GPT-4 Turbo vs Claude 3.5 Sonnet: Technical Benchmark Comparison

Key Takeaways

• GPT-4 Turbo scores higher on HumanEval (code) and multi-tool use benchmarks
• Claude 3.5 Sonnet outperforms on GPQA (expert reasoning) and long-form writing quality
• Both models score comparably on MMLU, with <2% difference across subject areas
• Claude 3.5 Sonnet has a 200K token context window vs GPT-4 Turbo’s 128K
• GPT-4 Turbo has faster latency for short completions; Claude is faster for long outputs
• Pricing: GPT-4 Turbo at $10/$30 per M tokens; Claude 3.5 Sonnet at $3/$15 per M tokens

GPT-4 Turbo and Claude 3.5 Sonnet represent the two dominant AI models for enterprise and developer use in 2025. While marketing claims abound, this comparison cuts through the noise with benchmark data, real-world test results, and practical guidance on which model to choose for specific use cases.

Benchmark Overview

Note: Benchmark scores sourced from official Anthropic and OpenAI technical reports, as well as independent evaluations from HELM and Hugging Face Open LLM Leaderboard. Scores may vary slightly based on prompt formatting and evaluation methodology.

MMLU (Massive Multitask Language Understanding)

MMLU evaluates knowledge across 57 academic subjects, from elementary mathematics to advanced professional medicine and law. It’s one of the most widely used benchmarks for measuring general knowledge.

Results Breakdown by Subject Area

Claude 3.5 Sonnet edges out GPT-4 Turbo across most MMLU categories, with the largest gap in social sciences and professional subjects.

HumanEval – Code Generation Performance

HumanEval is the gold standard benchmark for code generation, consisting of 164 Python programming problems with test cases. The score represents the percentage of problems solved correctly on the first attempt (pass@1).

Claude 3.5 Sonnet’s 92% on HumanEval places it among the best models for code generation. In practice, developers working with Claude report several advantages:

• More complete implementations: Claude tends to write full, working implementations rather than leaving TODO comments or placeholder code
• Better error handling: Code generated by Claude more often includes appropriate try/catch blocks and edge case handling
• Superior docstrings and comments: Claude’s code is generally better documented

GPT-4 Turbo at 87.1% is still excellent, and many developers prefer it for:

• Multi-file projects: GPT-4’s context handling works well for larger codebases when combined with tools like GitHub Copilot
• Tool calling and function use in agentic workflows
• Code in less common languages where training data may differ

Reasoning Performance (GPQA Diamond)

GPQA (Graduate-Level Google-Proof Q&A) Diamond is arguably the most demanding reasoning benchmark currently in use. Questions are specifically designed to be resistant to simple web search—they require genuine expert-level reasoning in biology, chemistry, and physics.

Claude 3.5 Sonnet’s 59.4% vs GPT-4 Turbo’s 35.7% represents a substantial gap. In real-world terms, this translates to superior performance on:

• Multi-step logical deduction problems
• Complex causal reasoning tasks
• Scientific hypothesis evaluation
• Legal case analysis requiring nuanced judgment

Creative Writing Quality

Unlike the objective benchmarks above, creative writing quality requires human evaluation. Based on blind evaluations across 200+ writing samples:

Tool Use and Agentic Performance

For developers building AI agents that call external APIs and chain multiple tools together, tool use reliability is critical. The Tau-bench evaluation tests models on realistic multi-step tool use scenarios.

Claude 3.5 Sonnet scores approximately 90% on Tau-bench, outperforming GPT-4 Turbo’s ~78%. In practice, this means Claude is significantly less likely to:

• Call tools with incorrect parameter formats
• Hallucinate tool capabilities that don’t exist
• Get stuck in tool-use loops
• Lose context about previous tool results in long chains

Speed and Latency

Pricing Comparison

Claude 3.5 Sonnet is significantly more cost-effective—approximately 3x cheaper on input tokens and 2x cheaper on output. Combined with its performance advantages, Claude 3.5 Sonnet offers a better value proposition for most production applications.

Which Model Should You Choose?

Choose GPT-4 Turbo if:

• You’re already deeply integrated into the OpenAI ecosystem
• Your application requires specific OpenAI features (DALL-E integration, GPTs, Assistants API)
• You need the lowest possible time-to-first-token for interactive applications
• Your mathematical computation tasks are performance-critical

Choose Claude 3.5 Sonnet if:

• Code generation quality and completeness are important
• You’re processing long documents (200K context window)
• You’re building complex agentic workflows with tool chaining
• Cost optimization matters at scale
• You need superior reasoning for complex analysis tasks
• Creative writing or long-form content quality is a priority

Frequently Asked Questions

Is Claude 3.5 Sonnet better than GPT-4 Turbo overall?

On most current benchmarks, Claude 3.5 Sonnet outperforms GPT-4 Turbo, particularly on code generation, reasoning, and tool use. The performance advantage is coupled with significantly lower pricing, making Claude 3.5 Sonnet the better choice for most new projects in 2025.

Which model is better for coding?

Claude 3.5 Sonnet consistently performs better on code generation benchmarks, including HumanEval (92% vs 87%). For complex, multi-file codebases and agentic coding tasks, Claude’s advantage is particularly pronounced.

How does GPT-4 Turbo compare to Claude on long documents?

Claude 3.5 Sonnet has a 200K token context window (vs GPT-4 Turbo’s 128K) and maintains better accuracy across its full context length. For tasks requiring analysis of very long documents, Claude is the clear choice.

Which model hallucinates less?

Both models have similar hallucination rates on factual questions, but Claude 3.5 Sonnet tends to express uncertainty more appropriately and is less likely to confidently state incorrect information.

Can I switch between GPT-4 Turbo and Claude in my application?

Yes. If you use an abstraction layer like LangChain or LiteLLM, switching between models requires minimal code changes. Many production applications implement model routing, using different models for different task types.