GPT-4o
OpenAI • Multimodal
Context Window128K
Knowledge CutoffUnknown
GPT-4o vs Claude 3.5 Sonnet
GPT-4o for newer model with multimodal improvements, Claude 3.5 Sonnet for proven reliability at a lower price point.
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Claude 3.5 Sonnet
Anthropic • Text + Vision
Context Window200K
Knowledge CutoffUnknown
Editorial Verdict
GPT-4o is the highest capability option; Claude 3.5 Sonnet is the best value for most production workloads.
GPT-4o outperforms Claude 3.5 Sonnet on most benchmarks, but the gap is smaller than the model names suggest. Claude 3.5 Sonnet is significantly cheaper and faster while delivering 90% of GPT-4o's capability on typical tasks. For cost-sensitive applications, Claude 3.5 Sonnet is the winner. For maximum quality on the hardest tasks, GPT-4o leads.
GPT-4o
- Higher benchmark scores on reasoning, coding, and multimodal tasks
- Mature API with more consistent function calling and JSON mode behavior
- Stronger on complex, multi-step reasoning tasks requiring deep analysis
Best for: Maximum quality requirements and complex multimodal production applications
Claude 3.5 Sonnet
- Significantly lower cost per token, making high-volume applications economically viable
- Excellent coding performance with strong repository-level understanding
- Fast inference with reliable response quality for everyday production workloads
Best for: Cost-sensitive production applications and teams prioritizing price-to-performance
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 3.5 Sonnet | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| 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
VariableClaude 3.5 Sonnet Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
GPT-4oMMLU Signal (Reasoning)Claude 3.5 Sonnet
69%
69%
GPT-4oHumanEval Signal (Coding)Claude 3.5 Sonnet
69%
69%
GPT-4oAgentic Tooling SignalClaude 3.5 Sonnet
69%
69%
GPT-4oMultimodal SignalClaude 3.5 Sonnet
69%
78%
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Claude 3.5 Sonnet Strengths
- Supports image inputs for multimodal analysis workflows.
- Large context window for long documents and codebases.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
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-3.5-sonnet",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello"}]
)
print(message.content)Choose GPT-4o when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose Claude 3.5 Sonnet when...
- Your application includes visual reasoning and image understanding tasks.
- You process large documents or code repositories in a single prompt.
- 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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