for users to switch between available models. This is invaluable for developers seeking to optimize their Copilot experience across different IDEs. The table highlights any known differences or potential feature lags between the IDEs, adding crucial practical context for developers.

Copilot Feature Default Model Available Models VS Code Access/Switching Method JetBrains IDEA Access/Switching Method Notes
Copilot Chat GPT-4.1 12 GPT-4o, GPT-4.1, GPT-4.5 (preview), Claude Sonnet 3.5/3.7/3.7 Thinking/4 (preview), Claude Opus 4 (preview), Gemini 2.0 Flash/2.5 Pro (preview), o1 (preview) 16 Chat icon in activity bar / Ctrl+Alt+i (Win/Linux) or Cmd+Ctrl+i (Mac) -> CURRENT-MODEL dropdown 16 Status bar icon -> Open GitHub Copilot Chat -> CURRENT-MODEL dropdown 16 GPT-4o to be deprecated in 90 days for Chat/Edits/Agent.12 Multi-model support is in public preview.16
Code Completion GPT-4o Copilot (fine-tuned GPT-4o mini) 14 GPT-4o Copilot, GPT-4.1 (available for selection in latest extensions) 14 Command Palette -> “GitHub Copilot: Change Completions Model” OR Command Center -> Configure Code Completions 14 Status bar icon -> Edit Model for Completion -> Settings dialog for “Languages & Frameworks > GitHub Copilot” -> Model for completions dropdown 14 Code completion model selection is independent of chat model.14 Feature parity may lag in JetBrains IDEs.19
Edits & Agent Mode GPT-4.1 12 GPT-4.1, GPT-4o (available in model picker for 90 days) 12 Via Copilot Chat or specific agent commands 16 Via Copilot Chat or specific agent commands 16 GPT-4o to be deprecated in 90 days for these modes.12 Agent mode in public preview.16

6. Strategic Recommendations for Developers

To maximize the benefits of GitHub Copilot in modern development workflows, developers should strategically leverage the capabilities of GPT-4.1 and understand its optimal application scenarios.

Conclusion

GPT-4.1 marks a pivotal and transformative moment in the landscape of AI-assisted software development. Its integration as the new default model across key functionalities within GitHub Copilot, coupled with the impending deprecation of GPT-4o for these roles, signifies a clear and decisive shift towards a new benchmark for AI capabilities in the developer workflow.
The advancements embodied in GPT-4.1—particularly its remarkable improvements in coding accuracy, strict adherence to instructions, and its expansive 1 million token context window—directly address critical pain points and long-standing limitations for developers. These enhancements empower Copilot to evolve beyond a sophisticated code completion tool into a more intelligent, reliable, and truly agentic coding partner, capable of tackling complex, multi-faceted software engineering challenges with unprecedented precision and contextual understanding. The strategic focus on optimizing the “cost-performance frontier” by OpenAI ensures that these powerful capabilities are also economically viable for widespread adoption and high-frequency usage.
This strategic move by GitHub and OpenAI underscores a maturation of AI’s role in the software development lifecycle, moving from general-purpose AI to models purpose-built for the nuanced demands of software engineering. The continuous evolution of models like GPT-4.1, with their relentless focus on efficiency, reliability, and deeper contextual understanding, promises to further accelerate developer workflows and pave the way for the emergence of even more autonomous AI agents in the near future. Developers who proactively embrace and master these advanced AI capabilities will undoubtedly gain a significant competitive advantage in the rapidly evolving technological landscape.

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