GPT-4o
OpenAI • text+image+file->text
Context Window128K
Knowledge CutoffMay 13, 2024
GPT-4o vs Claude 3.7 Sonnet
GPT-4o for multimodal breadth, Claude 3.7 for coding-heavy workflows.
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Claude 3.7 Sonnet
Anthropic • text+image+file->text
Context Window200K
Knowledge CutoffFeb 24, 2025
Editorial Verdict
Claude 3.7 Sonnet is purpose-built for deep reasoning and coding; GPT-4o is optimized for broad production use.
GPT-4o and Claude 3.7 Sonnet are both top-tier general-purpose models, but they serve different priorities. GPT-4o wins on ecosystem breadth and multimodal consistency, while Claude 3.7 Sonnet wins on coding depth and instruction adherence. For production applications needing mature tooling and broad API support, GPT-4o is the safer bet. For teams doing heavy software development or needing nuanced, long-form reasoning, Claude 3.7 Sonnet edges ahead.
GPT-4o
- Industry-leading API ecosystem with extensive tooling, language support, and production hardening
- Consistent multimodal behavior across text, images, audio, and video in a single model
- Faster inference speeds in production with well-optimized serving infrastructure
Best for: Production apps, multimodal workflows, and teams needing mature tooling and ecosystem support
Claude 3.7 Sonnet
- Superior coding performance on complex, multi-file tasks and large codebases
- Industry-leading 200K context window for processing extensive documents or codebases
- More precise instruction following with fewer off-topic responses on ambiguous queries
Best for: Software development, complex reasoning tasks, and long-document analysis
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | GPT-4o | Claude 3.7 Sonnet | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $2.50 | $3.00 | 🏆 GPT-4o |
| Output (per 1M tokens) | $10.00 | $15.00 | 🏆 GPT-4o |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
GPT-4o Total
$237.5Claude 3.7 Sonnet Total
$330Estimated Monthly Savings: $92.5 with GPT-4o
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
GPT-4oMMLU Signal (Reasoning)Claude 3.7 Sonnet
69%
87%
GPT-4oHumanEval Signal (Coding)Claude 3.7 Sonnet
76%
76%
GPT-4oAgentic Tooling SignalClaude 3.7 Sonnet
84%
84%
GPT-4oMultimodal SignalClaude 3.7 Sonnet
78%
78%
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o Strengths
- Supports image inputs for multimodal analysis workflows.
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Claude 3.7 Sonnet Strengths
- Supports image inputs for multimodal analysis workflows.
- Strong tool-calling and structured response support.
- Large context window for long documents and codebases.
API Implementation
Quick start snippets for each provider style.
OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Anthropic (Python)
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="anthropic/claude-3.7-sonnet",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello"}]
)
print(message.content)Choose GPT-4o when...
- Your application includes visual reasoning and image understanding tasks.
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
Choose Claude 3.7 Sonnet when...
- Your application includes visual reasoning and image understanding tasks.
- You process large documents or code repositories in a single prompt.
- You rely on function calling, tool chaining, and structured output contracts.
Frequently Asked Questions
Practical checks before selecting a production model.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
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