Claude vs. GPT-4: Performance Benchmarks

Claude vs. GPT-4: Performance Benchmarks

Quick answer: Claude 4 models score higher on real-world coding benchmarks like SWE-bench. GPT-4 stays strong for broad tasks. Choose Claude Opus 4 for heavy coding and long runs, Sonnet 4 if you want a great free option, and GPT-4 for wide availability and general workflows.

What this article covers

This guide compares benchmark scores, speed, accuracy, and practical use. It pulls data from public model posts and hands-on reporting so you can pick the right model for coding, writing, or research.

Why these tests matter

Benchmarks and real tests show how models handle real tasks. SWE-bench measures real coding problems. Speed tests show how fast a model replies.

Reasoning checks whether a model can finish many steps correctly. We use these signals to judge value for teams and builders.

What we looked at

• Anthropic's Claude 4 launch notes and model pages
• independent benchmark summaries
• developer reports and testing writeups
• speed and quality observations
• reasoning budget details

Benchmark snapshot (coding tasks)

Below is a short table of public scores on SWE-bench and similar coding tests. Numbers come from Anthropic and independent posts.

Model	Representative SWE-bench score	Source
Claude Sonnet 4	72.7%	DataCamp summary
Claude Opus 4	72.5% (high-compute up to 79.4%)	Anthropic
Claude 3.7 Sonnet	~62.3%	WandB report
OpenAI GPT-4.1 (example)	~54.6%	Anthropic comparatives and public summaries
Google Gemini 2.5 Pro	63.2%	Medium / model cards

Speed and latency

Simple answers come fast. Reports say Claude returns short responses in seconds and handles longer prompts well, though it can be slightly slower in peak load. See the practical notes in the Claude review and model pages.

• Short replies: usually under a few seconds on low load.
• Long or complex tasks: take longer. Claude may trade speed for coherent, high-quality output.

Accuracy, reasoning, and long runs

Claude 4, especially Opus 4, is built to keep working on long tasks. Anthropic reports sustained performance on long runs and many steps. Claude also offers a manual "reasoning budget" that lets you favor deeper step-by-step thinking at the cost of time and tokens (WandB analysis).

Key trade-offs:

• More reasoning budget = better correctness on hard coding problems but slower replies.
• Opus models handle large continuous tasks better than Sonnet models.
• Full reasoning traces improve transparency during debugging.

How that compares to GPT-4

GPT-4 performs well across many categories. Public comparisons show Claude 4 leading on specific coding benchmarks. But GPT-4 remains a solid generalist and often integrates widely via APIs and tools. Choose based on the task, not just a single score.

Use-case guide: pick by role

• Software teams: Use Claude Opus 4 for heavy automated refactors, test generation, and long multi-step jobs. The high SWE-bench scores matter here.
• Indie devs and startups: Sonnet 4 gives strong coding help with a free tier. It balances quality and cost.
• Content teams and marketers: Both Claude and GPT-4 write well. Claude is praised for clear, grammatical output (review).
• Researchers: If you need long reasoning chains and traceability, Claude's reasoning features are useful (WandB).

Cost and access

Claude Sonnet 4 is noted as a competitive free-tier option in public reporting, which makes high quality more accessible (DataCamp). Opus models target paid, high-compute use. Compare price per token and expected inference time before you commit.

Practical tests to run for your team

Try these quick checks. They take an hour and show real-world fit.

1. Run the same coding task on each model: ask for a refactor, tests, and a failing edge case fix. Compare correctness and patch quality.
2. Measure latency on typical prompts. Time short answers and a long multi-step job.
3. Check cost: tokens used and price per call for a standard workload.
4. Test traceability: ask for a step-by-step reasoning trace and compare usefulness for debugging.

Decision checklist

• Need best coding accuracy and long-run work? Consider Claude Opus 4.
• Want a strong free option for everyday tasks? Try Claude Sonnet 4.
• Need wide tool and API support? GPT-4 may be preferable.
• Balance speed vs accuracy: lower reasoning budget for speed, higher for correctness.

Short FAQ

Does a higher SWE-bench score always mean better results? Not always. It shows strength on coding tests, but integration and prompt design matter.

Are Claude models slow? They are fast on short replies. Long or deep-reasoning tasks take longer by design (review).

Where to learn more? Read Anthropic's announcement at Anthropic and independent analyses like DataCamp, Medium, and hands-on notes at WandB.

Sources

• Anthropic: Introducing Claude 4
• DataCamp: Claude 4 tests and benchmarks
• Medium: Claude 4 in software engineering
• Claude review: speed and accuracy notes
• WandB: reasoning budget and cost analysis
• eWeek: Claude 3.5 and model variants

Bottom line: Claude 4 leads on coding benchmarks and long-run reasoning, while GPT-4 is a strong general-purpose model. Your choice should depend on the tasks you run, the speed you need, and your budget.