Practical comparison of Claude Sonnet 4.6 vs GPT-4o for developers: pricing, context window, coding, tool use, and when to use each. With Python examples.
Claude Sonnet 4.6 and GPT-4o are the two most-used mid-tier LLM APIs in 2026. Here's what actually differs, with numbers and code.
Quick comparison
Feature
Claude Sonnet 4.6
GPT-4o
Provider
Anthropic
OpenAI
Input price
$3 / M tokens
$2.50 / M tokens
Output price
$15 / M tokens
$10 / M tokens
Cached input price
$0.30 / M tokens (native)
No native caching
Context window
200K tokens
128K tokens
Tool / function calling
Yes (tools + tool_use)
Yes (tools + tool_calls)
Vision / image input
Yes (URL or base64)
Yes (URL or base64)
Streaming
Yes (SSE)
Yes (SSE)
Batch API
Yes (50% discount)
Yes (50% discount)
JSON mode
Via tool use or prompt
Native JSON mode flag
Audio input
No
Yes (GPT-4o Audio)
Python: same task, both APIs
# Claude Sonnet 4.6
import anthropic
client = anthropic.Anthropic() # ANTHROPIC_API_KEY
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system="You are an expert code reviewer.",
messages=[{"role": "user", "content": "Review this Python function for bugs:
def divide(a, b):
return a / b"}]
)
print(response.content[0].text)
# GPT-4o (same task)
from openai import OpenAI
client = OpenAI() # OPENAI_API_KEY
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are an expert code reviewer."},
{"role": "user", "content": "Review this Python function for bugs:
def divide(a, b):
return a / b"}
]
)
print(response.choices[0].message.content)
Prompt caching — Claude's biggest cost advantage
# Claude: cache a long system prompt (saves 90% on repeated calls)
import anthropic
client = anthropic.Anthropic()
LONG_SYSTEM = "You are a code review assistant with deep expertise in..." * 200 # ~4K tokens
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=[{
"type": "text",
"text": LONG_SYSTEM,
"cache_control": {"type": "ephemeral"} # ← marks this block for caching
}],
messages=[{"role": "user", "content": "Review this function: def add(a,b): return a+b"}]
)
# First call: normal price. Subsequent calls: 90% cheaper on the cached block.
# cache_read_input_tokens in response.usage tells you how many tokens were served from cache.
print(response.usage)
GPT-4o has no equivalent native caching mechanism. For a chatbot with a 2,000-token system prompt making 1M calls/month, Claude's caching saves roughly $5,400/month vs paying full price every time.
Context window: practical implications
Task
Sonnet 4.6 (200K)
GPT-4o (128K)
Process a 100-page PDF
Fits (≈75K tokens)
Fits (≈75K tokens)
Analyze a 40K-line codebase
Fits (≈120K tokens)
Borderline — may need chunking
Process a 60K-line codebase
Fits (≈180K tokens)
Does not fit — must chunk
Full meeting transcript (3h)
Fits (≈150K tokens)
Borderline
When to choose Claude Sonnet 4.6
Long-context tasks: 200K window handles entire codebases, long documents, full conversation histories without chunking logic.
Production cost at scale: Prompt caching turns a $3/M input price into $0.30/M for repeated context — critical for high-volume apps.
Code review and refactoring: Claude's training tends toward longer, more reasoning-heavy explanations — better for understanding why code should change, not just what to change.
Avoiding OpenAI lock-in: Direct API or AWS Bedrock; no dependency on OpenAI infrastructure.
When to choose GPT-4o
JSON mode: GPT-4o's native response_format: {type: "json_object"} is simpler than Claude's tool-use-based approach for structured output.
Audio input: GPT-4o Audio handles speech natively. Claude requires separate transcription.
OpenAI Assistants API: If you need persistent threads, file search, or code interpreter as managed services.
Slightly cheaper output: $10/M vs $15/M output tokens matters if your app is output-heavy (long reports, essays) without caching.
It depends on the task. Claude Sonnet 4.6 outperforms GPT-4o on long-document analysis (200K vs 128K context), has superior prompt caching (up to 90% cost reduction, native in the API), and tends to produce more detailed code explanations. GPT-4o has stronger ecosystem integrations (plugins, Assistants API, structured outputs as a native flag) and is slightly cheaper at $2.50/$10 vs $3/$15 per million tokens.
How much does Claude Sonnet 4.6 cost vs GPT-4o?
As of 2026: Claude Sonnet 4.6 is $3 per million input tokens / $15 per million output tokens. GPT-4o is $2.50/$10. With Claude's prompt caching, repeated context costs $0.30/$15 (cached input at 90% discount), making Claude significantly cheaper for production chatbots and RAG with long system prompts.
What is Claude Sonnet's context window?
Claude Sonnet 4.6 supports a 200K token context window — roughly 150,000 words or about 500 pages of text. GPT-4o supports 128K. The extra 72K tokens matters most when processing large codebases, long legal documents, or full meeting transcripts in a single request.
Does Claude Sonnet support tool use like GPT-4o?
Yes. Both models support tool use (function calling). The API shape is slightly different: Claude uses input_schema instead of parameters, and response blocks come back as tool_use content blocks. The capability is equivalent — parallel tool calls, multi-turn tool conversations, and complex agentic workflows all work in both APIs.
Which model is better for coding?
Both perform well on coding benchmarks (HumanEval, SWE-bench). Claude Sonnet 4.6 tends to write more thoroughly documented code and excels at large-scale refactoring due to its context window advantage. GPT-4o tends to be faster at short-form code generation. For production coding tools, test both on your specific codebase — quality varies significantly by language and task type.