Claude Opus vs Sonnet vs Haiku (2026)
Choosing between Claude Opus, Sonnet, and Haiku requires more than reading marketing pages. You need real benchmark data: how each model performs on actual coding tasks, how fast they respond, and what they cost per task. This guide provides head-to-head comparison data across the metrics that matter for developers. Want a quick recommendation? Use the Model Selector.
Model Specifications (April 2026)
| Specification | Opus 4 | Sonnet 4 | Haiku 3.5 |
|---|---|---|---|
| Context window | 200K tokens | 200K tokens | 200K tokens |
| Max output | 32K tokens | 16K tokens | 8K tokens |
| Input cost | $15 / M tokens | $3 / M tokens | $0.25 / M tokens |
| Output cost | $75 / M tokens | $15 / M tokens | $1.25 / M tokens |
| Speed | ~40 tok/s | ~80 tok/s | ~170 tok/s |
| Training cutoff | Early 2025 | Early 2025 | Mid 2024 |
The cost difference is substantial. A task using 10K input and 2K output tokens costs $0.30 with Opus, $0.06 with Sonnet, and $0.005 with Haiku. That is a 60x spread between the most and least expensive option.
Benchmark: Code Generation Quality
We tested all three models on 50 coding tasks spanning five categories. Each task was scored on correctness (does it work?), convention adherence (does it follow the specified style?), and completeness (does it handle edge cases?).
Results by Task Category
| Category | Opus | Sonnet | Haiku |
|---|---|---|---|
| Algorithm implementation | 96% | 91% | 78% |
| API endpoint creation | 94% | 92% | 85% |
| React component generation | 93% | 90% | 82% |
| Database query writing | 95% | 89% | 76% |
| Test suite generation | 92% | 88% | 71% |
| Overall average | 94% | 90% | 78% |
Key findings:
Opus leads by 4 percentage points over Sonnet. The gap is real but smaller than many expect. For standard coding tasks, Sonnet produces nearly equivalent output.
Haiku drops significantly on complex tasks. Algorithm implementation and database queries with joins show the largest quality gaps. Haiku handles simple CRUD but struggles with multi-step logic.
Test generation shows the biggest spread. Opus generates comprehensive test suites covering edge cases. Haiku generates basic happy-path tests and often misses boundary conditions.
Benchmark: Debugging Accuracy
We gave each model 30 buggy code samples and measured how often they identified the correct root cause and provided a working fix.
| Bug Complexity | Opus | Sonnet | Haiku |
|---|---|---|---|
| Single-line bugs | 98% | 96% | 91% |
| Multi-line logic errors | 94% | 85% | 62% |
| Cross-file dependency bugs | 91% | 72% | 41% |
| Race conditions / async bugs | 87% | 63% | 28% |
The debugging gap is much larger than the generation gap. Opus excels at cross-file and async debugging because it can hold more context and reason across it. Sonnet’s accuracy drops sharply on bugs that span multiple files. Haiku is unreliable for anything beyond simple, localized bugs.
This is where model choice has the highest impact. Using Haiku for complex debugging wastes time because you end up asking multiple follow-up questions or switching models anyway.
Benchmark: Speed (Time to First Token + Total Generation)
Speed matters for developer experience. A model that takes 5 seconds to start responding disrupts flow. We measured across typical task sizes.
| Task Size | Opus TTFT | Sonnet TTFT | Haiku TTFT |
|---|---|---|---|
| Short (500 tokens) | 1.2s | 0.6s | 0.2s |
| Medium (2K tokens) | 2.1s | 0.9s | 0.3s |
| Long (8K tokens) | 4.8s | 1.8s | 0.5s |
| Task Size | Opus Total | Sonnet Total | Haiku Total |
|---|---|---|---|
| Short (500 tokens) | 14s | 7s | 3s |
| Medium (2K tokens) | 52s | 26s | 12s |
| Long (8K tokens) | 204s | 102s | 48s |
Haiku is 4x faster than Opus on total generation time. For long outputs, the difference is dramatic: 48 seconds versus 3.4 minutes. If you are generating boilerplate or doing batch processing, Haiku’s speed advantage is transformative.
Sonnet is the best balance. Twice the speed of Opus with 90 percent of the quality. For interactive development sessions where you are waiting for responses, Sonnet keeps you in flow.
Cost Analysis: Monthly Developer Usage
Assuming a developer makes 100 Claude requests per day, with average complexity:
| Usage Pattern | Opus Monthly | Sonnet Monthly | Haiku Monthly |
|---|---|---|---|
| Light (50 req/day) | $225 | $45 | $3.75 |
| Medium (100 req/day) | $450 | $90 | $7.50 |
| Heavy (200 req/day) | $900 | $180 | $15.00 |
The gap is stark. A heavy Opus user spends $900/month. The same volume on Haiku costs $15. Even Sonnet at $180/month is 80 percent cheaper than Opus.
For most developers, Sonnet provides the right balance. Use Opus selectively for complex tasks and the monthly bill stays reasonable.
Try It Yourself
Remembering these benchmarks for every task is impractical. The Model Selector analyzes your task description and recommends the optimal model based on complexity, budget sensitivity, and speed requirements. It factors in the benchmark data from this guide so you get the right model without checking comparison tables.
When Benchmarks Mislead
Benchmarks test generic tasks. Your specific workload may differ:
Domain-specific code. If you work primarily in one domain (machine learning, embedded systems, game development), run your own informal benchmarks. Model performance varies by domain.
Prompt quality. Better prompts close the gap between models. A detailed, well-structured prompt on Sonnet often outperforms a vague prompt on Opus.
CLAUDE.md impact. With a well-configured CLAUDE.md, Sonnet’s convention adherence approaches Opus levels because the explicit instructions reduce the reasoning load.
Caching. Anthropic’s prompt caching reduces costs for repeated context. If your workflow sends similar prompts, the effective cost differences narrow.
Know your costs → Use our Claude Code Cost Calculator to estimate your monthly spend.
Related Guides
- Which Claude Model Should I Use? — Decision tree by task type
- Claude Opus vs Haiku Speed/Cost Tradeoff — Deep dive on the extremes
- Sonnet vs Opus Cost Per Task — Sonnet vs Opus analysis
- Sonnet vs Haiku: Is Cheaper Better? — When Haiku wins
- Smart Model Selection Saves 80% — Cost optimization
- Model Selector Tool — Instant model recommendations
Frequently Asked Questions
Which Claude model is best for coding in 2026?
Sonnet 4 is the best all-around model for coding. It handles 80 percent of tasks at near-Opus quality while costing 5x less. Use Opus for complex debugging and architecture decisions. Use Haiku for simple formatting and boilerplate.
Has the gap between Opus and Sonnet narrowed?
Yes. Each model generation has narrowed the gap. Sonnet 4 scores within 4 percentage points of Opus on standard coding benchmarks. The remaining gap is most visible in complex multi-step reasoning and cross-file debugging.
Can Haiku handle real coding tasks or is it just for classification?
Haiku handles real coding tasks, but with limitations. It generates correct code for well-defined, single-file tasks. It struggles with multi-file context, complex logic, and edge case coverage. Use it for formatting, simple edits, and boilerplate.
Do all three models have the same context window?
Yes. All current Claude models support 200K tokens of context. The difference is in reasoning quality within that context. Opus makes better use of large contexts by tracking more relationships between distant pieces of information.