GPT-4o
OpenAI • Multimodal
Context Window128K
Knowledge CutoffUnknown
GPT-4o vs Claude 3 Opus
Claude 3 Opus for highest reasoning quality, GPT-4o for production ecosystem and speed.
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Claude 3 Opus
Anthropic • Text + Vision
Context Window200K
Knowledge CutoffUnknown
Editorial Verdict
Claude 3 Opus prioritizes maximum reasoning quality; GPT-4o prioritizes production readiness and cost efficiency.
Claude 3 Opus represents Anthropic's highest capability model and outperforms GPT-4o on the most demanding reasoning and analysis tasks. GPT-4o maintains advantages in speed, API maturity, and multimodal consistency. For maximum quality on complex problems where budget is secondary, Claude 3 Opus is the winner. For production applications where latency and cost matter, GPT-4o is the practical choice.
GPT-4o
- Battle-tested production API with global availability and enterprise SLA guarantees
- Strong multimodal performance across vision, audio, and video in a unified model
- Lower cost per token than Claude 3 Opus with faster average response times
Best for: Production applications, cost-sensitive deployments, and multimodal use cases requiring API maturity
Claude 3 Opus
- Highest benchmark scores on complex reasoning, graduate-level science, and multi-step analysis
- Better handling of ambiguous or underspecified prompts without clarification requests
- More consistent outputs on long, structured tasks like legal document review or research synthesis
Best for: High-stakes analysis, research-grade reasoning, and tasks where quality is paramount
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 Opus | 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 Opus 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 Opus
69%
76%
GPT-4oHumanEval Signal (Coding)Claude 3 Opus
69%
69%
GPT-4oAgentic Tooling SignalClaude 3 Opus
69%
69%
GPT-4oMultimodal SignalClaude 3 Opus
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 Opus 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-opus",
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 Opus 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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