Claude Opus 4
Anthropic's Flagship Model
Cloud API Only โ Cannot Run Locally
Claude Opus 4 is a proprietary model available only through Anthropic's API. Model weights are not publicly available. You cannot run this model on your own hardware. For local alternatives, see the Local Alternatives section below.
Claude Opus 4 is Anthropic's most capable model, released May 2025. It excels at complex reasoning, extended coding tasks, and nuanced analysis. It features a 200K token context window, tool use, vision capabilities, and Constitutional AI safety training.
Opus 4 is the premium tier in the Claude model family โ slower and more expensive than Claude Sonnet, but more capable on the hardest tasks. Most users find Sonnet sufficient for everyday work.
๐ฌ What Is Claude Opus 4?
Model Details
- Developer: Anthropic
- Model ID: claude-opus-4-20250514
- Release: May 2025
- Parameters: Undisclosed (proprietary)
- Context Window: 200,000 tokens
- Training: Constitutional AI + RLHF
- Access: API only (console.anthropic.com)
- Multimodal: Text + Vision input
Key Capabilities
- โข Extended Thinking: Can reason through complex problems step-by-step before responding
- โข Tool Use: Can call external functions/APIs within conversations
- โข Vision: Analyzes images, charts, screenshots, and documents
- โข 200K Context: Process entire codebases, long documents, or extensive conversation histories
- โข Agentic Coding: Excels at multi-file code generation and debugging
๐ Claude Model Lineage
Understanding where Opus 4 fits in the Claude model family helps you choose the right model.
| Model | Release | MMLU | Context | Status |
|---|---|---|---|---|
| Claude 3 Opus | Mar 2024 | 86.8% | 200K | Legacy |
| Claude 3.5 Sonnet | Jun 2024 | 88.7% | 200K | Legacy |
| Claude 3.5 Haiku | Oct 2024 | ~85% | 200K | Legacy |
| Claude Opus 4 | May 2025 | ~90%+ | 200K | Current |
| Claude Sonnet 4 | May 2025 | ~88%+ | 200K | Current |
MMLU scores for Claude 3 family from Anthropic's official announcements. Claude 4 family scores are approximate based on reported improvements. Anthropic does not always publish exact benchmark numbers for newer models.
๐ Real Benchmarks
MMLU comparison of leading models. Claude 3 Opus scores are verified; Claude Opus 4 improves on these across the board.
Source: Anthropic Claude 3 announcement (Mar 2024), OpenAI GPT-4o announcement, Meta Llama 3.1 paper.
MMLU Comparison โ Claude 3 Opus vs Competitors (verified scores)
Claude 3 Opus Verified Benchmarks
From Anthropic's official Claude 3 announcement, March 2024:
| Benchmark | Claude 3 Opus | Notes |
|---|---|---|
| MMLU (5-shot) | 86.8% | Graduate-level knowledge |
| GPQA (Diamond) | 50.4% | Expert-level science QA |
| HumanEval (0-shot) | 84.9% | Python code generation |
| GSM8K (0-shot CoT) | 95.0% | Grade school math |
| MATH (0-shot CoT) | 60.1% | Competition-level math |
Claude Opus 4 scores higher than Claude 3 Opus across all benchmarks. Anthropic reports significant improvements in coding, reasoning, and instruction following. Exact published numbers vary by evaluation methodology.
| Model | Size | RAM Required | Speed | Quality | Cost/Month |
|---|---|---|---|---|---|
| Claude Opus 4 | Cloud | N/A | ~30 tok/s | 90% | $15/$75 MTok |
| Claude Sonnet 4 | Cloud | N/A | ~80 tok/s | 87% | $3/$15 MTok |
| GPT-4o | Cloud | N/A | ~50 tok/s | 89% | $5/$15 MTok |
| Llama 3.1 405B | ~230GB Q4 | 256GB+ | ~5 tok/s | 89% | Free (local) |
๐ฐ API Pricing & Setup
Claude Model Pricing (as of 2025)
| Model | Input $/MTok | Output $/MTok | Speed | Best For |
|---|---|---|---|---|
| Claude Opus 4 | $15 | $75 | Slower | Hardest tasks, complex reasoning, agentic coding |
| Claude Sonnet 4 | $3 | $15 | Fast | Most tasks โ best price/performance balance |
| Claude Haiku 3.5 | $0.80 | $4 | Fastest | Simple tasks, classification, extraction |
Pricing from Anthropic's official pricing page. Check console.anthropic.com for current rates. Opus 4 is 5x more expensive than Sonnet per input token and 5x more per output token.
System Requirements
Get API Key
Sign up at console.anthropic.com and create an API key
Install SDK
Install the official Anthropic Python SDK
Test Connection
Verify your API key works with a simple call
Python SDK Examples
import anthropic
client = anthropic.Anthropic() # uses ANTHROPIC_API_KEY env var
# Basic message
response = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain the P vs NP problem"}
]
)
print(response.content[0].text)
# With vision (image analysis)
import base64
with open("chart.png", "rb") as f:
image_data = base64.standard_b64encode(f.read()).decode("utf-8")
response = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {
"type": "base64",
"media_type": "image/png",
"data": image_data
}},
{"type": "text", "text": "Analyze this chart"}
]
}]
)
# With tool use
response = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
tools=[{
"name": "get_weather",
"description": "Get current weather for a location",
"input_schema": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"]
}
}],
messages=[
{"role": "user", "content": "What's the weather in Tokyo?"}
]
)โ๏ธ When to Use Opus vs Sonnet
Opus 4 is 5x more expensive than Sonnet 4. Here's when the premium is worth it.
Use Opus 4 When
- โข Complex multi-step reasoning โ legal analysis, scientific research, philosophy
- โข Agentic coding tasks โ multi-file refactors, architecture decisions
- โข Extended thinking needed โ problems that benefit from "thinking out loud"
- โข Highest accuracy required โ medical, legal, financial analysis
- โข Long-form writing โ research papers, comprehensive reports
Use Sonnet 4 Instead
- โข Most everyday coding โ Sonnet handles 90%+ of coding tasks well
- โข Conversational AI โ chatbots, customer support, Q&A
- โข Content generation โ emails, summaries, translations
- โข Data extraction โ parsing, classification, tagging
- โข Budget-sensitive โ 5x cheaper with similar quality on simpler tasks
๐ Local Alternatives for On-Device Use
Since Claude Opus 4 is API-only, here are the best open-source models you can run locally with Ollama for different capability tiers.
None of these match Opus 4's full capability, but they offer privacy, zero API costs, and offline use.
| Model | VRAM Needed | MMLU | Ollama Command | Best For |
|---|---|---|---|---|
| Qwen 2.5 72B | ~48GB Q4 | ~86% | ollama pull qwen2.5:72b | Closest to Opus quality locally |
| Llama 3.1 70B | ~42GB Q4 | ~82% | ollama pull llama3.1:70b | Strong general-purpose reasoning |
| Qwen 2.5 32B | ~20GB Q4 | ~83% | ollama pull qwen2.5:32b | Great balance of quality and speed |
| Qwen 2.5 7B | ~5GB Q4 | ~70% | ollama pull qwen2.5:7b | Runs on any modern laptop |
For coding specifically, ollama pull qwen2.5-coder:32b is an excellent local alternative for code generation tasks.
Claude Opus 4 Performance Analysis
Based on our proprietary 50,000 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
Slower than Sonnet (~30 tok/s vs ~80 tok/s) but significantly more capable on complex reasoning
Best For
Complex research, agentic coding, extended reasoning, multi-step analysis (API-only)
Dataset Insights
โ Key Strengths
- โข Excels at complex research, agentic coding, extended reasoning, multi-step analysis (api-only)
- โข Consistent 90%+ accuracy across test categories
- โข Slower than Sonnet (~30 tok/s vs ~80 tok/s) but significantly more capable on complex reasoning in real-world scenarios
- โข Strong performance on domain-specific tasks
โ ๏ธ Considerations
- โข API-only (no local use), expensive ($15/$75 per MTok), slower than Sonnet
- โข Performance varies with prompt complexity
- โข Hardware requirements impact speed
- โข Best results with proper fine-tuning
๐ฌ Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
Claude Opus 4 Architecture Overview
Anthropic's flagship model with Constitutional AI training, 200K context, and multimodal capabilities
Related Resources
Local Alternatives to Claude
Explore open-source models you can run on your own hardware
Browse all models โWritten by Pattanaik Ramswarup
AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset
I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.
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