GPT-5.6 vs Claude Sonnet 5 In-Depth Selection Guide: Coding Agents Need More Than Token Prices

Coding Agent evaluations must align the model and harness

In the Artificial Analysis Coding Agent Index results published with GPT-5.6, Sol, Terra, and Luna scored 80, 77, and 75 in the Codex harness. The index combines DeepSWE, Terminal-Bench, and SWE-Atlas-QnA to cover code changes, terminal work, and repository questions.

Independent Sonnet 5 testing showed gains of nine points over Sonnet 4.6 on Terminal-Bench v2.1 and seven points on SciCode. On knowledge-work evaluations such as AA-Briefcase and GDPval-AA, it can approach or exceed Opus 4.8. Anthropic also positions the model for sustained multi-step changes and self-verification inside Claude Code.

The results do not form a universal leaderboard. Coding Agent performance depends on the model, reasoning effort, harness, tool permissions, context compaction, and test budget. Directly comparing a GPT-5.6 result in Codex with a Sonnet result in Claude Code risks attributing harness differences to the model.

How should the full cost of a successful coding task be calculated?

A team can replace the simple input-plus-output calculation with the following measure:

Total cost per successful task =
(input tokens + cache writes + cache reads + output tokens
 + tools and sandbox + failed retries + human review time)
 / tasks that pass final acceptance

Four measurements are often missed.

• Success rate: A model can generate a patch while failing tests or misunderstanding the requirement. The bill still exists.
• Human interventions: Every context correction, plan repair, and command approval consumes engineering time.
• End-to-end duration: Fast token generation does not guarantee a fast workflow. Reasoning time and repeated tool calls may dominate the wait.
• Post-generation rework: A model that writes more code may still increase review and rollback. Track the percentage of generated code that survives final review.

Starting with GPT-5.6, OpenAI charges cache writes at 1.25 times the uncached input price, while cache reads retain a 90% discount. Anthropic also separates cache-write and cache-hit pricing. On long repositories with repeated context, cache policy can materially change the bill.

GPT-5.6 Sol targets complex tasks with high failure costs

GPT-5.6 Sol fits work where failure is expensive and sustained reasoning matters: cross-module refactors, production-incident diagnosis, complex migrations, long code reviews, and tasks that must deliver code, tests, and documentation together.

Its independent results are strongest at high reasoning effort, on the Coding Agent Index, and in task-level token efficiency. A team willing to pay a higher token rate for fewer retries and a more complete handoff may spend less overall with Sol than with a cheaper model.

Sol still needs a budget ceiling. Running every low-complexity batch task at maximum effort turns unused capability into extra cost and latency.

How should GPT-5.6 Luna and Terra be divided?

GPT-5.6 Luna fits frequent tasks with automatic acceptance tests: adding tests, fixing formatting and type errors, updating dependencies, generating migration scripts, and processing small tickets in bulk. At maximum effort it scored 51 on the Artificial Analysis Intelligence Index, close to Sonnet 5's 53, while costing $0.21 per task in that benchmark.

GPT-5.6 Terra occupies the middle tier. Independent analysis found that it did not consistently sit on the intelligence-cost frontier because a higher-effort Luna or lower-effort Sol could offer more capability at similar cost. Terra can still serve as an operational default when a team wants one middle configuration, task difficulty is predictable, or a product plan already exposes Terra by default. Lower routing complexity has operational value.

Claude Sonnet 5 extends an existing Claude Code workflow

Claude Sonnet 5 is attractive to teams that already built permissions, hooks, project conventions, and review flows around Claude Code. Migrating the harness carries engineering cost, and a stable existing workflow can outweigh a temporary leaderboard gap.

Its promotional price is $2 per million input tokens and $10 per million output tokens through August 31. That window is useful for running real-repository evaluations. Output volume and agentic turns should be measured separately so a low token rate is not mistaken for a low task cost. Budget models must switch to the standard $3 / $15 rate after the promotion.

The model also targets brownfield workflows that rely on Claude Code behavior: navigating an old repository, following local conventions, adding tests, and validating a change. Much of this evidence comes from Anthropic and early customers, so procurement decisions still need an internal task set.

A 20-task internal model evaluation

A practical internal evaluation can start with 20 historical tasks in five groups:

1. Four well-scoped small fixes to test low-cost reliability.
2. Four cross-file defects to test diagnosis, implementation, and regression tests.
3. Four legacy refactors to test convention following and scope control.
4. Four terminal or browser tasks to test permissions and failure recovery.
5. Four long-horizon deliveries requiring code, tests, documentation, and verification records.

Every model should receive the same repository snapshot, tool permissions, acceptance tests, and time limit. Reasoning effort must be recorded; a Sol max run cannot be grouped with a Sonnet low run. Collect success rate, total tokens, cache fees, tool turns, wall-clock time, human interventions, and the percentage of code retained after review.

The result will often support tiered routing. Luna handles cheap tasks with automatic acceptance, while Sol takes high-risk and high-complexity work. Teams deeply invested in Claude Code can keep Sonnet 5 as the primary model and use the same task set to decide whether selective routing is worthwhile. Repository failure rate, rework, and total spend decide the deployment; public leaderboards supply the shortlist.