Claude 3.7 Sonnet
Anthropic • text+image+file->text
Context Window200K
Knowledge CutoffFeb 24, 2025
Claude 3.7 Sonnet vs GPT-4o-mini
Claude 3.7 Sonnet for premium coding quality, GPT-4o mini for high-volume cost-sensitive workloads.
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GPT-4o-mini
OpenAI • text+image+file->text
Context Window128K
Knowledge CutoffJul 18, 2024
Editorial Verdict
Claude 3.7 Sonnet is the high-capability model; GPT-4o mini is the efficient mini option.
Claude 3.7 Sonnet significantly outperforms GPT-4o mini on coding, reasoning, and instruction following benchmarks. GPT-4o mini is optimized for cost and speed. For maximum quality, Claude 3.7 Sonnet wins by a wide margin. For cost-sensitive simple tasks, GPT-4o mini is the better value.
Claude 3.7 Sonnet
- Significantly higher capability on coding, reasoning, and instruction following
- Extended thinking mode for complex multi-step reasoning
- 200K context window for long-document processing
Best for: Complex coding, analysis, and instruction-following tasks
GPT-4o-mini
- Low cost and fast inference for simple, high-volume tasks
- 128K context with good general capability
- Mature API with reliable function calling
Best for: Simple high-volume tasks optimizing for cost and speed
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | Claude 3.7 Sonnet | GPT-4o-mini | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $3.00 | $0.15 | 🏆 GPT-4o-mini |
| Output (per 1M tokens) | $15.00 | $0.60 | 🏆 GPT-4o-mini |
| 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.
Claude 3.7 Sonnet Total
$330GPT-4o-mini Total
$14.25Estimated Monthly Savings: $315.75 with GPT-4o-mini
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Claude 3.7 SonnetMMLU Signal (Reasoning)GPT-4o-mini
87%
69%
Claude 3.7 SonnetHumanEval Signal (Coding)GPT-4o-mini
76%
76%
Claude 3.7 SonnetAgentic Tooling SignalGPT-4o-mini
84%
84%
Claude 3.7 SonnetMultimodal SignalGPT-4o-mini
78%
78%
Capabilities Matrix
Feature highlights for architecture and production fit.
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.
GPT-4o-mini 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.
API Implementation
Quick start snippets for each provider style.
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)OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)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.
Choose GPT-4o-mini 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.
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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