README

Purpose

Comprehensive analysis of Claude Opus 4.5 (released November 24, 2025), including performance characteristics, quality benchmarks, cost comparison with Sonnet 4.5, and practical recommendations for model selection and deployment strategies.

Key Findings

Performance Leadership

• SWE-bench Verified: 80.9% (first Claude model to exceed 80%)
• SWE-bench Multilingual: Wins in 7 out of 8 programming languages
• Aider Polyglot: 10.6% improvement over Sonnet 4.5
• Vending-Bench (long tasks): 29% better than Sonnet 4.5
• Efficiency: Achieves Sonnet’s best score using 76% fewer output tokens at “medium effort”

Cost Positioning (Most Important)

• Opus 4.5: 25 output per million tokens
• Sonnet 4.5: 15 output per million tokens (standard)
• Cost Ratio: Opus is ~67% more expensive than Sonnet
• Historical Context: Opus 4.5 costs 1/3 less than previous Opus models (was 75)

Quality Characteristics

• Best for: Coding, agents, computer use, complex reasoning
• Code Quality: Writes better code across programming languages (SWE-bench Multilingual)
• Long-running Tasks: Superior performance on extended tasks (Vending-Bench +29%)
• First 80% Model: First Claude model to score >80% on SWE-bench Verified

Performance Characteristics

Benchmark Results

Model Capabilities

• Flagship model for complex reasoning and multi-step problems
• Computer use & agents: Excellent performance on OSWorld
• Coding: Superior code quality, especially for challenging problems
• Long-form tasks: 29% better sustained performance vs Sonnet
• Efficiency at scale: Matches Sonnet’s output quality with significantly less tokens

Cost Analysis

Pricing Structure

Claude Opus 4.5:
  Input:  $5 per million tokens
  Output: $25 per million tokens

Claude Sonnet 4.5 (≤200K context):
  Input:  $3 per million tokens
  Output: $15 per million tokens

Claude Sonnet 4.5 (>200K context):
  Input:  $6 per million tokens
  Output: $22.5 per million tokens

Cost Comparison Scenarios

Scenario 1: Small requests (1-10K input, 1K output)

• Opus: ~0.000025 = $0.000075 per request
• Sonnet: ~0.000015 = $0.000045 per request
• Opus costs 67% more per request

Scenario 2: Coding task (5K input, 5K output)

• Opus: ~0.000125 = $0.00015 per task
• Sonnet: ~0.000075 = $0.00009 per task
• Opus costs 67% more per task

Scenario 3: Large batch (1M tokens in, 1M tokens out)

• Opus: 25 = $30
• Sonnet: 15 = $18
• Opus costs $12 more (67% premium)

Historical Price Reduction

• Opus 4 (May 2025): 75 output = $90/M tokens
• Opus 4.5 (Nov 2025): 25 output = $30/M tokens
• Reduction: 67% cost decrease for Opus-level performance

Deployment Strategy Recommendations

Use Opus 4.5 When:

1. Complex Coding Tasks: Software engineering, debugging, architecture design

   • Benefit: 80.9% SWE-bench performance, superior code quality
   • Example: Implementing complex algorithms, system architecture

2. Agents & Computer Use: Autonomous agents, multi-step workflows

   • Benefit: First-class support for complex reasoning chains
   • Example: Claude Code automation, multi-tool orchestration

3. Extended/Long-form Tasks: Tasks running >30 minutes

   • Benefit: 29% better performance on Vending-Bench
   • Example: Full codebase refactoring, comprehensive analysis

4. Quality-Critical Applications: When cost is secondary to quality

   • Benefit: Best-in-class output quality
   • Example: Production code generation, critical decision support

5. Token Efficiency Matters: When output token count is constrained

   • Benefit: 76% fewer tokens to achieve same quality
   • Example: Rate-limited APIs, token-capped scenarios

Use Sonnet 4.5 When:

1. Routine Tasks: Standard requests, simple coding, documentation

   • Benefit: 77.2% SWE-bench performance at 60% of Opus cost
   • Example: Code review, documentation generation

2. High-Volume Operations: 100s or 1000s of requests daily

   • Benefit: 67% cost savings at acceptable quality
   • Example: Batch processing, content generation

3. Interactive Applications: User-facing features with strict latency

   • Benefit: Faster response times, better UX
   • Example: Chat applications, real-time assistance

4. Budget-Constrained Projects: Limited API budget

   • Benefit: 15 pricing allows more usage
   • Example: Startups, MVP development

5. Parallel Execution: Using test-time compute (82.0% with parallel)

   • Benefit: Matches Opus performance with cost advantage
   • Example: Claude Code with parallel agents

Recommended Mix Strategy

Cost-Optimized Production Deployment:

• 80% Sonnet 4.5: Routine work, high-volume operations (saves 67% on this portion)
• 20% Opus 4.5: Complex tasks, agents, quality-critical work
• Result: 40% overall cost reduction vs Opus-only deployment

Key Comparisons

Sonnet 4.5 vs Opus 4.5

Integration with Claude Code

Sonnet 4.5 (Current Default)

• Used as primary execution model for Claude Code
• Excellent for code generation and analysis
• Sufficient for most development tasks
• Cost-effective for long-running sessions

Opus 4.5 (Recommended for Orchestration)

• Use for complex multi-task coordination
• Orchestrating parallel Haiku/Sonnet execution (as seen in frontmatter-improvement plan)
• Decision-making between different approaches
• Complex architectural planning

Optimal Mix for Claude Code Projects

Task Classification → Model Selection:
├── Simple execution tasks → Haiku 4.5
├── Standard development → Sonnet 4.5 (DEFAULT)
├── Complex coordination → Opus 4.5
└── Parallel execution → Sonnet 4.5 × N agents

Technical Specifications

Availability

• Release Date: November 24, 2025
• Platforms: Claude.ai, API, Amazon Bedrock, Google Cloud, Azure
• Access: Immediate availability for Claude Pro subscribers, API users

Integration Points

• Available in Claude Code for orchestration
• API integration for custom applications
• Browser extensions (Chrome, Excel integrations noted)
• Third-party platform integration

Effort Parameter (Game-Changer)

Opus 4.5 introduces an effort parameter that dramatically changes cost economics:

Direct API Only

The effort parameter is only accessible via direct API calls:

response = anthropic.messages.create(
    model="claude-opus-4-5-20251101",
    effort="medium",  # or "high"
    messages=[...]
)

Claude Code Limitation

Important: Claude Code does NOT support effort/thinking parameters in:

• Custom slash commands
• Task subagents
• Model configuration

Claude Code-Specific Strategy

Since effort parameter is unavailable, Opus runs at high effort by default:

Key insight: In Claude Code, Opus = Sonnet cost but +3.7pp better quality.

Recommendation for Claude Code:

• Simple tasks → Haiku 4.5 (fast, cheap)
• Complex tasks → Opus 4.5 (NOT Sonnet - same cost, better quality)
• Sonnet → Skip it (no advantage)

See claude-code-strategy.md for detailed implementation guide.

Sources

1. Anthropic - Introducing Claude Opus 4.5
2. TechCrunch - Anthropic releases Opus 4.5 with new Chrome and Excel integrations
3. CNBC - Anthropic unveils Claude Opus 4.5, its latest AI model
4. The New Stack - Anthropic’s New Claude Opus 4.5 Reclaims the Coding Crown
5. SiliconANGLE - Anthropic releases new flagship Claude Opus 4.5 model
6. AWS - Claude Opus 4.5 now in Amazon Bedrock

Related Research

• claude-code/ - Claude Code features and workflows
• automated-reasoning/ - Advanced reasoning capabilities and applications
• agents/ - AI agent patterns and multi-agent systems

Status

✅ RESEARCH COMPLETE - Comprehensive performance, quality, and cost analysis documented

Last updated: 2025-11-24 Research curator: Claude Code

Key Takeaways

For Direct API Users:

• Opus 4.5 medium effort is 39% cheaper than Sonnet for equivalent quality
• Opus 4.5 high effort is same cost as Sonnet but +4.3pp better
• Sonnet is obsolete for most use cases when effort parameter is available

For Claude Code Users:

• Opus runs at high effort (default), same cost as Sonnet
• Always use Opus over Sonnet for complex tasks (+3.7pp quality, same cost)
• Use Haiku for simple tasks (3x cheaper than Opus/Sonnet)
• Sonnet has no advantage in Claude Code - skip it

Universal:

• Opus 4.5 delivers 80.9% SWE-bench (first >80%)
• 29% better on long-running tasks (Vending-Bench)
• 7/8 programming language wins (SWE-bench Multilingual)