In the ever-evolving world of artificial intelligence, developers, IT professionals, and even hobbyists are experiencing a pivotal transformation in how software is conceived, built, and maintained. Two years ago, the launch of OpenAI’s ChatGPT marked a new era—prompting a surge of AI-assisted coding tools. Today, with dozens of large language models (LLMs) vying for supremacy, the question that matters most for the Windows community is no longer whether AI can help with coding—but which AI should you actually choose for real-world programming tasks in 2025?
Since ChatGPT’s debut, the field of code-generating AIs has become both crowded and competitive. What started with a few experimenters testing ChatGPT’s ability to generate workable plugins has blossomed into a rigorous industry-wide evaluation of capabilities, reliability, security, and usability. Multiple LLMs now claim to help you design, debug, and deliver robust software faster than ever before. But as David Gewirtz’s comprehensive ZDNET analysis confirms, not all AI chatbots are created equal: only a handful are truly up to the task of handling diverse, real-world development challenges.
Over the course of fourteen LLM tests simulating authentic coding assignments—from writing WordPress plugins to debugging nuanced syntax errors—Gewirtz and his colleagues distilled the field down to a few top performers. Their findings are especially relevant for anyone in the Microsoft and Windows development ecosystems, as several key players have made dramatic leaps forward, while others promised more than they could deliver.
LLMs Supported: GPT-4o, Claude 3.5 Sonnet, Sonar Large, Claude 3 Opus, Llama 3.1 405B
Tests Passed: 4/4
Security: Email-only login, no multi-factor authentication (MFA)
Platform Support: Browser only, no dedicated desktop app
Perplexity Pro has emerged as an excellent choice for power users and professionals. Its main differentiator is the ability to switch among several industry-leading models. While it lacks traditional login credentials or MFA—raising some security concerns for enterprise deployment—its code generation abilities are second to none.
Through rigorous testing, Perplexity Pro—particularly when set to GPT-4o—aced every programming challenge, outperforming most rivals. The flexibility to cross-validate code across multiple AI engines adds a new dimension to AI-driven code review. However, the absence of desktop apps and strong authentication means extra caution is warranted if used in high-security environments, especially within regulated industries.
Notable Strength: The unique multi-LLM approach offers unprecedented code cross-checking and debugging flexibility, which can help reduce “hallucinated” answers—AI’s tendency to make plausible but incorrect statements.
Potential Risk: Security limitations and the lack of a native application may be a dealbreaker for some organizations.
LLMs Supported: Gemini Pro 2.5
Tests Passed: 4/4
Security: Multi-factor authentication supported
Platform Support: Browser only
Google’s LLM offerings stumbled early in the race but have rebounded impressively with Gemini Pro 2.5, which powered through all coding tests. However, the free tier is so severely restricted (users often cut off after just a few queries) that real development work requires paid access, which is metered by tokens.
Notable Strength: Impeccable code generation accuracy and robust security thanks to MFA options aligns well for those already invested in Google’s developer tools and cloud infrastructure.
Potential Risk: Predicting expenses can be challenging, given the token-based pricing model and frequent access throttling. For budget-conscious developers, this can introduce planning uncertainty, despite its technical prowess.
LLMs Supported: Microsoft proprietary (undisclosed for free version)
Tests Passed: 4/4
Security: Multi-factor authentication available
Platform Support: Browser only
Where Copilot once lagged, it now leads. Microsoft’s continuous refinement has transformed its AI coding assistant from an underperformer to a best-in-class solution, especially notable for passing all four independent coding tests even in the basic (free) version.
This newfound reliability, combined with tight integration with Microsoft’s ecosystem—especially Visual Studio and GitHub—makes it perhaps the best free option for .NET, C#, and Windows developers.
Notable Strength: Unmatched ecosystem integration with Visual Studio, Azure, and GitHub, along with the corporate backing and security posture expected from Microsoft.
Potential Risk: Features and performance for enterprise usage may hinge on licensing tiers, introducing complexity for organizations with diverse developer needs.
LLMs Supported: Grok-1
Tests Passed: 3/4
Security: Multi-factor authentication
Platform Support: Browser only
Initially met with skepticism, Grok by X (Twitter) exceeded expectations, delivering accurate code in all but the most niche scenarios. Its unique lineage, influenced by AI innovations from both Tesla and SpaceX, leaves much for the Windows developer community to watch.
Notable Strength: Diversity in the LLM ecosystem—being independent from OpenAI’s models—brings fresh problem-solving perspectives and potentially novel debugging approaches.
Potential Risk: Unclear future trajectory and features largely accessible "for free" only in the present. X’s history of abrupt feature changes means production users should keep alternatives handy.
ChatGPT Free: Uses GPT-3.5 when GPT-4o is unavailable (common during peak times). Excellent results, but session and prompt throttling can cut you off unexpectedly, undermining reliability for complex development cycles.
Perplexity Free: Based on GPT-3.5, offers robust research tools and cited sources. While it’s not as strong for code as its paid cousin, it’s particularly valuable for developers who research and code in tandem.
Strength: Cost-free, widespread availability, and solid baseline performance.
Risk: Lower reliability, prompt throttling, no dedicated Windows applications, and less effective support for advanced code scenarios.
LLMs Supported: DeepSeek MoE
Tests Passed: 3/4
Security: No MFA
Platform Support: Browser only
DeepSeek offers a rare open-source angle—important for developers who need transparency or run on restricted infrastructure. The V3 model’s efficiency and code quality, especially in common programming environments, show promise.
Strength: Open source, free-to-use chatbot; effective at routine programming tasks.
Risk: Inconsistent on obscure programming tasks; lacks the ecosystem and integrations seen with Microsoft or OpenAI offerings.
However, security, access, integration, and ongoing oversight are essential watchwords:
And as the competitive landscape shifts, keep testing, keep validating, and stay engaged: The “best” AI for coding is always evolving, and today’s runner-up could be tomorrow’s champion.
Source: ZDNET The best AI for coding in 2025 (including two new top picks - and what not to use)
The State of AI-Powered Coding: A Landscape Transformed
Since ChatGPT’s debut, the field of code-generating AIs has become both crowded and competitive. What started with a few experimenters testing ChatGPT’s ability to generate workable plugins has blossomed into a rigorous industry-wide evaluation of capabilities, reliability, security, and usability. Multiple LLMs now claim to help you design, debug, and deliver robust software faster than ever before. But as David Gewirtz’s comprehensive ZDNET analysis confirms, not all AI chatbots are created equal: only a handful are truly up to the task of handling diverse, real-world development challenges.Over the course of fourteen LLM tests simulating authentic coding assignments—from writing WordPress plugins to debugging nuanced syntax errors—Gewirtz and his colleagues distilled the field down to a few top performers. Their findings are especially relevant for anyone in the Microsoft and Windows development ecosystems, as several key players have made dramatic leaps forward, while others promised more than they could deliver.
The 2025 Code-Bot Leaderboard: Who Makes the Cut?
Among the 13 LLMs rigorously tested, only four earned high recommendations. Here’s an in-depth look at the standouts, their pricing, security features, and performance—plus a critical examination of those that fail to meet the mark.1. Perplexity Pro: Feature-Rich, Cross-Model Flexibility (Recommended)
Pricing: $20/monthLLMs Supported: GPT-4o, Claude 3.5 Sonnet, Sonar Large, Claude 3 Opus, Llama 3.1 405B
Tests Passed: 4/4
Security: Email-only login, no multi-factor authentication (MFA)
Platform Support: Browser only, no dedicated desktop app
Perplexity Pro has emerged as an excellent choice for power users and professionals. Its main differentiator is the ability to switch among several industry-leading models. While it lacks traditional login credentials or MFA—raising some security concerns for enterprise deployment—its code generation abilities are second to none.
Through rigorous testing, Perplexity Pro—particularly when set to GPT-4o—aced every programming challenge, outperforming most rivals. The flexibility to cross-validate code across multiple AI engines adds a new dimension to AI-driven code review. However, the absence of desktop apps and strong authentication means extra caution is warranted if used in high-security environments, especially within regulated industries.
Notable Strength: The unique multi-LLM approach offers unprecedented code cross-checking and debugging flexibility, which can help reduce “hallucinated” answers—AI’s tendency to make plausible but incorrect statements.
Potential Risk: Security limitations and the lack of a native application may be a dealbreaker for some organizations.
2. Google Gemini Pro 2.5: The Best for Google Ecosystem Users
Pricing: Free for limited use, then token-based chargesLLMs Supported: Gemini Pro 2.5
Tests Passed: 4/4
Security: Multi-factor authentication supported
Platform Support: Browser only
Google’s LLM offerings stumbled early in the race but have rebounded impressively with Gemini Pro 2.5, which powered through all coding tests. However, the free tier is so severely restricted (users often cut off after just a few queries) that real development work requires paid access, which is metered by tokens.
Notable Strength: Impeccable code generation accuracy and robust security thanks to MFA options aligns well for those already invested in Google’s developer tools and cloud infrastructure.
Potential Risk: Predicting expenses can be challenging, given the token-based pricing model and frequent access throttling. For budget-conscious developers, this can introduce planning uncertainty, despite its technical prowess.
3. Microsoft Copilot: A Resurgent Force for Windows Developers
Pricing: Free for basic version; paid licenses for advanced tiersLLMs Supported: Microsoft proprietary (undisclosed for free version)
Tests Passed: 4/4
Security: Multi-factor authentication available
Platform Support: Browser only
Where Copilot once lagged, it now leads. Microsoft’s continuous refinement has transformed its AI coding assistant from an underperformer to a best-in-class solution, especially notable for passing all four independent coding tests even in the basic (free) version.
This newfound reliability, combined with tight integration with Microsoft’s ecosystem—especially Visual Studio and GitHub—makes it perhaps the best free option for .NET, C#, and Windows developers.
Notable Strength: Unmatched ecosystem integration with Visual Studio, Azure, and GitHub, along with the corporate backing and security posture expected from Microsoft.
Potential Risk: Features and performance for enterprise usage may hinge on licensing tiers, introducing complexity for organizations with diverse developer needs.
4. Grok: A Contender from X (Formerly Twitter)
Pricing: Free for nowLLMs Supported: Grok-1
Tests Passed: 3/4
Security: Multi-factor authentication
Platform Support: Browser only
Initially met with skepticism, Grok by X (Twitter) exceeded expectations, delivering accurate code in all but the most niche scenarios. Its unique lineage, influenced by AI innovations from both Tesla and SpaceX, leaves much for the Windows developer community to watch.
Notable Strength: Diversity in the LLM ecosystem—being independent from OpenAI’s models—brings fresh problem-solving perspectives and potentially novel debugging approaches.
Potential Risk: Unclear future trajectory and features largely accessible "for free" only in the present. X’s history of abrupt feature changes means production users should keep alternatives handy.
Free Tier Champions (with Caveats): ChatGPT Free and Perplexity Free
Both OpenAI’s ChatGPT (Free version) and Perplexity’s free option impressed by passing most coding tests—particularly ChatGPT, which outperformed the majority of the LLM field.ChatGPT Free: Uses GPT-3.5 when GPT-4o is unavailable (common during peak times). Excellent results, but session and prompt throttling can cut you off unexpectedly, undermining reliability for complex development cycles.
Perplexity Free: Based on GPT-3.5, offers robust research tools and cited sources. While it’s not as strong for code as its paid cousin, it’s particularly valuable for developers who research and code in tandem.
Strength: Cost-free, widespread availability, and solid baseline performance.
Risk: Lower reliability, prompt throttling, no dedicated Windows applications, and less effective support for advanced code scenarios.
DeepSeek V3: Open Source Option to Watch
Pricing: Free for chatbot; paid for API usageLLMs Supported: DeepSeek MoE
Tests Passed: 3/4
Security: No MFA
Platform Support: Browser only
DeepSeek offers a rare open-source angle—important for developers who need transparency or run on restricted infrastructure. The V3 model’s efficiency and code quality, especially in common programming environments, show promise.
Strength: Open source, free-to-use chatbot; effective at routine programming tasks.
Risk: Inconsistent on obscure programming tasks; lacks the ecosystem and integrations seen with Microsoft or OpenAI offerings.
LLMs to Avoid for Coding (2025)
Not every AI is up to the challenge. Here are several that underperformed or introduce reliability risks for programming in 2025:- DeepSeek R1: Advanced reasoning but poor code-writing, even struggling with regular expressions—a basic programming test.
- GitHub Copilot (VS Code Extension): Despite tight IDE integration, it often produces incorrect code, making it risky to use without extensive manual validation. Not recommended for production software.
- Meta AI & Meta Code Llama: Both general-purpose and code-focused AI from Meta failed most tests, showing inconsistent code quality and missing functionality even in supposedly simple scenarios.
- Claude 3.5 Sonnet: Despite Anthropic’s claims, it failed all but one test. It offers stronger performance for non-coding tasks and research but isn’t suited for programming help as of 2025.
Digging Deeper: What Makes Top Coding AIs Stand Out?
Sourcing and Transparency
A vital improvement across several leading LLMs—particularly Perplexity Pro and Perplexity Free—is robust sourcing. When AI chatbots cite their data or code origins, it allows developers to verify claims, reducing the risk of introducing subtle bugs or vulnerabilities. Microsoft Copilot and Gemini Pro have also begun offering more detailed sourcing, aligning with best practices for responsible AI deployment.Security: More Than a Checkbox
Security features such as MFA are growing but not universal. While Microsoft and Google now mandate MFA across most of their applications (verified by company documentation and recent product updates), Perplexity lags behind, and most open-source or smaller providers have yet to implement advanced login features. For enterprise adoption—especially in sectors handling sensitive or regulated data—this could be a critical concern.Ecosystem Integration
The top performers in 2025 not only answer queries capably but also integrate with the broader developer workflow. Microsoft Copilot’s integration with Visual Studio and GitHub, ChatGPT Plus’ Mac desktop app (but not Windows; a sore point for many in the community), and Perplexity’s cross-LLM capabilities all cater to professional users’ needs.Prompt Throttling and Access Restrictions
A common denominator among free-tier offerings is the paradox of abundance and scarcity: while the technology is incredibly advanced, access often gets throttled during high traffic—downgrading you to less capable models or cutting off sessions. This can pose real roadblocks for those on deadlines or working on intricate debugging.AI in Practice: What’s Realistic in 2025?
Despite marketing hype, even the best AI coding assistants excel most at incremental support rather than greenfield development:- Strengths: Writing functions, fixing syntax errors, debugging, and suggesting refactors. Excellent for learning new frameworks and “unsticking” developers from tricky spots.
- Limits: Large-scale application development or sustained, context-rich programming remains out of reach. No current LLM (including GPT-4o, Gemini Pro 2.5, and Copilot) can consistently architect and implement complete, robust software systems without substantial human oversight—a claim repeatedly validated by multiple independent evaluations.
How to Choose the Right AI Coding Assistant for Your Needs
When selecting an AI to assist with coding, consider the following:- Security Requirements: Is MFA or SSO required by your organization? Are there limitations on browser-only tools?
- Ecosystem Fit: Are you tightly embedded in Microsoft, Google, or open-source ecosystems?
- Budget Sensitivity: Do you need reliability and unlimited access, or can you tolerate prompt throttling and occasional access loss?
- Nature of Work: Routine code snippets? Debugging production code? Heavy research mixed with programming?
- Verification and Traceability: Are cited sources and explainability important for your use case?
Critical Analysis: Trends, Strengths, and Risks on the Horizon
Notable Advancements (2023-2025)
- Accuracy Improvements: Both Copilot and Gemini leapt from “barely usable” to “best-in-class” via model upgrades and sustained investment.
- Multi-Model Platforms: Perplexity’s broad LLM selection is a game-changer, offering resilience to single-model failure and deeper insight when code is validated across engines.
- Open Source Momentum: While not always the most accurate, DeepSeek V3 and similar initiatives signpost a vibrant future for customizable, transparent code bots.
Lingering Risks and Pitfalls
- Security Gaps: Until all tools adopt robust authentication and secure access, production deployments remain risky—especially for regulated industries.
- Hallucination and Overconfidence: No model is immune to occasionally generating believable but defective code. Always lint, test, and code review output.
- Ecosystem Lock-In: As platforms build deeper ties to proprietary dev tools, switching costs and incompatibility concerns may grow.
- Rapid Pace of Change: The speed with which underperforming tools (e.g., Copilot, Gemini) can become leaders in a single upgrade cycle underscores the importance of re-evaluating tooling regularly.
Summary: Your Roadmap to AI Coding Success in 2025
No single AI perfectly serves all coding needs, and sweeping claims of “writing entire apps with one prompt” remain unrealistic. The current crop of leaders—Microsoft Copilot, Perplexity Pro, Gemini Pro 2.5, and the ever-improving Grok—offer impressive depth and accessibility for real-world programming.However, security, access, integration, and ongoing oversight are essential watchwords:
- For enterprise Windows developers: Microsoft Copilot now offers a secure, robust, and free starting point—especially when paired with Microsoft’s broader AI suite.
- For those juggling multiple platforms or needing research alongside coding: Perplexity Pro and its multi-LLM support stand out (albeit with caveats around login security).
- For open-source and independent tinkerers: DeepSeek V3 provides a compelling, transparent alternative that is sure to keep evolving.
- For advanced users with Google infrastructure: Gemini Pro 2.5 has matured into a worthy challenger, so long as you can manage unpredictable costs.
And as the competitive landscape shifts, keep testing, keep validating, and stay engaged: The “best” AI for coding is always evolving, and today’s runner-up could be tomorrow’s champion.
Source: ZDNET The best AI for coding in 2025 (including two new top picks - and what not to use)
