GPT-4o and Claude 3.5 Sonnet are the two models most teams evaluate first when choosing a frontier AI API. Both sit at the top of major benchmarks, both offer generous context windows, and both have mature, production-ready APIs. Yet they differ in meaningful ways -- in pricing structure, latency characteristics, benchmark strengths, and the subjective "feel" of their outputs. This guide puts them side by side with real numbers so you can make an informed decision.

Core Specifications at a Glance

GPT-4o, released by OpenAI in mid-2024 and iteratively updated since, supports a 128K-token context window and accepts text, images, and audio as input. Its API pricing sits at $2.50 per million input tokens and $10.00 per million output tokens as of early 2026. Latency is competitive: most requests return first tokens within 300-600 ms depending on prompt length.

Claude 3.5 Sonnet, released by Anthropic in June 2024 and refreshed in October 2024, offers a 200K-token context window -- 56% larger than GPT-4o. Pricing is $3.00 per million input tokens and $15.00 per million output tokens. While slightly more expensive per token, Sonnet's longer context window means you can fit more information into a single call, which can reduce the total number of API requests your application needs.

Benchmark Performance

On MMLU (Massive Multitask Language Understanding), both models score in the high-80s to low-90s depending on the evaluation variant. GPT-4o typically edges ahead on MMLU-Pro with scores around 72-74%, while Claude 3.5 Sonnet lands at 69-72%. The practical difference is small.

Where the gap widens is coding. On HumanEval, Claude 3.5 Sonnet scores 92.0% compared to GPT-4o's 90.2%. On SWE-bench Verified, a more realistic evaluation that tests multi-file code changes, Sonnet achieved 49.0% vs GPT-4o's 38.6% -- a significant margin. If your primary use case is code generation, code review, or developer tooling, Claude 3.5 Sonnet has a measurable advantage.

For mathematical reasoning, GPT-4o leads on GSM8K (95.8% vs 96.4% depending on evaluation conditions, effectively a tie) and on MATH (76.6% vs 71.1%). If your workload involves heavy quantitative analysis, GPT-4o is the stronger choice.

On graduate-level reasoning benchmarks like GPQA Diamond, Claude 3.5 Sonnet scores 65.0% vs GPT-4o's 53.6%, suggesting Sonnet handles complex, multi-step reasoning tasks more effectively.

You can explore these benchmarks in detail and compare them visually on our leaderboard page.

Pricing and Cost Analysis

For a typical enterprise workload processing 10 million input tokens and 2 million output tokens per day:

  • •GPT-4o: (10M x $2.50 / 1M) + (2M x $10.00 / 1M) = $25.00 + $20.00 = $45.00/day
  • •Claude 3.5 Sonnet: (10M x $3.00 / 1M) + (2M x $15.00 / 1M) = $30.00 + $30.00 = $60.00/day

That puts GPT-4o at roughly 25% cheaper for the same token volume. However, Sonnet's larger context window (200K vs 128K) means you might need fewer calls for tasks involving long documents, which can close the cost gap depending on your architecture. Visit our pricing comparison page to model your specific usage patterns.

Both providers offer batch API pricing at 50% discounts for non-real-time workloads, bringing GPT-4o down to $1.25/$5.00 and Claude 3.5 Sonnet to $1.50/$7.50 per million tokens.

Multimodal Capabilities

GPT-4o natively handles text, images, and audio input within a single API call. This makes it the stronger choice for applications that need to process voice input, analyze images, and generate text responses in one pipeline. OpenAI's vision capabilities are mature and well-documented.

Claude 3.5 Sonnet supports text and image input but does not process audio natively. Its vision capabilities are strong -- Anthropic reports competitive performance on visual question answering benchmarks -- but the lack of audio input means you need a separate speech-to-text step if your application handles voice.

Instruction Following and Output Quality

In subjective evaluations and user preference studies, Claude 3.5 Sonnet consistently receives higher marks for instruction following, particularly with complex multi-step prompts. Sonnet tends to produce more structured, well-formatted outputs and is less prone to ignoring specific formatting requirements in the system prompt.

GPT-4o produces natural, conversational output and handles creative writing tasks with a more varied, less formulaic style. It also supports structured output mode (JSON mode) with strong reliability, which is valuable for applications that parse model responses programmatically.

Anthropic's constitutional AI approach gives Sonnet notably different safety characteristics. It is more conservative in certain edge cases but also more transparent about its reasoning and limitations. For enterprise applications where predictable behavior matters, this can be an advantage.

Context Window and Long-Document Performance

Claude 3.5 Sonnet's 200K-token window is one of its strongest differentiators. In needle-in-a-haystack evaluations, Sonnet maintains high recall even at the far end of its context window, whereas some models degrade significantly beyond 64K tokens. If your application involves analyzing legal contracts, codebases, research papers, or other long documents, Sonnet's context window gives it a practical edge.

GPT-4o's 128K window is still generous by industry standards and sufficient for most applications. Performance within that window is reliable, but for truly massive document analysis, you may need to implement chunking strategies.

API Ecosystem and Developer Experience

OpenAI's API ecosystem is more mature, with a wider range of ancillary features: fine-tuning, assistants API, function calling, file storage, and vector search built into the platform. If you need a one-stop platform with extensive tooling, OpenAI has the edge.

Anthropic's API is leaner but well-designed. Claude supports tool use (function calling), streaming, and system prompts. The developer documentation is excellent, and the API surface is clean and predictable. For teams that prefer simplicity and plan to build their own orchestration layer, Anthropic's approach works well.

When to Choose GPT-4o

  • •Your workload is primarily mathematical or quantitative reasoning
  • •You need native audio input processing
  • •Cost per token is your primary concern and your workload is high-volume
  • •You want a batteries-included API platform with fine-tuning, assistants, and built-in vector search
  • •Your team already has significant OpenAI integration and switching costs are high

When to Choose Claude 3.5 Sonnet

  • •Your primary use case is code generation, code review, or developer tools
  • •You work with long documents that benefit from a 200K context window
  • •Instruction following and output formatting precision are critical
  • •You need strong performance on complex, multi-step reasoning tasks
  • •Your application values predictable, well-structured outputs over creative variation

The Bottom Line

Neither model is universally "better." GPT-4o wins on price, multimodal breadth, and math reasoning. Claude 3.5 Sonnet wins on coding benchmarks, context length, instruction following, and complex reasoning. The right choice depends on your specific use case, volume, and integration requirements.

Use our side-by-side comparison tool to evaluate both models against your exact requirements, or explore our full model directory to see how they stack up against alternatives like Gemini 2.0 Flash, DeepSeek-V3, and Llama 3.