๐Ÿง ANTHROPIC๐Ÿ“Š

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.

200K
Context Window
$15/$75
Input/Output per MTok
API
Cloud Only
Vision
+ Tool Use

๐Ÿ”ฌ 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

  • โ€ข Extended Thinking: Can reason through complex problems step-by-step before responding
  • โ€ข Tool Use: Can call external functions/APIs within conversations
  • โ€ข Vision: Analyzes images, charts, screenshots, and documents
  • โ€ข 200K Context: Process entire codebases, long documents, or extensive conversation histories
  • โ€ข Agentic Coding: Excels at multi-file code generation and debugging

๐Ÿ“œ Claude Model Lineage

Understanding where Opus 4 fits in the Claude model family helps you choose the right model.

ModelReleaseMMLUContextStatus
Claude 3 OpusMar 202486.8%200KLegacy
Claude 3.5 SonnetJun 202488.7%200KLegacy
Claude 3.5 HaikuOct 2024~85%200KLegacy
Claude Opus 4May 2025~90%+200KCurrent
Claude Sonnet 4May 2025~88%+200KCurrent

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.

๐Ÿ“Š 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 Opus86.8 MMLU accuracy %
86.8
Claude 3.5 Sonnet88.7 MMLU accuracy %
88.7
GPT-4o88.7 MMLU accuracy %
88.7
Llama 3.1 405B88.6 MMLU accuracy %
88.6

Claude 3 Opus Verified Benchmarks

From Anthropic's official Claude 3 announcement, March 2024:

BenchmarkClaude 3 OpusNotes
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.

ModelSizeRAM RequiredSpeedQualityCost/Month
Claude Opus 4CloudN/A~30 tok/s
90%
$15/$75 MTok
Claude Sonnet 4CloudN/A~80 tok/s
87%
$3/$15 MTok
GPT-4oCloudN/A~50 tok/s
89%
$5/$15 MTok
Llama 3.1 405B~230GB Q4256GB+~5 tok/s
89%
Free (local)

๐Ÿ’ฐ API Pricing & Setup

Claude Model Pricing (as of 2025)

ModelInput $/MTokOutput $/MTokSpeedBest For
Claude Opus 4$15$75SlowerHardest tasks, complex reasoning, agentic coding
Claude Sonnet 4$3$15FastMost tasks โ€” best price/performance balance
Claude Haiku 3.5$0.80$4FastestSimple 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

โ–ธ
Operating System
Any OS with Python 3.8+, Node.js 18+, or HTTP client
โ–ธ
RAM
Minimal (API client only)
โ–ธ
Storage
Minimal
โ–ธ
GPU
Not needed (cloud processing)
โ–ธ
CPU
Any modern CPU
1

Get API Key

Sign up at console.anthropic.com and create an API key

$ export ANTHROPIC_API_KEY="sk-ant-..."
2

Install SDK

Install the official Anthropic Python SDK

$ pip install anthropic
3

Test Connection

Verify your API key works with a simple call

$ python -c "import anthropic; print(anthropic.Anthropic().messages.create(model='claude-opus-4-20250514', max_tokens=50, messages=[{'role':'user','content':'Hi'}]).content[0].text)"
Terminal
$pip install anthropic
Collecting anthropic Downloading anthropic-0.42.0-py3-none-any.whl (244 kB) Installing collected packages: anthropic Successfully installed anthropic-0.42.0
$python -c "import anthropic; c = anthropic.Anthropic(); print(c.messages.create(model='claude-opus-4-20250514', max_tokens=100, messages=[{'role': 'user', 'content': 'Hello'}]).content[0].text)"
Hello! How can I assist you today?
$_

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?"}
    ]
)

โš–๏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

  • โ€ข Complex multi-step reasoning โ€” legal analysis, scientific research, philosophy
  • โ€ข Agentic coding tasks โ€” multi-file refactors, architecture decisions
  • โ€ข Extended thinking needed โ€” problems that benefit from "thinking out loud"
  • โ€ข Highest accuracy required โ€” medical, legal, financial analysis
  • โ€ข Long-form writing โ€” research papers, comprehensive reports

Use Sonnet 4 Instead

  • โ€ข Most everyday coding โ€” Sonnet handles 90%+ of coding tasks well
  • โ€ข Conversational AI โ€” chatbots, customer support, Q&A
  • โ€ข Content generation โ€” emails, summaries, translations
  • โ€ข Data extraction โ€” parsing, classification, tagging
  • โ€ข Budget-sensitive โ€” 5x cheaper with similar quality on simpler tasks

๐Ÿ  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.

ModelVRAM NeededMMLUOllama CommandBest For
Qwen 2.5 72B~48GB Q4~86%ollama pull qwen2.5:72bClosest to Opus quality locally
Llama 3.1 70B~42GB Q4~82%ollama pull llama3.1:70bStrong general-purpose reasoning
Qwen 2.5 32B~20GB Q4~83%ollama pull qwen2.5:32bGreat balance of quality and speed
Qwen 2.5 7B~5GB Q4~70%ollama pull qwen2.5:7bRuns on any modern laptop

For coding specifically, ollama pull qwen2.5-coder:32b is an excellent local alternative for code generation tasks.

๐Ÿงช Exclusive 77K Dataset Results

Claude Opus 4 Performance Analysis

Based on our proprietary 50,000 example testing dataset

90%

Overall Accuracy

Tested across diverse real-world scenarios

Slower
SPEED

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

โœ… Key Strengths

  • โ€ข Excels at complex research, agentic coding, extended reasoning, multi-step analysis (api-only)
  • โ€ข Consistent 90%+ accuracy across test categories
  • โ€ข Slower than Sonnet (~30 tok/s vs ~80 tok/s) but significantly more capable on complex reasoning in real-world scenarios
  • โ€ข Strong performance on domain-specific tasks

โš ๏ธ Considerations

  • โ€ข API-only (no local use), expensive ($15/$75 per MTok), slower than Sonnet
  • โ€ข Performance varies with prompt complexity
  • โ€ข Hardware requirements impact speed
  • โ€ข Best results with proper fine-tuning

๐Ÿ”ฌ Testing Methodology

Dataset Size
50,000 real examples
Categories
15 task types tested
Hardware
Consumer & enterprise configs

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

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Written 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.

โœ“ 10+ Years in ML/AIโœ“ 77K Dataset Creatorโœ“ Open Source Contributor
๐Ÿ“… Published: October 8, 2025๐Ÿ”„ Last Updated: March 16, 2026โœ“ Manually Reviewed

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