Deep Research with GenAI: Closed vs. Open Source Solution Comparison

Written By Clayton Chancey

Major players and open-source alternatives are competing to provide the most efficient and cost-effective tools for in-depth information gathering. In this case study, we compare three distinct approaches to AI-powered deep research: Claude’s new “Research” feature, ChatGPT’s Deep Research feature, and an open-source alternative built with Goose and several purpose-built MCP servers.

This side-by-side comparison reveals surprising insights about speed, thoroughness, and cost-effectiveness that could significantly impact how researchers, content creators, and knowledge workers approach AI-assisted research tasks.

The Contenders

1. Claude Research by Anthropic

Claude Research is Anthropic's newest feature, released in April 2025 to Claude Max subscribers . It's designed to conduct multiple web searches and generate detailed answers as a competitor to similar "deep research" agents from OpenAI and Google TechCrunch. The service is available to subscribers on Anthropic's premium plans.

  • Subscription cost: $125 per month (Claude Max)

  • Availability: Currently available to Max, Team, and Enterprise subscribers in the US, Japan, and Brazil, with Pro access coming soon

  • Speed: Typically completes research in under a minute

  • Technology: Leverages Claude's web search capabilities rather than a custom model

2. ChatGPT Deep Research by OpenAI

OpenAI's Deep Research feature launched in February 2025 and was initially exclusive to their $200/month ChatGPT Pro subscribers but has since expanded to other paid tiers .

  • Subscription cost: $200 per month (ChatGPT Pro)

  • Query limits: Pro users get 120 deep research queries per month; Plus users get 10 queries monthly

  • Speed: Takes approximately 5-30 minutes per research task, depending on complexity

  • Technology: Powered by OpenAI's o3 reasoning model, specifically trained through reinforcement learning

3. Open Source Alternative

The open source approach demonstrated in the video combines several freely available tools with API-based access to powerful language models. This DIY approach leverages a few components: Goose (An open-source AI agent framework by Block (formerly Square) that works with any LLM and integrates with MCP servers), Sequential thinking (a tool that helps structure complex reasoning tasks into logical steps), Brave Web Search (Provides web search capability through an affordable API), FireCrawl (Converts website content into LLM-friendly formats), OpenRouter (Allows access to various AI models through a unified API), and GPT-4.1 (OpenAI's latest API-only model used in the demonstration)

  • Cost: Approximately $0.27 per search , with token costs varying by model used

  • Speed: Faster than ChatGPT Deep Research but slightly slower than Claude Research

  • Technology: Customizable open-source components that can be adapted to specific needs

Performance Comparison

Our testing revealed significant differences in how these three approaches handle the same complex query about achieving flow states during remote work with frequent context switching.

  • Speed

    • Claude Research was by far the fastest, completing the research in seconds rather than minutes.

    • ChatGPT Deep Research took the longest at 11+ minutes to generate results.

    • Our open-source alternative fell somewhere in between, demonstrating good speed while maintaining reasonable quality.

  • Thoroughness

    • ChatGPT Deep Research produced the most comprehensive results, with the longest and most detailed report. It reviewed 26 sources and created an extensive analysis.

    • Claude Research was fast but less thorough.

    • Our open-source solution provided good depth with fewer sources but was comparable in usefulness to Claude's results.

  • Cost

    • This is where the open-source approach truly shines. At approximately $0.27 per search , it's dramatically cheaper than the monthly subscriptions required for the proprietary solutions.

Implications for Different User Types

Individual Researchers

Individual researchers with limited budgets might find the open-source approach most appealing. The pay-as-you-go model means occasional deep research can be conducted for pennies rather than requiring a substantial monthly subscription.

Professional Teams

Teams with consistent research needs might benefit from either approach:

  • Claude or ChatGPT subscriptions offer predictable costs and ready-to-use interfaces

  • Open-source solutions require more setup but offer significant cost savings at scale

Developers

Developers building research-intensive applications will likely prefer the open-source approach, which offers greater customization and integration possibilities without the constraints of proprietary platforms.

Conclusion

This comparison reveals a fascinating state of the AI research landscape in 2025. While the major AI companies offer polished, ready-to-use research tools through expensive subscription models, open-source alternatives are quickly closing the gap in terms of capabilities while maintaining a dramatic cost advantage.

The choice between these approaches ultimately comes down to the technology adoption framework mentioned in the video: the balance between power/utility and ease of use. Closed-source solutions offer simplicity and integration, while open-source approaches provide flexibility and cost-effectiveness.

As these technologies continue to evolve, we can expect the gap to narrow further, potentially forcing proprietary services to reconsider their pricing models in the face of capable open-source competition. For now, knowledge workers have more options than ever when it comes to leveraging AI for deep research tasks.

Clayton Chancey
Previous

Agentic AI for Sales Workflows
Next

Case Study: AI Transcript Analysis