GPT-4o vs Claude 3.7 Sonnet (2026) — API Comparison

Head-to-head comparison

API pricing — GPT-4o is cheaper at scale

GPT-4o costs roughly 17% less on input and 33% less on output compared to Claude 3.7 Sonnet. At 100M tokens/month that gap is meaningful. For budget-sensitive workloads, both providers offer cheaper alternatives: GPT-4o mini and Claude 3.5 Haiku.

At very high volumes, consider GPT-4o's Batch API (50% discount) or Claude's prompt caching (up to 90% discount on repeated context). Both can flip the cost equation depending on your access pattern.

Coding & benchmarks — Claude wins

Claude 3.7 Sonnet scores 49% on SWE-bench Verified — the industry-standard benchmark of real GitHub issues requiring reading code, writing patches, and running tests. GPT-4o sits around 33%. On MMLU (general knowledge), Claude leads 90.1% to GPT-4o's 88.7%.

Claude's extended thinking mode activates deeper multi-step reasoning before responding. Enable it with a budget_tokens parameter — useful for architecture decisions, complex debugging, and algorithm design. GPT-4o has no equivalent (OpenAI's o3 is the reasoning model, at $10/M input).

{`import anthropic

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=16000,
    thinking={"type": "enabled", "budget_tokens": 10000},
    messages=[{"role": "user", "content": "Fix the race condition in this code..."}]
)`}

Speed & latency — roughly equal (extended thinking is slower)

In standard (non-thinking) mode, both models are fast: GPT-4o typically delivers 40–80 tokens/sec, Claude 3.7 Sonnet 60–100 tokens/sec depending on load. Real-world latency depends heavily on prompt size, time of day, and tier.

Claude's extended thinking mode is slower — it generates internal reasoning tokens before the visible response. For latency-sensitive apps (chatbots, autocomplete), use standard mode or Claude 3.5 Haiku. Extended thinking is best reserved for background tasks, complex coding, and batch jobs where accuracy outweighs speed.

Tool calling & structured output — both capable, different APIs

GPT-4o uses JSON schema enforcement for structured output — you pass a JSON schema and the model is guaranteed to return valid JSON matching it. Claude uses tool_use blocks — define a tool with an input schema, and the model calls it with valid arguments.

In practice both approaches produce reliable structured output. GPT-4o's native JSON mode is slightly simpler for extraction tasks. Claude's tool_use is more expressive for multi-turn agentic workflows where the model needs to choose among multiple tools.

{`response_format={
  "type": "json_schema",
  "json_schema": {
    "name": "result",
    "schema": {
      "type": "object",
      "properties": {
        "sentiment": {"type": "string"},
        "score": {"type": "number"}
      }
    }
  }
}`}

{`tools=[{
  "name": "analyze_sentiment",
  "description": "Return sentiment analysis",
  "input_schema": {
    "type": "object",
    "properties": {
      "sentiment": {"type": "string"},
      "score": {"type": "number"}
    }
  }
}]`}

Which to pick for your use case

Check live API status

OpenAI (GPT-4o)

Full history →

Anthropic (Claude)

Full history →

Frequently asked questions