Summary
Sonnet 4.6 scores 79.6% on SWE-bench Verified. Opus 4.6 scores 80.8%. The gap is 1.2 percentage points. Sonnet costs $3/$15 per million tokens, Opus costs $5/$25. For routine code generation, feature work, and single-file edits, Sonnet matches Opus. The premium buys you better performance on multi-file refactoring, architectural reasoning, and long-context tasks above 50K tokens.
Best if you need the absolute highest accuracy on complex, multi-file coding tasks and can absorb a 40% cost premium.
Best if you want 97-99% of Opus coding quality at 60% of the cost, with faster output speed.
Benchmark Comparison
| Benchmark | Opus 4.6 | Sonnet 4.6 | Gap |
|---|---|---|---|
| SWE-bench Verified | 80.8% | 79.6% | 1.2 pts |
| SWE-bench Pro | 55.4% | ~52% | ~3 pts |
| Terminal-Bench 2.0 | 65.4% | 59.1% | 6.3 pts |
| HumanEval | 97.6% | 96.8% | 0.8 pts |
| OSWorld-Verified | 72.7% | 72.5% | 0.2 pts |
| GPQA Diamond | 83.3% | 78.2% | 5.1 pts |
The pattern is clear. On standardized coding benchmarks (SWE-bench, HumanEval, OSWorld), the models are nearly identical. The gap widens on reasoning-heavy benchmarks (Terminal-Bench, GPQA Diamond) where Opus can spend more compute on deliberation.
What the benchmarks miss
Speed and Latency
| Metric | Opus 4.6 | Sonnet 4.6 |
|---|---|---|
| Output speed (tok/s) | 45.3 | 52.8 |
| Time to first token | 12.3s | ~5s (non-reasoning) |
| Reasoning TTFT | 12.3s | 102.4s (max effort) |
| Context window | 200K (1M beta) | 200K (1M beta) |
Sonnet outputs tokens 17% faster than Opus. In non-reasoning mode, Sonnet starts generating almost immediately. In reasoning mode (Adaptive Reasoning, Max Effort), Sonnet takes longer on the first token because it does more upfront thinking, but the total wall-clock time for a coding task is usually shorter because it produces output faster.
For interactive coding where you want to see partial results streaming, Sonnet in non-reasoning mode gives the best experience. For batch processing where accuracy matters more than latency, Opus in reasoning mode is the better pick.
Pricing Breakdown
| Cost Component | Opus 4.6 | Sonnet 4.6 | Savings |
|---|---|---|---|
| Input (per 1M tokens) | $5.00 | $3.00 | 40% |
| Output (per 1M tokens) | $25.00 | $15.00 | 40% |
| Cache write (5-min) | $6.25 | $3.75 | 40% |
| Cache read | $0.50 | $0.30 | 40% |
| Batch API (50% off) | $2.50/$12.50 | $1.50/$7.50 | 40% |
The 40% savings is consistent across every pricing tier. For a team running 1,000 coding sessions per day averaging 30K output tokens each, the difference is $300/day or roughly $9,000/month. That is the cost of one junior engineer.
Prompt caching matters more than model choice
When Opus Pulls Ahead
Multi-file refactoring
Renaming a type across 15 files, updating all callsites and tests. Opus maintains consistency better because it can hold the full dependency graph in its reasoning trace.
Architectural decisions
Choosing between event-driven vs request-response, evaluating trade-offs across latency, complexity, and team familiarity. Opus explores more solution paths before committing.
Long-context reasoning
Tasks requiring understanding of 50K+ tokens of existing code. Opus shows less degradation in the 'lost in the middle' range (tokens 30K-100K) compared to Sonnet.
Terminal-Bench style tasks
Autonomous terminal workflows: compiling, configuring servers, debugging system issues. Opus scores 65.4% vs Sonnet's 59.1% on Terminal-Bench 2.0, a 6.3-point gap.
When Sonnet Is Enough
Single-file code generation
Writing a new React component, implementing an API endpoint, generating unit tests. Sonnet matches Opus on these tasks (79.6% vs 80.8% SWE-bench, within noise).
Bug fixes from error messages
Given a stack trace and a file, fix the bug. Both models solve these at near-identical rates. Sonnet does it 17% faster.
High-volume code review
Reviewing PRs, checking for security issues, suggesting improvements. Sonnet's 40% lower cost makes it the clear choice for high-throughput review pipelines.
Interactive coding sessions
Pair programming in an IDE where response time matters. Sonnet's faster output speed (52.8 vs 45.3 tok/s) gives a snappier experience.
Real-World Coding Tests
Benchmarks measure capability. Production coding measures something different: how well the model handles ambiguity, partial context, and iterative refinement. We tested both models on three real coding scenarios from Morph customer workloads.
| Task | Opus 4.6 | Sonnet 4.6 | Notes |
|---|---|---|---|
| Add auth to Express API (3 files) | Completed, 42s | Completed, 38s | Both correct |
| Refactor monolith to services (12 files) | Completed, 4m12s | Completed w/ 1 error, 3m48s | Opus caught a missing import |
| Debug race condition (async/await) | Found root cause | Found root cause | Opus identified it in fewer turns |
| Generate test suite (Jest, 45 tests) | All pass | 44/45 pass | Sonnet missed an edge case |
On simple tasks (auth, test generation), the models performed identically or within noise. On the multi-file refactor, Opus caught a missing import that Sonnet missed. The difference was recoverable in one follow-up turn, but it illustrates where Opus's deeper reasoning pays off.
Using Both with Morph
The optimal strategy is not picking one model. It is using both for what they do best. Morph's routing layer analyzes each coding task and selects the model with the best cost-to-quality ratio.
Simple file edits and code generation go to Sonnet. Multi-file refactoring, architecture decisions, and complex debugging go to Opus. The result is Opus-level quality on hard problems and Sonnet-level costs on easy ones. Teams using this routing pattern typically save 30-50% compared to Opus-only workflows.
Route between Opus and Sonnet automatically
Morph selects the optimal Claude model per coding task. Get Opus quality on complex problems and Sonnet speed on simple ones.
FAQ
Is Claude Opus or Sonnet better for coding?
For most coding tasks, Sonnet 4.6 is sufficient. It scores 79.6% on SWE-bench Verified vs Opus 4.6's 80.8%, a 1.2-point gap, while costing $3/$15 vs $5/$25 per million tokens. Opus pulls ahead on multi-file refactoring, architectural reasoning, and tasks requiring 50K+ tokens of context.
How much cheaper is Claude Sonnet than Opus for coding?
Sonnet 4.6 costs $3 input / $15 output per million tokens. Opus 4.6 costs $5 input / $25 output. That's 40% cheaper on input and 40% cheaper on output. For a typical coding session generating 50K output tokens, Sonnet saves about $0.50 per session.
Which Claude model is faster for coding tasks?
Sonnet 4.6 outputs at 52.8 tokens per second vs Opus 4.6's 45.3 tokens per second (Adaptive Reasoning, Max Effort). Sonnet is approximately 17% faster on raw output speed. Opus has a lower time to first token (12.3s vs Sonnet's 102.4s in reasoning mode), which matters for interactive coding.
What is the SWE-bench gap between Opus and Sonnet?
On SWE-bench Verified, Opus 4.6 scores 80.8% and Sonnet 4.6 scores 79.6%, a gap of 1.2 percentage points. This is the smallest Sonnet-to-Opus gap in any Claude model generation. On Terminal-Bench 2.0, the gap is wider: Opus scores 65.4% vs Sonnet's 59.1%.
Should I use Opus or Sonnet in Claude Code?
Claude Code defaults to Opus 4.6 for its deep reasoning capabilities. For most single-file edits and feature implementation, switching to Sonnet 4.6 saves 40% with minimal quality loss. For complex multi-file refactoring or architectural decisions, Opus is worth the premium.
Does Opus write better code than Sonnet?
On standardized benchmarks, Opus 4.6 edges out Sonnet 4.6 by small margins: 80.8% vs 79.6% on SWE-bench Verified, 65.4% vs 59.1% on Terminal-Bench 2.0. In Anthropic's internal evaluations, engineers preferred Sonnet 4.6 over Opus 4.5 in 59% of head-to-head comparisons, suggesting the practical gap is even smaller than benchmarks indicate.
Can I use both Opus and Sonnet for different coding tasks?
Yes. Many teams route by task complexity: Sonnet for code generation, bug fixes, and single-file changes; Opus for multi-file refactoring, architecture decisions, and tasks requiring deep reasoning. Morph's API routes automatically based on complexity signals, using the optimal model per task.