5 Best AI assistants in 2025 and how to choose the right one

This analysis shares what the Pieces team has learned from extensive hands-on use across various coding, documentation, and integration tasks. We're not here to crown a winner, but to provide fellow developers with practical insights based on hands-on usage rather than marketing claims.

This analysis reflects our team's experience through mid-2025. Given the rapid pace of development in AI tools, capabilities and performance characteristics change frequently. Hence when you might read it, it might not be up-to-date, but we hope this information will still be be useful and we work on updating our materials to the best of our ability.  

Our testing approach

We evaluated each assistant through the lens of real development work:

• Daily development tasks: Code generation, debugging assistance, documentation writing, and technical research across multiple programming languages, frameworks and topics.

• Integration scenarios: How well each assistant works within existing development environments, from IDEs to terminal workflows to browser-based research.

• Context understanding: How effectively each tool maintains understanding of project structure, coding standards, and ongoing work across multiple sessions.

• Accuracy in technical domains: Reliability when dealing with complex technical concepts, API documentation, and domain-specific knowledge.

• Workflow impact: Whether the assistant enhances or disrupts natural development patterns, and how much overhead is required to get value from the tool.

  
  

The bigger picture

What's interesting from our perspective as tool builders is how these different approaches reflect fundamental questions about AI assistance:

Should AI assistants be general-purpose utilities that can handle any request, or specialized tools that deeply understand specific domains and users?

Is the convenience of cloud-scale processing worth the privacy and dependency trade-offs, or do local, user-controlled systems provide better long-term value?

How much context and personalization do developers actually want from their AI tools?

We don't think there's a universal answer to these questions. Different developers, teams, and organizations will reasonably make different choices based on their priorities, constraints, and values.

Our recommendation

Rather than picking a single "best" assistant, we recommend understanding the architectural trade-offs and choosing the approach that aligns with your development philosophy and practical constraints.

Many developers benefit from using different assistants for different types of tasks, perhaps ChatGPT for quick questions, Claude for important analysis, and a context-aware tool like Pieces for ongoing project work.

The key is understanding what each tool is optimized for and using them accordingly, rather than expecting any single assistant to excel at everything.

TLDR

OpenAI 

For many teams, OpenAI’s ChatGPT has become the default assistant to try and for good reason. It was the breakout name that introduced generative AI to the mainstream, and today remains a versatile, widely adopted tool with support for a variety of use cases.

Whether you're coding, documenting, or brainstorming, GPT-4o (via ChatGPT) typically delivers usable results immediately. Its plugin and integration ecosystem is expansive, with connectors for Zapier, Google Docs, Slack, and more, making it a modular and extensible solution for many workflows.

But that general-purpose flexibility comes with trade-offs. OpenAI’s assistant often operates like a fast-thinking generalist: capable, but lacking deep continuity. 

It struggles with long-term memory between sessions and can't retain project-specific details like preferred libraries, logic chains, or prior decisions without repeated prompting. This can be a real tax on productivity in longer, iterative work.

Pricing starts at $20/month for ChatGPT Plus with GPT-4o, while API access is metered by usage. For heavier developer teams, these costs can scale quickly.

“ChatGPT felt like the most well-rounded assistant… it explained problems like a real colleague would.” – Reddit user on r/learnprogramming

Anthropic

Anthropic's Claude, now in version 4, is built on a different foundation: safety, interpretability, and structured reasoning above all else. This design principle gives Claude a distinct personality, cautious and deliberate, but highly capable in complex, high-context tasks.

Claude is especially effective for work involving long documents, technical specs, or compliance-heavy tasks, especially after customization. Its massive 200K+ token context window allows it to handle entire codebases, multi-page PDFs, and more, producing high-quality summaries and insight-rich responses with built-in rationale.

Developers appreciate the iterative, code-aware nature of Claude’s outputs, especially when used through the command-line-driven Claude Code experience. 

One developer wrote, 

“Close the editor. Open Claude. Your job is now to manage, review, corral, and improve a relentless junior dev working on six PRs in parallel.”

However, that same carefulness can feel limiting. Claude often refuses ambiguous prompts, erring on the side of caution – a strength in regulated industries, but a bottleneck during creative sprints or fast prototyping. It also lacks the extensibility and ecosystem breadth of tools like ChatGPT or Gemini.

Claude access is available via API or web, with the free Sonnet tier and Opus (its premium model) priced at around $20/month.

Pieces 

Unlike OpenAI or Anthropic, Pieces is not an LLM provider. Instead, it’s a powerful developer tool that enhances and personalizes your experience with any LLM you choose including models from OpenAI, Anthropic, Google, Mistral, Meta, or local models like LLaMA and Mamba.

This distinction is critical: when you pay for Pieces, you're not locked into a single provider like Claude or GPT. You can bring your own key, switch providers, or test across models, all without having to choose one ecosystem over another.

Unlike cloud-based assistants, Pieces runs locally by default, though cloud opt-in is available, giving it direct access to your workflow without sending any data off-device.

And it gets better: that local-first architecture means your code snippets, terminal commands, browser activity, and communication history (including Slack, Google Chat, or Teams), meeting notes, GitHub PRs, and other developer materials are continuously indexed and remembered. No data leaves your machine unless you choose to sync, making Pieces a uniquely private, persistent memory layer for your development environment.

This approach gives Pieces access to details that cloud LLMs often miss: the real structure of your repo, your naming conventions, and that utility function you wrote six months ago but forgot about. As a result, it excels at debugging recurring issues, surfacing project-specific knowledge, and accelerating onboarding for new developers joining a shared codebase.

Though newer to the market than some of the bigger players, Pieces has gained serious traction in 2025, especially among developers. Our team recently closed a Series A funding round and continues to grow rapidly in both product velocity and adoption across engineering teams.

On Product Hunt, Pieces holds a 4.8/5 rating, with developers praising it for Long-term memory to deeply personalize interactions with any of the LLMs. 

One full-stack developer shared:

“I added it to the dock on my Mac next to VS Code... it’s completely transformed how I debug and reuse code. It remembers everything I forget.”

Pieces is currently free during early access, with premium tiers on the roadmap as features expand.

Pieces won’t replace your cloud assistant but it will remember what they forget. If you’re looking for an AI teammate that actually understands your environment, this might be the most underrated tool in your stack. Think of it as your second brain for code, private, contextual, and always available.

Microsoft Copilot

Where Gemini embeds into Google, Microsoft Copilot burrows deep into the DNA of Office 365. It lives inside Word, PowerPoint, Excel, Outlook, and increasingly, GitHub, Azure, and even Teams.

If your day blends code and compliance, or documentation and dashboards, Copilot offers frictionless assistance. It can write SQL queries from English prompts inside Excel, summarize meetings from Teams transcripts, and generate polished slide decks from technical reports.

The strength of Copilot is also its tightest constraint: it thrives only within the Microsoft universe. Standalone use is limited, and the assistant often struggles with open-ended reasoning tasks that ChatGPT or Claude handle more flexibly.

Copilot is bundled with various Microsoft 365 business plans. GitHub Copilot (its developer sibling) is priced separately, starting at $10/month.

For enterprise teams who live in Outlook, Visual Studio, and SharePoint, Copilot delivers integrated, contextual help without the fluff. Outside that world? You may want to look elsewhere.

GitHub Copilot 

When GitHub Copilot launched in 2021, it changed everything. Now in 2025, it remains the most widely adopted code assistant for day-to-day software development.

Copilot doesn’t chat much. Instead, it quietly completes your thoughts as you type, suggesting code inline across dozens of languages. Its new “Copilot Workspace” experience has made it more than autocomplete, it now drafts tests, builds PRs, and explains code context on demand.

The assistant performs best in familiar environments, VS Code, JetBrains IDEs (similarly to Pieces) and benefits from GitHub’s tight integration. It understands repo structure, common project layouts, and even your commit history. 

Speaking of which, you could maximize by enabling long term memory to save all your work history (or go the MCP way). 

Still, Copilot lacks deeper planning capabilities. If you ask it to generate a 10-file application with cross-cutting concerns, it quickly hits its limits. Its design also favors “write once” over memory, it doesn’t learn from your past sessions the way a context-aware tool like Pieces might.

Pricing is $10/month for individuals or $19/month for business seats. Copilot for CLI and Chat are bundled in the paid plans.

Still the most ergonomic assistant for getting code written faster. But for architectural thinking, documentation, or long-term memory, you’ll want to supplement it with something smarter.

Google Gemini 

Gemini isn’t trying to replace your IDE or become your programming partner. Instead, it thrives by living where you already work, inside Google Docs, Gmail, Sheets, and increasingly, across the broader Google Workspace. 

If your workflow is already nestled in Google’s cloud, Gemini can feel less like a chatbot and more like a smart coworker, always a tab away.

It excels in multimodal tasks. Gemini is uniquely capable of interpreting diagrams, code snippets, UI screenshots, and even math-heavy PDFs, often in the same session. It’s not just trained to answer; it’s trained to see.

However, the assistant’s strength is also its weakness. Gemini is deeply tied to Google’s data ecosystem.

That means if you’re using alternative cloud storage or non-Google IDEs, the magic dims considerably. And while its privacy policies are robust on paper, many developers still raise eyebrows over how much data context it can access in real time.

The Gemini Advanced tier (with Gemini 1.5 Pro) is available via Google One for $20/month, bundled with 2TB of Drive storage. API pricing is comparable to other cloud LLMs.

For developers and teams embedded in Google’s ecosystem, Gemini offers impressive synergy. But if you're building outside that orbit, its utility becomes more limited.

DeepSeek 

DeepSeek is a rising name in the AI assistant space, quickly attracting attention from developers for its code-centric design, strong multilingual capabilities, and performance on algorithmic tasks. Built by a Chinese research team with an open-source ethos, DeepSeek takes a different approach than generalized assistants from OpenAI or Anthropic, focusing specifically on coding fluency and logic-heavy workflows.

Where it stands out is in competitive programming-style performance. Whether you're debugging deeply nested logic, solving algorithmic puzzles, or working across languages, DeepSeek responds with speed, clarity, and accuracy. It's especially appealing for devs outside primarily English-speaking markets, thanks to training on both English and multilingual corpora.

That said, the product still feels early-stage. The interface is minimal, integrations are limited, and there’s little surrounding infrastructure compared to the full ecosystems offered by OpenAI or Anthropic. 

You don’t get features like long-term memory, IDE plugins, or workflow-aware assistance, at least not yet. But what you do get is a lean, high-performing code assistant that does one thing extremely well.

One area that’s sparked discussion among developers is privacy. DeepSeek’s privacy policy indicates that user data is stored on servers in China, which has raised concerns for teams dealing with sensitive code or operating under strict compliance policies. While this won’t matter for all users, it’s something many teams consider when choosing tools that process proprietary or personal information.

Overall, DeepSeek is a powerful, focused option, especially for developers who prioritize multilingual support, raw model performance, and an open-access approach. It may not yet rival the broader toolchains of ChatGPT or Claude, but it's carving out a strong niche with clear momentum.

Cursor

Cursor has quickly built a strong reputation among developers who want AI help exactly where they work, inside their code editor. 

Built on top of VS Code, Cursor enhances the development experience without disrupting it, making it a favorite for engineers who prefer an AI assistant that feels like an invisible copilot, not a separate chatbot.

What makes Cursor stand out is its tight integration with the development workflow (we also love Cursor at Pieces). You can explain a bug in plain English, highlight a function for refactoring, or generate unit tests, all without leaving your current file. Changes are applied in place via an intuitive command palette, avoiding the typical copy-paste loop most AI tools require.

Cursor’s context-aware suggestions go beyond standard autocomplete. It doesn’t just finish lines, it can anticipate intent, refactor intelligently, and even make repo-wide changes. Thanks to long context windows and local file indexing, it understands relationships across files, which is a major advantage for multi-file refactors, legacy logic exploration, or structural improvements.

That said, Cursor isn’t flawless. While it deeply understands your code during a session, it doesn’t retain memory between sessions. 

It also depends on external LLMs, meaning it inherits some of the cloud model limitations around reliability and token constraints. It’s incredibly useful in the moment, but it’s not yet a persistent teammate.

There’s also a learning curve. Features like Composer Mode (for stepwise refactors) and the .cursorrules config file (for customizing AI behavior) offer deep power, but take time to master. As one user put it:

“It’s the power-user option. The UI can feel overwhelming at first, but once you adapt, it’s indispensable.”

That sentiment is echoed widely. Developers who invest the time to tailor Cursor to their workflow consistently report meaningful productivity gains, and many have made it a default part of their daily stack.

Cursor offers a free tier with usage caps, and premium plans starting at around $20/month for extended usage and model access.

→ Read here on how to use Pieces + Cursor to bring your Long-Term Memory into your dev workflow, or watch this video.

Other tools on the market

There are also other AI assistants that focus on more specific parts of your workflow:

• Notion AI is great for summarizing notes, drafting content, and managing documents within your knowledge base, perfect for teams already using Notion as their workspace.

• Lovable, the assistant on everyone’s lips lately, excels at lightweight personal tasks, organizing thoughts, generating messages, or crafting daily to-dos with a fun, conversational touch. By the way, we use Lovable as well when it comes to writing documentation. 

• DALL·E, from OpenAI, is purpose-built for visual generation. It’s best for turning rough ideas into concept art, UI mockups, or creative illustrations quickly.

Some users prefer to go broader, using Model Context Protocol (MCP) to connect multiple LLMs and tools into a unified, personalized system.

Most of these setups are discovered through communities, dev forums, or by staying active on LinkedIn.

Pieces users often use MCP to build assistants that adapt to their dev environment, pulling from IDEs, terminals, docs, and chats, to create an assistant that actually understands their workflow.

Choosing the right approach

While new AI tools continue to launch every month, the real decision comes down to what you value most in an assistant, not just the model behind it, but the tooling, integrations, and workflow alignment it offers.

For teams seeking broad capabilities  and robust ecosystem support (and we know the AI adoption can be tough then), OpenAI’s ChatGPT remains the most flexible option. It handles a wide range of tasks and integrates smoothly with tools like Zapier, Slack, and Google Workspace, making it a strong choice for those who want a single assistant that fits nearly everywhere.

When precision and interpretability are paramount, Anthropic’s Claude excels. Its rationale-driven responses and resistance to hallucination make it particularly valuable for long-form analysis, research, and technically sensitive work.

For those focused on contextual intelligence and deep personalization, Pieces stands apart. As a local-first tool that enhances any LLM whether from OpenAI, Anthropic, Google, or open-source models – Pieces integrates directly with your development workflow. 

It indexes your code, browser, terminal, communication history (Slack, Teams, Google Chat), GitHub PRs, and more, creating a private, persistent memory layer. 

You’re not locked into any one model, and you gain the flexibility to run models locally for sensitive or offline use.

And for teams already embedded in the Google or Microsoft ecosystems, assistants like Gemini or GitHub Copilot offer native compatibility that may make them the easiest to adopt.

In the end, no single assistant wins on every front. The smartest approach is to understand your team’s real needs and build a mix of tools that align with your workflow, not the other way around.

Start by reading real-world feedback, compare performance for your use case, and weigh pricing against actual effectiveness. 

No single assistant wins across the board, so learn how to mix and match based on what matters most for your workflow.