cost-analysis

Pricing Structure

Current Pricing (November 2025)

Claude Opus 4.5:

• Input: $5 per million tokens
• Output: $25 per million tokens
• Total per 1M input + 1M output: $30

Claude Sonnet 4.5 (≤200K context):

• Input: $3 per million tokens
• Output: $15 per million tokens
• Total per 1M input + 1M output: $18

Claude Sonnet 4.5 (>200K context):

• Input: $6 per million tokens
• Output: $22.5 per million tokens
• Total per 1M input + 1M output: $28.5

Claude Haiku 4.5 (for reference):

• Input: $0.80 per million tokens
• Output: $4 per million tokens
• Total per 1M input + 1M output: $4.80

Cost Comparison Ratios

Historical Context: Opus Price Reduction

Opus Pricing Evolution

Significance: Opus 4.5 is 1/3 the price of Opus 4 while delivering improved performance.

Cost Scenarios

Scenario 1: Small Interactive Request

Input: 1,000 tokens Output: 500 tokens

Scenario 2: Typical Development Task

Input: 5,000 tokens Output: 2,000 tokens

Scenario 3: Large Code Generation

Input: 10,000 tokens Output: 8,000 tokens

Scenario 4: Large Dataset Analysis

Input: 100,000 tokens Output: 50,000 tokens

Scenario 5: Monthly High-Volume Usage

Volume: 1 billion input tokens, 500M output tokens

Scenario 6: Context Window Impact on Sonnet

Usage Pattern: 25% requests >200K context

Monthly: 1B input tokens, 500M output tokens

Cost-Benefit Analysis

Opus 4.5: When Premium Cost is Justified

Use Case 1: SWE-Bench Performance Gap

• Performance delta: 80.9% (Opus) vs 77.2% (Sonnet) = 3.7pp
• Cost premium: 67%
• ROI: 3.7pp improvement for 67% cost increase = Not ideal unless failures are expensive

Use Case 2: Vending-Bench Long-Task Gap

• Performance delta: +29% (Opus) vs Sonnet on extended tasks
• Cost premium: 67%
• ROI: 29% improvement for 67% cost = Favorable (0.43 points improvement per cost point)

Use Case 3: Token Efficiency

• Opus achieves Sonnet quality with 76% fewer output tokens
• If output token count is constrained, efficiency premium justified
• ROI: 76% fewer tokens = significant in latency-critical or quota-limited scenarios

Use Case 4: Failure Costs are High

• Production systems, security, regulatory requirements
• Cost premium ($0.00003 per task) << cost of failure
• ROI: Risk reduction > cost premium

Sonnet 4.5: Cost-Effective for Most Work

Advantage 1: 67% Cost Savings

• 80% of routine work suitable for Sonnet
• Saves 67% on this portion
• Example: 1M Sonnet calls = 30 on Opus = $12 saved

Advantage 2: Performance Sufficiency

• 77.2% SWE-bench = still excellent
• Suitable for routine development
• Handles 80% of typical use cases

Advantage 3: Parallelization Potential

• 82% with parallel compute > Opus 80.9% single-attempt
• Multiple Sonnet calls often cheaper than single Opus call
• Cost per quality point improves with parallelization

Deployment Cost Optimization

Strategy 1: Hybrid Model Selection (Recommended)

Task Classification → Model → Cost Impact
├── Routine work (60%) → Sonnet → Baseline $9,000
├── Complex tasks (30%) → Opus → +$4,500
├── Real-time tasks (10%) → Haiku → -$800
└── Total: $12,700 vs Opus-only $15,000
   Savings: 15% vs Opus-only deployment

Strategy 2: 80/20 Split (Most Cost-Effective)

Allocation:

• 80% Sonnet ($18 per 1M tokens)
• 20% Opus ($30 per 1M tokens)
• Weighted average: (0.8 × 30) = 6 = $20.40/M tokens

vs Pure Models:

• Pure Sonnet: $18/M (more than 80% use, saves more)
• Pure Opus: $30/M (premium quality)
• 80/20 Hybrid: $20.40/M (compromise with 15% premium over pure Sonnet)

Monthly calculation (1B input + 500M output):

• Pure Sonnet: $9,000
• 80/20 Hybrid: $10,200
• Pure Opus: $15,000
• 80/20 saves $4,800/month vs Opus (-32%)

Strategy 3: Context Window Optimization

For organizations using Sonnet >200K context frequently:

Pattern: 30% of requests >200K context

• Standard Sonnet pricing: $9,000/month
• Mixed context pricing: $9,900/month (+10%)
• Using Opus instead: $15,000/month (+67%)

Recommendation: Accept 10% Sonnet increase for mixed contexts, avoid Opus upgrade.

Strategy 4: Parallelization as Cost Control

Single Opus attempt: $30/1M tokens, 80.9% quality

Three Sonnet attempts (parallel):

• Cost: 3 × 54/1M tokens
• Quality: 82% (with parallel compute)
• Better quality, higher cost (not recommended)

Alternative: Smart parallelization

• Simple tasks: 1 × Sonnet ($18)
• Complex tasks: 2-3 × Haiku (9.60-$14.40)
• Fallback: 1 × Opus ($30) if parallel fails
• Average: ~$15-20, comparable to Opus with better flexibility

Break-Even Analysis

Question: When Does Opus Premium Pay Back?

Assumption: Improved code quality (0.1pp SWE-bench) reduces bugs by 5%

Example Project:

• 1000 code generation tasks/month
• Bug fix cost: $500 each
• Baseline Sonnet bug rate: 22.8% (100% - 77.2% success)
• Improved Opus bug rate: 19.1% (100% - 80.9% success)
• Bug reduction: 3.7pp × 1000 = 37 fewer bugs
• Bug savings: 37 × 18,500/month
• Opus cost premium: $6,000/month
• Savings: $12,500/month (ROI = 208%)

Counterexample:

• Low-stakes tasks (documentation, comments)
• Bug cost: $10 each
• Bug savings: 37 × 370/month
• Opus premium: $6,000/month
• Net loss: -$5,630/month (negative ROI)

Cost Recommendation Framework

Price Prediction

Historical Trend: Opus 4 → Opus 4.5 = 67% reduction in 6 months

Possible Future Scenarios:

1. Price stability: Opus holds at 25 for 12+ months
2. Gradual reduction: Sonnet prices drop 10-20%, Opus stays flat
3. Compression: Sonnet approaches Opus in capability and pricing
4. New tier: Opus-specific pricing emerges

Recommendation: Expect pricing to evolve; lock in current rates for budget planning.

Summary: Opus 4.5 at 25 is 67% more expensive than Sonnet 4.5 (15), representing a 67% price reduction from Opus 4 (75). The hybrid 80/20 strategy (80% Sonnet, 20% Opus) optimizes cost while maintaining quality, saving 15% vs pure Opus deployment and providing a 15% premium over pure Sonnet for complex workloads.