Claude vs OpenAI GPT for Building AI Agents: A Developer's Complete Comparison

I've built AI agents with both Claude and GPT. Early on, I treated them as interchangeable. They're not.

The difference isn't obvious in benchmarks or demo videos. It shows up in production—when your agent needs to make the same API call reliably, handle a 200k token document without losing context, or run for hours without hallucinating.

Here's what I've learned from shipping agents on both platforms.

The Real Difference: Tool Use Reliability

This is where the gap matters most.

Claude more reliably calls the correct CRM API rather than attempting database queries. It's not flashy, but it's critical. When your agent needs to look up customer data, you don't want it guessing about which tool to use.

When calculations are required, Claude consistently uses calculator tools rather than computing in text. The model reasons more carefully about which tool matches each task, resulting in fewer incorrect API calls.

GPT-5 handles broader tool orchestration better. GPT-5 excels in understanding context, memory usage, and complex tool orchestration. If you're building an agent that coordinates multiple systems simultaneously, GPT-5 has the edge.

The practical takeaway: Claude for precision, GPT for breadth.

Context Windows: Where Claude Wins

Claude Sonnet 4 and 4.5 support a 1-million token context window. This extended context window allows you to process much larger documents, maintain longer conversations, and work with more extensive codebases.

That's not theoretical. I've built agents that analyze entire repositories, process hundred-page contracts, and maintain state across thousands of tool calls—all in a single session without document segmentation.

ChatGPT (GPT-5.1) offers the largest context window at 400,000 tokens and strongest mathematical reasoning, scoring 94.6% on AIME 2025 benchmarks. Claude Opus 4.1 excels in content creation and coding tasks with superior code generation capabilities and a 200,000 token context window.

For agents handling long-running tasks or massive knowledge bases, Claude's context management is the deciding factor.

Pricing: The Landscape Shifted

A year ago, Claude was the expensive choice. That's no longer true.

On November 24, 2025, Anthropic released Claude Opus 4.5, delivering a massive price reduction alongside performance gains (80.9% on SWE-bench Verified). The newer, smarter model is 66% cheaper than its predecessor.

Current pricing (January 2026):

Claude Opus 4.5: $5 input / $25 output per million tokens
GPT-4o: $2.50 per million input tokens and $10 per million output tokens

For high-volume agents, GPT is still cheaper. For capability-per-dollar, Claude Opus 4.5 is competitive with mid-tier models while delivering frontier performance.

Claude includes cost-saving features that reduce spending at scale. Prompt caching cuts costs by up to 90% for repeated queries. Batch processing can save up to 50% on non-urgent tasks.

Tool Use Architecture: Claude's MCP Advantage

I've mentioned MCP (Model Context Protocol) before—it's the infrastructure that lets Claude connect to hundreds of tools without bloating your context window.

Tool Search Tool allows Claude to work with hundreds or even thousands of tools without loading all their definitions into the context window upfront.

Programmatic Tool Calling enables Claude to orchestrate tools through code rather than through individual API round-trips. Instead of Claude requesting tools one at a time with each result being returned to its context, Claude writes code that calls multiple tools, processes their outputs, and controls what information actually enters its context window. Claude excels at writing code and by letting it express orchestration logic in Python rather than through natural language tool invocations, you get more reliable, precise control flow.

GPT-5 has comparable capabilities but through a different architecture. If you're already invested in the Claude ecosystem or need MCP-specific integrations, Claude has the advantage.

Agent Memory and State Management

This is where production agents live or die.

The memory tool enables Claude to store and consult information outside the context window through a file-based system. Claude can create, read, update, and delete files in a dedicated memory directory stored in your infrastructure that persists across conversations. This allows agents to build up knowledge bases over time, maintain project state across sessions, and reference previous learnings without having to keep everything in context.

Context editing automatically clears stale tool calls and results from within the context window when approaching token limits. As your agent executes tasks and accumulates tool results, context editing removes stale content while preserving the conversation flow, effectively extending how long agents can run without manual intervention.

GPT-5 doesn't have equivalent built-in memory management. You'll implement it yourself or use a wrapper layer. That's extra work, but it's doable.

Real-World Performance: What Actually Matters

Benchmarks are useful. Production is what counts.

Overall, GPT-5 won for Twitter repurposing but lagged slightly behind Claude Sonnet 4 on LinkedIn post quality. Claude Sonnet 4 produces concise, well-structured LinkedIn posts with strong hooks—better for professional social media content.

Claude Sonnet 4.5 is the undisputed king of coding AI. It achieved 0% error rate on Replit's internal benchmark, demonstrating unprecedented reliability for production code.

For coding agents specifically, Claude has a clear edge. For general-purpose orchestration, GPT-5 is stronger.

When to Use Each

Use Claude if:

You're building agents that need to process large documents or codebases
Tool reliability and precision matter more than breadth
You want built-in memory and context management
You're already using Claude Code or MCP
You need to run long-duration agents without hitting context limits

Use GPT if:

You need fast, multi-tool orchestration across diverse platforms
Cost per token is the primary constraint
Your agents need strong mathematical or reasoning-heavy tasks
You want broader ecosystem integration and plugin support
You're already invested in the OpenAI platform

The Hybrid Approach

I don't use just one anymore.

For production AI agents, I route based on the task:

Claude Opus 4.5 for document analysis, long-context reasoning, and code generation
GPT-5 for multi-step orchestration and coordination
Claude Sonnet or GPT-4o mini for high-volume, low-complexity tasks

This requires managing multiple API keys and error handling, but the performance-per-dollar and reliability gains justify it.

Building Reliable Agents on Either Platform

The foundation matters less than the architecture. Whether you choose Claude or GPT, building reliable AI tools requires:

Structured output - Never parse free-form text from your model. Use schemas and validation.
Error handling - Assume your model will fail. Build retry logic and fallbacks.
Cost monitoring - Track token usage per agent per day. Optimize ruthlessly.
Human oversight - Escalate decisions that matter. Don't let agents run unsupervised.

The model you pick is important. The system you build around it matters more.

The Bottom Line

With a few exceptions, Anthropic and OpenAI's flagship models are essentially at parity. That means to usefully compare them, we need to focus less on under-the-hood power and more on the features and specialized use cases that make each app unique.

Claude gives you precision, context, and built-in agent infrastructure. GPT gives you breadth, ecosystem, and lower per-token costs. Both can power production agents. Your choice depends on what your agents actually need to do.

Start with the one that fits your immediate constraints. You'll likely end up using both.

What's your experience? Are you building with Claude, GPT, or both? Get in touch and let me know what's working in your production agents.