Claude Opus vs Sonnet: which is better for coding?

As of November 2026, Sonnet 5.5 is the cheaper default for coding agents: it cost $0.59 per solved task in Requesty's benchmark, versus $1.01 for Opus 5.5. Opus solved 92.2% of 129 backend tasks against 87.6% for Sonnet, with its biggest advantage on extra hard tasks.

Opus vs Sonnet at a glance

At Anthropic's November 2026 list prices, Opus 5.5 costs twice as much as Sonnet 5.5 for input, output and cache reads. A cache read reuses input that has already been processed, a common pattern in agent loops that resend the same context on successive turns.

Sources: Anthropic pricing, Anthropic's Sonnet 5.5 model overview, and the Requesty coding benchmark.

The context and output limits are identical. The thinking controls differ: you cannot disable thinking on Opus 5.5, while Sonnet 5.5 has a between_tools setting that turns off up-front thinking at high effort or below (Opus thinking behavior, Sonnet model overview).

For the Opus price cut and API changes, see Claude Opus 5.5 explained.

What we measured

Our coding benchmark puts every model in the same agent, with bash, read, edit and write tools and an 80-turn budget. The 129 private tasks come from our Go backend and run from easy to extreme difficulty.

Opus finished first among 25 models, solving 92.2% of tasks. Sonnet ranked fourth with 87.6%, but passed a slightly higher share of individual tests: 96.8% versus 96.4% for Opus.

All 129 tasks come from one backend, so use the results as a starting point for your own evaluation. The Requesty coding benchmark has the full method and results for all 25 models.

The largest gap is on extra hard tasks

Opus's overall lead is six tasks. The difficulty breakdown shows where those extra solves sit.

Source: Requesty coding benchmark.

Opus solved 26 of the 30 extra hard tasks, while Sonnet solved 21. On the 18 extreme tasks, Opus solved 14 and Sonnet solved 12. Sonnet picked up one more easy task, and the models tied on medium and hard tasks.

That makes your task mix part of the cost calculation. A workload dominated by well-scoped fixes could produce a different cost and solve-rate gap from one dominated by difficult backend changes.

What does an extra solve cost?

Take a team running 1,000 coding tasks per month. Assume those tasks match the benchmark's difficulty mix. The benchmark costs include retries, with cache hit rates of 100% for Opus and 99.2% for Sonnet.

Those solve rates give Opus 1,000 × 92.2% = 922 solves and Sonnet 1,000 × 87.6% = 876. Applying the measured costs per task gives 1,000 × $0.93 = $930 for Opus and 1,000 × $0.52 = $520 for Sonnet.

Source: Requesty coding benchmark.

Opus solves an estimated 46 extra tasks and costs $410 more. Divide $410 by 46 and each additional solved task costs about $8.91.

That gives a break-even test. If each extra solved task avoids more than $8.91 of review, rework or human takeover, Opus has the lower total cost under these assumptions. Below that threshold, Sonnet has the lower total cost. Your task mix and cache hit rate will change the result.

For controls on spending after you choose a default, see our guide to managing coding agent costs across a team.

Sonnet is faster per turn

Sonnet generated output faster per turn in our benchmark. Even so, the two models averaged the same time per task.

Source: Requesty coding benchmark. Minutes per task include tool execution and agent overhead, not only model generation time.

Production latency uses another measure: time to first token, or the wait before a response starts. Our live latency rankings show the current median for each model. Test full task time in your own agent alongside that first-token wait.

Public benchmarks give a mixed result

Our backend benchmark is one measure of coding performance. Other suites order the models differently, including a Sonnet lead on Terminal-Bench 4.0.

Sources: Terminal-Bench and GDPval-AA results, SWE-Bench Pro figures, and Artificial Analysis.

Anthropic says Opus remains stronger on complex, open-ended work requiring sustained judgment in its own and external testing. It positions Sonnet as a lower-cost model for well-scoped work across coding and agents (Anthropic's Sonnet 5.5 announcement).

What developers route through Requesty

In our production rankings for September 7 to November 9, 2026, Opus 5.5 had a larger share of tokens and spend than Sonnet 5.5. Opus launched on September 22, six days before Sonnet.

The charged cost per million tokens includes cache discounts and reflects each model's production traffic mix. It is not a fixed rate for a new workload.

Adding the Opus 5.5, 5, 4.8 and 4.7 rows gives 11.5% of tokens and 35.8% of spend. The Sonnet 5, 5.5 and 4.6 rows sum to 7.8% of tokens and 18.4% of spend.

Our April 2026 coding agent data note looks at the tools from another angle: Claude's share of spend within each one. It covers Claude models as a group in April 2026, before the 5.5 releases, and measures share of spend.

Which should you use?

Run both through one API

Requesty has both models behind the same OpenAI-compatible API. Use anthropic/claude-sonnet-5-5 for Sonnet and anthropic/claude-opus-5-5 for Opus (Sonnet model page, Opus model details).

1. 1
   Set the base URL

   Point your OpenAI-compatible client at https://router.requesty.ai/v1 and use your Requesty API key. Switch the model ID to compare Sonnet and Opus with the same integration.

2. 2
   Choose a routing policy

   Requesty's fallback policies support starting with a cheaper model and falling back to a premium model when needed. For automatic selection, set up an Auto Routing policy with routes for your task types.

3. 3
   Measure cost against results

   Use your task tests to count solves, then compare them with Requesty's cost analytics. Set API limits to keep the evaluation within budget.

Auto Routing chooses a route from the first user message and pins that choice for 60 minutes per conversation using trace_id, so prompt caching can keep working. For a comparison beyond Anthropic, see the wider field of coding models.