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# Claude Opus 5.5 vs GPT-6 Sol (2026): when cheaper changes
- URL: https://all-about-ai.ghost.io/comparisons/claude-opus-5-5-vs-gpt-6-sol-2026-when-cheaper-changes/
- Published: 2026-09-23T09:06:26.000Z
- Updated: 2026-09-23T09:06:26.000Z
- Description: Sol halves standard input and output prices, but Opus can cost less on cache-heavy large prompts. Coding quality and supervision still need a paired pilot.
- Author: Frontier Models Desk
- Tags: Comparisons, claude, gpt, anthropic, openai

[GPT-6 Sol](https://all-about-ai.ghost.io/news/openai-launches-gpt-6-sol-at-half-the-previous-api-price) costs half as much as [Claude Opus 5.5](https://all-about-ai.ghost.io/news/claude-opus-5-5-launches-with-lower-prices-and-faster-output) for ordinary API input and output: $2/$10 versus $4/$20 per million tokens. For teams buying AI to edit code or complete office workflows, that makes Sol our starting choice for routine volume. But repeated large prompts can reverse the economics: both models charge $0.20 per million cache-read tokens at standard rates, and Sol raises prices across the entire request above 272,000 input tokens.

Caching lets an application reuse previously processed instructions, documents or code at a discount. Opus keeps its standard rates throughout its million-token context window. Once Sol crosses its threshold, sufficiently heavy reuse can make Opus cheaper even before differences in task completion enter the calculation.

| Direct API comparison                       | GPT-6 Sol                                                                                                | Claude Opus 5.5                                                                |
| ------------------------------------------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
| Standard input / output, per million tokens | $2 / $10                                                                                                 | $4 / $20                                                                       |
| Standard cache reads, per million tokens    | $0.20                                                                                                    | $0.20                                                                          |
| Cache writes, per million tokens            | $2.50 at standard rates                                                                                  | $5 for five minutes; $8 for one hour                                           |
| Above 272,000 input tokens                  | Whole request: $4 input, $0.40 cache reads, $5 cache writes, $15 output                                  | Standard rates throughout long context                                         |
| AutomationBench v1.0.6 strict completion    | 33.2%, XHigh effort                                                                                      | 40.0%, Max effort                                                              |
| AutomationBench average task cost           | $0.27                                                                                                    | $1.28                                                                          |
| Integration constraint                      | Use Responses for reasoning with tools; Chat Completions function calling requires reasoning set to none | Disabled or manually budgeted thinking and forced tool selection return errors |

Prices come from OpenAI’s supplied model documentation and [Anthropic’s pricing documentation](https://platform.claude.com/docs/en/about-claude/pricing?ref=all-about-ai.ghost.io); workflow results come from [Zapier](https://zapier.com/fr/benchmarks?ref=all-about-ai.ghost.io). These are published rates and reported evaluations, not our own model tests. The price comparison excludes tool fees, regional premiums, speed tiers, batch discounts and negotiated rates. The benchmark configurations use different reasoning settings.

For a simple uncached request with 100,000 input tokens and 10,000 billed output tokens, the arithmetic is $0.30 for Sol and $0.60 for Opus. That supports starting with Sol for repeated document processing, bounded code edits and other high-volume work, provided it meets the team’s acceptance standard. It does not establish that either model will consume those quantities on the same task.

The more surprising case is a request with 500,000 cached input tokens, 10,000 uncached input tokens and 10,000 output tokens. With no cache writes on that request, Sol costs $0.39 and Opus costs $0.34\. Those are illustrative calculations from list prices, assuming identical metered token counts and successful cache hits. Sol’s 510,000-token input triggers the surcharge on everything, including output; the higher rates do not apply merely to the portion above 272,000.

The crossover is reproducible. Above Sol’s threshold, let U, C, W and O represent millions of uncached input, cache-read, cache-write and output tokens. With Opus’s five-minute cache writes, Sol costs 4U + 0.40C + 5W + 15O; Opus costs 4U + 0.20C + 5W + 20O. **Opus becomes cheaper when cache-read tokens exceed 25 times output tokens.** At exactly 272,000 input tokens, Sol still uses standard rates.

With Opus’s one-hour cache writes, the condition becomes C > 15W + 25O. Actual decisions need each provider’s measured token counts, cache-hit behavior and retries: the same source material may tokenize differently. For cache-heavy large prompts, we would compare full session bills before adopting Sol on the strength of its headline discount.

Zapier offers a separate reason to distinguish token prices from useful work. Its AutomationBench v1.0.6 reports that Opus 5.5 at Max effort completed 40.0% of tasks under its strict all-requirements test, against 33.2% for GPT-6 Sol at XHigh. Opus’s advantage is 6.8 percentage points, with an average task cost of $1.28 against Sol’s $0.27.

Dividing average task cost by strict completion rate gives approximately $3.20 per expected strict pass for Opus and $0.81 for Sol. These are our calculations from aggregate figures, not observed bills for individual successful tasks. They exclude review time, latency, correlated retry failures and differences in the value of completed tasks. Within this test, Sol has the stronger cost case; Opus completes more workflows outright.

The [benchmark’s scope](https://arxiv.org/abs/2604.18934?ref=all-about-ai.ghost.io) is autonomous business workflows across simulated applications. Its current evaluation covers more than 600 held-out tasks across six business domains and 47 apps, with no human clarification. Deterministic checks establish whether every required state change occurred. That makes the result relevant to cross-app operations, but it does not measure repository coding, live customer deployments or every permission and data-quality problem in production software.

For long autonomous coding, **no matched current-model marathon-coding test in the consulted public evidence establishes a universal winner**. Anthropic’s selected examples of multi-hour migrations and audits make Opus worth piloting, but lack matched GPT-6 Sol runs. [FrontierCode](https://cognition.com/frontiercode?ref=all-about-ai.ghost.io) added both models on September 22 and tests whether maintainer-defined repository changes are mergeable. That answers a narrower question than whether an agent can reliably handle an overnight engineering assignment.

We would put Opus into a paired pilot for sprawling migrations or investigations, with Sol as the cost baseline. Give both the same repository issues, tools, permissions and time cap, with comparable resource budgets rather than assuming equally named effort settings are equivalent. Count abandoned attempts and reviewer rework alongside accepted patches, total cost and wall time. A cheaper run that leaves an engineer untangling its changes can lose the purchasing decision.

Switching costs also belong in that pilot. Anthropic’s developer guide says Opus 5.5 requires adaptive thinking controlled through effort, and rejects forced selection of any tool or a named tool. Automatic selection with strict schemas can constrain tool arguments, but does not guarantee a tool call. An application that depends on forced execution needs a revised control flow.

Opus’s reasoning blocks are also bound to their model and conversation. Some model switches discard earlier reasoning; replaying blocks after changing preceding instructions or tools can fail under preserved-thinking enforcement, which differs by account age. Older computer-use integrations need migration on the Claude API and Google Cloud, while Bedrock still accepts the earlier tool. For Sol, teams combining reasoning and tool calls should use Responses; its Chat Completions function calling requires reasoning effort set to none.

Neither vendor’s safety evaluations justify handing either model unrestricted authority. Anthropic reports improved broad behavior alongside remaining failures involving pasted malicious instructions and unverifiable authorization. OpenAI reports fewer severe misalignment flags for Sol in simulated internal Codex work, while exfiltration flags increased. The tests differ and cannot identify a safer model overall.

Our deployment recommendation is the same for both: isolate execution, issue narrowly scoped short-lived credentials, restrict network destinations and writable paths, and retain logs the agent cannot alter. Enforce human approval for merges, deployments, publishing, exports and material spending in the application. Model judgment and vendor safeguards provide additional protection; neither establishes permission to act.

For routine volume, we would start with Sol and require Opus to earn its premium through more accepted work or less supervision. For large prompts dominated by cache reads, price Opus directly against Sol’s surcharge. For overnight coding, make the purchasing commitment after the paired pilot: the decisive result is what your team accepts, at what total cost, under permissions you can enforce.