GPT-6 Sol vs Claude Opus 5.5 for coding and API cost

GPT-6 Sol has lower ordinary input and output rates than Claude Opus 5.5 in the direct-API comparison below. That alone does not tell you which will finish your coding task for less. The useful comparison is the cost and review effort required to reach the same accepted result.

These models are a reasonable pair to evaluate for daily coding work because both vendors position them for coding and agent workflows. This is an editorial comparison based on current documentation, not a claim that they occupy identical positions in every benchmark or product lineup. The sources were checked September 23, 2026; no new paid head-to-head benchmark was performed for this article.

Compare a like-for-like rate card

The table uses USD per million tokens. Sol is direct OpenAI Standard processing with input at or below 272K tokens. Opus is direct Anthropic standard pricing, with the five-minute cache-write option shown separately. Providers, cache duration and processing modes remain different product choices.

Sources: OpenAI’s model page and Anthropic pricing. Sol’s ordinary input/output rates are half Opus’s here, while the cache-read rates are equal. Saying “Sol is half the price in every category” would be incorrect.

Opus also offers a one-hour write rate; OpenAI’s caching rules differ. Sol’s long-input threshold changes the full-request rates. The dedicated Sol cost guide and Opus cost guide cover those details. Never compare one vendor’s discounted processing mode against another’s standard mode without labeling the difference.

See how cache mix changes the result

Using the same assumed token counts for each model—20,000 ordinary input, 60,000 cache reads, 20,000 cache writes and 5,000 output—gives $0.152 for Sol and $0.292 for Opus using the write options above. Sol is about 47.9% less expensive in this arithmetic example, rather than exactly 50%, because the read rate is the same.

The assumptions do not guarantee identical cache eligibility or hit behavior across providers. They isolate rate differences. The downloadable cost calculator makes no model call and does not measure either model’s token efficiency.

For a simpler uncached assumption of 20,000 input and 5,000 output tokens, the totals are $0.09 and $0.18. Real coding agents may consume different token counts, perform different tool actions and require different repairs. Replace the assumed categories with recorded usage before drawing a spending conclusion.

What the public benchmarks can and cannot settle

OpenAI’s launch report and Anthropic’s Opus 5.5 report describe their own evaluation results and comparisons. Read the version, effort, tool setup and limitations beside a score. An older Opus 5 result is not an Opus 5.5 result; a GPT-5.6 Sol row is not GPT-6 Sol.

Do not combine the best figure from one harness with a figure from another and call the result a controlled comparison. Different effort settings, fallback models and task definitions can change the meaning of a score. A manufacturer’s result can help select a task to investigate; it cannot establish the outcome on your private repository.

For the same reason, one forum user’s preference is useful context but not proof of general model capability. This article does not turn community opinions into a performance table.

Run a comparison you can actually review

Use the blank acceptance sheet. Choose one reproduced bug, one bounded refactor and one small feature with an acceptance test. These are suggested categories, not tests we claim to have run.

1. Start each attempt from the same repository commit and provide the same task materials.
2. Record the exact model ID, provider, endpoint, effort and tool permissions.
3. Define which behavior must pass and which files may change before viewing the result.
4. Record failed attempts, total billed usage, elapsed time and manual interventions.
5. Review correctness and scope, not merely whether the model announces completion.

If your actual choice is Codex versus Claude Code, evaluate that complete product setup and label it accordingly. A product comparison includes harness behavior, tools and instructions. It cannot isolate a model difference by simply changing the model name in the title.

Which should you evaluate first?

If you already use Codex and ordinary API token cost is the main constraint, Sol is a sensible first candidate to evaluate. If your team already relies on Claude Code, start with Opus 5.5 in that existing workflow, then compare an alternative against the same acceptance criteria. This recommendation minimizes setup changes; it does not declare a universal coding winner.

Use the Sol/Luna/Astra guide for the OpenAI family choice and Claude Code setup guide to identify the Anthropic route. When using a gateway, follow the routing and billing checks before treating its invoice as either vendor’s direct price.