Benchmark Summary

Claude Opus 4.5 establishes itself as the highest-performing Claude model across most benchmarks while maintaining Sonnet’s efficiency advantages.

Detailed Performance Analysis

1. SWE-Bench Verified (Software Engineering)

What it measures: Ability to resolve real GitHub issues in actual repositories.

ModelScoreContextAnalysis
Opus 4.580.9%BaselineFirst Claude >80% - Sets new benchmark
Sonnet 4.577.2%Baseline attemptSolid performance, 3.7pp behind Opus
Sonnet 4.582.0%With parallel computeExceeds Opus when using test-time compute
Opus 4.174.5%Previous flagshipImprovement of 6.4pp in Opus 4.5

Interpretation:

  • Opus 4.5 single-attempt superior to base Sonnet
  • Sonnet with parallel execution can match/exceed Opus
  • Trade-off: Sonnet requires test-time compute; Opus is single-attempt

Use Case: For straightforward problem-solving, Opus faster (single attempt); for optimal results within latency constraints, Sonnet with parallelization.

2. SWE-Bench Multilingual (Programming Language Coverage)

What it measures: Code quality across diverse programming languages.

Results:

  • Opus 4.5: Wins in 7 out of 8 tested programming languages
  • Sonnet 4.5: Wins in 1 language
  • Performance gap: Opus shows superior cross-language coding capability

Languages tested (based on typical SWE-bench multilingual):

  • Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#

Interpretation:

  • Opus consistently produces higher-quality code across languages
  • Better for polyglot codebases
  • Sonnet still competitive in specific languages (likely Python, JavaScript)

3. Aider Polyglot (Real-world Coding Assistance)

What it measures: Performance on real GitHub issues using aider (AI code editor).

Results:

  • Opus 4.5: Baseline
  • Sonnet 4.5: -10.6% vs Opus
  • Gap: Over 10% difference in solving capability

Interpretation:

  • Opus more reliable for complex, real-world coding scenarios
  • Significant gap for production code quality
  • Suggests Opus better handles edge cases and complex interactions

4. Vending-Bench (Long-running Task Performance)

What it measures: Performance on extended, multi-step tasks (30+ minute sequences).

Results:

  • Opus 4.5: Baseline
  • Sonnet 4.5: -29% vs Opus
  • Magnitude: Nearly 30% performance degradation over extended tasks

Interpretation:

  • Significant advantage for Opus on sustained reasoning
  • Opus maintains quality better as task complexity increases
  • Sonnet may suffer from context accumulation or attention degradation
  • Critical for: agent loops, orchestration tasks, complex workflows

5. Token Efficiency (Output Token Count)

What it measures: Quality achieved per output token (efficiency metric).

Comparison (at “medium effort” level):

  • Opus 4.5: Achieves Sonnet’s best score using 76% fewer output tokens
  • Implication: Can reach same quality with 1/4 the tokens

Scenarios where this matters:

  • Rate-limited APIs
  • Cost-sensitive operations despite higher per-token cost
  • Context window constraints
  • Real-time applications where token count = latency

Performance Profiles

Opus 4.5 Performance Profile

✅ Strengths:

  • First model over 80% SWE-bench
  • Consistent wins across 7/8 programming languages
  • 10.6% better on real-world coding (Aider)
  • 29% better on long-running tasks (Vending)
  • 76% better token efficiency
  • Single-attempt excellence

❌ Weaknesses:

  • Slightly slower inference than Sonnet
  • 67% higher cost per token
  • Not better with parallel/test-time compute (Sonnet surpasses)

Sonnet 4.5 Performance Profile

✅ Strengths:

  • Fast inference (lower latency)
  • 77.2% baseline performance is excellent
  • 82% with parallel compute (exceeds Opus)
  • Better cost-to-performance ratio
  • More efficient with parallelization

❌ Weaknesses:

  • 3.7pp behind Opus on single-attempt coding
  • 10.6% weaker on real-world coding scenarios
  • 29% worse on extended tasks (critical for agents)
  • Lower per-token quality

Speed & Latency Comparison

Note: Exact latency metrics not disclosed in announcements, but:

  • Sonnet 4.5: Generally faster inference (implied by “efficient” positioning)
  • Opus 4.5: Slightly slower, typical for flagship models
  • Recommendation: Sonnet for latency-sensitive applications

When Performance Matters Most

Opus 4.5 Performance Advantages Matter When:

  1. Complex problem-solving required

    • Multi-step reasoning
    • Edge case handling
    • Novel solutions needed

  2. Code quality is paramount

    • Production systems
    • Security-critical code
    • Performance-critical algorithms

  3. Extended task execution

    • Agent loops (>30 min)
    • Complex orchestration
    • Iterative refinement workflows

  4. Polyglot codebases

    • Multi-language projects
    • Cross-platform development
    • Language translation patterns

Sonnet 4.5 Performance is Sufficient When:

  1. Routine development tasks

    • Standard CRUD operations
    • API implementations
    • Configuration code

  2. High-frequency requests

    • 100s of requests/day
    • Interactive user-facing features
    • Real-time applications

  3. Parallelizable workloads

    • Test-time compute available
    • Can afford latency for higher quality
    • 82% performance achievable with parallel

  4. Well-structured problems

    • Clear requirements
    • Standard algorithms
    • Common patterns

Benchmark Reliability

SWE-Bench Considerations:

  • Measures GitHub issue resolution (real-world proxy)
  • Simulates developer workflow
  • Reproducible and verified
  • Generally considered reliable indicator

Aider Polyglot Considerations:

  • Real coding scenarios in aider editor
  • Includes human interaction patterns
  • Less widely tracked (newer metric)
  • Good real-world relevance

Vending-Bench Considerations:

  • Long-running task proxy
  • Measures sustained performance
  • Important for agent applications
  • Less common in benchmarking

Practical Performance Implications

For Claude Code Development

  • Orchestration logic: Use Opus 4.5 for complex plan execution
  • Task execution: Sonnet 4.5 for individual tasks (cost-effective)
  • Agent loops: Use Opus 4.5 for long-running agents
  • Parallel execution: Sonnet 4.5 with multiple agents can achieve 82%

For Production Applications

  • Code generation: Opus 4.5 for critical paths, Sonnet for bulk
  • Real-time chat: Sonnet 4.5 for speed and cost
  • Analysis workflows: Opus 4.5 for multi-step analysis
  • Batch processing: Sonnet 4.5 for cost efficiency

Performance Outlook

Trajectory:

  • Opus 4.5 shows Anthropic pushing frontier on quality
  • Sonnet 4.5 shows strong efficiency positioning
  • Gap between them: ~3-10pp on single-attempt tasks, narrower with parallelization
  • Future: Likely Haiku 4.5 will approach Sonnet’s performance

Summary: Opus 4.5 wins decisively on single-attempt complex reasoning (80.9% SWE-bench), extended tasks (+29% Vending-Bench), and code quality (7/8 languages). Sonnet 4.5 becomes cost-competitive with parallelization (82% with parallel compute) but lacks Opus’s sustained performance on extended operations.