The Numbers That Matter
The AI model wars entered a new phase in late 2025 when Anthropic released Claude Sonnet 4.5, going head-to-head with OpenAI's GPT-4o on every major benchmark. Marketing claims aside, what do the actual numbers tell builders who need to pick a model for production workloads? The answer is more nuanced than either company would like you to believe, and the benchmarks themselves deserve scrutiny.
On MMLU, the Massive Multitask Language Understanding test that has become the standard yardstick for general knowledge, both models hover around 90% accuracy. GPT-4o scores 88.7%, while Claude Sonnet 4.5 edges slightly higher. For practical purposes, this is a statistical tie. The difference matters less than the specific domain you are testing against.
Coding Benchmarks Tell a Different Story
Where the gap widens is in code-related tasks. On HumanEval, Claude 3.5 Sonnet achieved 92% compared to GPT-4o's 90.2%. But the real headline is SWE-bench Verified, a benchmark that measures the ability to resolve actual GitHub issues from real open-source repositories. Claude Sonnet 4.5 scores 77.2% in standard runs and jumps to 82.0% with parallel compute enabled. This is the highest success rate among frontier models for real-world coding tasks as of late 2025.
On mathematical reasoning, Claude Sonnet 4.5 scores 100% on AIME 2025 when allowed to use Python tools and 87% without them. On GPQA Diamond, which tests graduate-level physics, biology, and chemistry questions, it reaches 83.4%. These numbers suggest that Claude's strength is not just in raw knowledge retrieval but in multi-step reasoning chains.
What GPT-4o Does Better
GPT-4o remains the default choice for many production applications, and there are good reasons for that beyond inertia. OpenAI's model handles multimodal inputs. Images, audio, and text. With lower latency than Claude's equivalent offerings. For applications that need real-time voice processing or image understanding integrated into a single API call, GPT-4o's unified architecture provides a smoother developer experience.
OpenAI also maintains a significant ecosystem advantage. With over 2 million developers using the API as of mid-2025, the breadth of community-built tools, fine-tuning recipes, and production case studies creates a gravitational pull that benchmarks alone cannot capture. When your team hits an edge case at 2 AM, the odds of finding a relevant Stack Overflow answer or GitHub discussion are higher with GPT-4o.
Context Windows and Pricing
Pricing is where the decision gets practical. GPT-4o charges $5 per million input tokens and $20 per million output tokens. Claude Sonnet 4.5 comes in at $3 per million input tokens and $15 per million output tokens. A 40% discount on input and 25% on output. For high-volume production workloads processing thousands of requests daily, this price difference compounds quickly. A company processing 10 million tokens per day would save roughly $600 per month switching from GPT-4o to Claude Sonnet 4.5 on input costs alone.
Both models support 128K context windows for standard usage, though Anthropic offers extended context through its Sonnet line reaching higher limits. For applications that need to ingest entire codebases or long legal documents in a single pass, this distinction matters.
The Benchmark Problem Itself
Here is the uncomfortable truth that neither Anthropic nor OpenAI will emphasize: benchmark scores are increasingly unreliable as a predictor of real-world performance. Models can be optimized for specific benchmarks, and the gap between benchmark performance and production behavior is growing. A model that scores 92% on HumanEval might struggle with your specific codebase's patterns, dependencies, and edge cases.
The more useful approach is to evaluate models against your own use case. Run both models through 100 representative prompts from your actual production workload. Measure not just accuracy but latency, consistency across runs, and how gracefully each model handles ambiguous or adversarial inputs. This kind of evaluation costs a few dollars and a few hours but saves months of regret.
What the Market Is Choosing
According to industry analysis, the market is not choosing one model. It is choosing both. Multi-model architectures are becoming the default for production AI systems. Companies route simple queries to cheaper, faster models like GPT-4o mini or Claude Haiku, escalate complex reasoning tasks to Claude Sonnet 4.5, and reserve GPT-4o for multimodal workloads that require image or audio processing.
This routing strategy is not just about cost optimization. Different models have different failure modes. By distributing traffic across providers, teams gain resilience against outages, rate limits, and the inevitable model regressions that come with updates.
Sources and Signals
The benchmark data cited here comes from Anthropic's official release notes for Claude Sonnet 4.5, OpenAI's model documentation, and third-party evaluations published on LM Council and SWE-bench leaderboards. Pricing data reflects published API rates as of late 2025 and does not account for volume discounts or enterprise agreements.
