Sonnet 4.6 vs Opus 4.6: When to Pay 5x More (March 2026)

Summary

These two models come from the same training pipeline and share the same safety features, tool use capabilities, and API interface. The difference is how much compute they spend per request. Opus thinks harder, charges more, and gets slightly more right on the hardest problems. Sonnet is the model you default to, switching to Opus only when the task demands it.

Stat Comparison

Side-by-side performance across the dimensions that affect daily coding work, rated on a 5-bar scale.

Benchmark Deep Dive

Both models from the same family, trained on the same data. The benchmark gaps come from how much inference compute each model allocates.

SWE-bench Verified: 1.2 Points Apart

Sonnet scores 79.6%. Opus scores 80.8%. The 1.2-point gap is real but narrow. Both models solve the same broad category of GitHub issues. Where they diverge is on the tail: issues requiring multi-step reasoning across several files, where Opus's thinking traces give it an edge.

On SWE-bench Pro, the gap widens. Opus scores 55.4%, Sonnet closer to 53%. The harder the problem set, the more Opus's extra compute pays off. This pattern is consistent across every benchmark.

MATH 500: The Largest Gap

Opus scores 96.4% on MATH 500 vs Sonnet at 90.6%. A 5.8-point gap. Competition-level math requires the kind of step-by-step reasoning that Opus's thinking traces are built for. If your work involves mathematical proofs, algorithm analysis, or formal verification, Opus is measurably better.

HumanEval: Near-Identical

Sonnet: 96.4%. Opus: 97.6%. A 1.2% gap on a saturated benchmark. For standard function-level code generation, both models are effectively equivalent. The choice between them should not rest on HumanEval scores.

Speed and Latency

Sonnet is the faster model. The gap matters for interactive coding where you are waiting on the response.

Why Opus is Slower

Opus generates hidden reasoning traces before streaming visible output. This "thinking pause" pushes time-to-first-token to 7.83 seconds on average. The pause is not wasted time. It is the model working through the problem before committing to an answer. On easy tasks, this is overhead. On hard tasks, it prevents wrong first attempts.

Interactive vs Batch

For interactive coding (playground, copilot-style suggestions), Sonnet's speed advantage is significant. You feel the difference between 2 seconds and 8 seconds to first token. For batch workloads (automated code review, CI/CD pipelines), latency matters less and you can use Opus's batch API at 50% discount.

Pricing Breakdown

Both models share the same pricing structure with different rates. The math is straightforward.

Cost Per Task

On a typical coding task generating 2,000 output tokens with 10,000 input tokens, Sonnet costs roughly $0.06 per request. Opus costs roughly $0.10 per request. The 67% premium is real but modest in absolute terms at low volume.

At scale, the difference compounds. An engineering team making 10,000 API calls per day pays roughly $600/day on Sonnet vs $1,000/day on Opus. Over a month, that is $12,000 vs $20,000. The $8,000 monthly difference buys a lot of compute.

Subscription Pricing

When to Use Sonnet 4.6

When to Use Opus 4.6

Routing Between Both via Morph

The optimal strategy is not choosing one model. It is routing each task to the model that handles it best.

The 80/20 Split

Most engineering teams find that roughly 80% of their coding tasks are implementation work where Sonnet's speed and cost advantage wins. The remaining 20% are complex reasoning tasks where Opus's accuracy advantage is worth the premium. Manually switching between models for every request is friction nobody needs.

# Morph routes to the right model automatically
# Implementation task → Sonnet 4.6 (fast, cheap)
response = client.chat.completions.create(
    model="morph-v3-fast",
    messages=[{"role": "user", "content": "Add input validation to the /api/users endpoint"}]
)

# Complex reasoning task → Opus 4.6 (accurate, thorough)
response = client.chat.completions.create(
    model="morph-v3-fast",
    messages=[{"role": "user", "content": "Refactor the auth module from cookies to JWT across all 40 route handlers"}]
)

# Same API endpoint. Morph detects complexity and routes accordingly.
# Result: Sonnet speed on simple tasks, Opus accuracy on hard ones.

Frequently Asked Questions

Is Sonnet 4.6 or Opus 4.6 better for coding?

Sonnet handles most coding tasks at 79.6% SWE-bench Verified accuracy, 1.7x faster, at 40% less cost. Opus wins on hard multi-file reasoning, scoring 80.8% on SWE-bench Verified and 96.4% on MATH 500 (vs Sonnet's 90.6%). Default to Sonnet; switch to Opus for complex reasoning.

How much cheaper is Sonnet 4.6 than Opus 4.6?

Sonnet costs $3/$15 per million tokens (input/output). Opus costs $5/$25. Sonnet is 40% cheaper on both input and output. With prompt caching, Sonnet drops to $0.30/1M cached input tokens vs Opus at $0.50/1M.

How fast is Sonnet 4.6 compared to Opus 4.6?

Sonnet: ~80 tok/s output, 2-3s TTFT. Opus: ~46 tok/s output, 7.83s average TTFT. Sonnet is 1.7x faster on output speed and 3-4x faster to first token. The TTFT gap is because Opus generates hidden reasoning traces before responding.

Do they have the same context window?

Both default to 200K tokens. Opus has a 1M token context window in beta at premium pricing ($10/$37.50 per 1M tokens). Sonnet does not. If your use case requires more than 200K tokens of context, Opus is the only option in the Claude family.

When should I use Opus over Sonnet?

Multi-file refactoring across 20+ files, architectural decisions, codebases exceeding 200K tokens, mathematical reasoning, and strict instruction following. On these tasks, Opus's first-pass accuracy saves more in retry cycles than it costs in compute.

Can I switch between them via API?

Yes. Same API, same format. Change the model parameter: claude-sonnet-4-6 or claude-opus-4-6. Morph's API routes between them automatically based on task complexity.