GPT-6 Sol vs Claude Opus 5.5: Price Is Not the Whole Story
The GPT-6 Sol vs Claude Opus 5.5 comparison starts with a striking price difference: Sol costs half as much per API token. That sounds decisive until you look at the work each model completes. On Zapier’s current AutomationBench leaderboard, Opus 5.5 Max completes more workflow tasks, while Sol at xhigh costs much less per task. The useful question for an engineering team is not “Which model is cheaper?” It is “Which model completes our real task reliably, at what total cost, and with how much human rework?”
This comparison separates token prices from benchmark results, checks the claims in the launch coverage, and builds a practical way to test both models on your own work. The short version: Sol is compelling for cost-sensitive, repeatable workloads; Opus 5.5 earns consideration when its stronger benchmark score on a target task justifies the extra spend. Neither result makes a universal winner.
Research checked on 23 September 2026. API prices and benchmark leaderboards change. Treat the examples below as a dated snapshot, and verify current prices before budgeting.
GPT-6 Sol vs Claude Opus 5.5: what the “9% of the cost” claim actually says
OpenAI’s GPT-6 Sol launch announcement reports that Sol at xhigh effort scored 33.2% on AutomationBench at $0.27 per task. The same table compares it with Claude Opus 5 at max effort: 26.9% at a cost 11.1 times higher. OpenAI’s “9% of the cost” statement is about Opus 5, not Claude Opus 5.5. Those are different model versions, so that headline is not evidence that Sol beats Opus 5.5 at one-tenth the cost.
The distinction matters because Anthropic’s newly published Opus 5.5 results use Zapier’s own early-access evaluation. The current AutomationBench leaderboard lists Opus 5.5 Max at 40.0% and $1.28 per task. It lists Sol xhigh at 33.2% and $0.27. Those rows show a real trade-off: Opus 5.5 Max has the higher score, while Sol’s listed task cost is about 79% lower. They also use different reasoning effort settings, so they are not a controlled same-effort duel.
A closer published effort-level comparison is Opus 5.5 xhigh at 34.4% and $0.80 per task against Sol xhigh at 33.2% and $0.27. The scores are closer, but the benchmark does not prove the models behave identically on your company’s workflows. The cost figures are benchmark-specific, not a universal price guarantee.
| AutomationBench configuration | Score | Cost per task | What it suggests |
|---|---|---|---|
| GPT-6 Sol, xhigh | 33.2% | $0.27 | Low published task cost, lower overall score than Opus 5.5 Max |
| Claude Opus 5.5, xhigh | 34.4% | $0.80 | Similar score in this snapshot, higher cost |
| Claude Opus 5.5, max | 40.0% | $1.28 | Highest of these three scores, highest cost |
Source: Zapier AutomationBench 1.0.6 leaderboard, checked 23 September 2026. Opus 5.5’s results were produced by Zapier during early access; the leaderboard and Anthropic announcement identify their respective measurement sources. Effort labels are not identical measures of compute or quality between providers.
GPT-6 Sol vs Claude Opus 5.5: token prices are only the first calculation
At standard short-context API rates, GPT-6 Sol costs $2 per million uncached input tokens and $10 per million output tokens. Claude Opus 5.5 costs $4 per million input and $20 per million output. That makes Sol 50% cheaper per token in both directions at these list prices. Opus 5.5 is itself 20% cheaper per token than its predecessor Opus 5, according to Anthropic, but that does not make it cheaper than Sol.
Here is a simple, reproducible example. Suppose an agent call uses one million input tokens and generates 250,000 output tokens, with no cache discount and no other fees:
- GPT-6 Sol: (1 × $2) + (0.25 × $10) = $4.50.
- Claude Opus 5.5: (1 × $4) + (0.25 × $20) = $9.00.
This illustrative request costs half as much on Sol at listed rates. But a model can spend more tokens, make more tool calls, need extra retries, or require more human correction. Those factors can erase or widen the gap. For production budgeting, record the whole run: input and output tokens, cache writes and reads, tool charges, retries, latency, and accepted result quality.
Caching adds another wrinkle. OpenAI lists Sol cached input reads at $0.20 per million tokens, a 90% discount from its $2 short-context input rate. Anthropic lists Opus 5.5 cache reads at $0.20 per million too, alongside $4 uncached input. A heavily reused prompt prefix can therefore make cached reads equally priced in absolute terms, even though uncached input is not. Cache writes, retention windows, cache-hit rates, and long-context rules also affect the bill, so compare the provider’s full cache behavior rather than only the read price.
The surprising result: cost per successful task can change the ranking
AutomationBench reports both score and cost per task. Dividing cost by the reported completion rate gives a rough “cost per successful task” estimate, provided you read it as a simplifying calculation rather than an official leaderboard metric:
- Sol xhigh: $0.27 ÷ 0.332 ≈ $0.81 per success-equivalent.
- Opus 5.5 xhigh: $0.80 ÷ 0.344 ≈ $2.33.
- Opus 5.5 max: $1.28 ÷ 0.40 = $3.20.
This rough calculation makes Sol look especially efficient in that benchmark snapshot. Yet it can mislead if the score includes partial completion, tasks differ in value, failed tasks still provide useful work, or a real workflow needs multiple attempts. Measure the quantity that matters to your business: cost per accepted invoice processed, cost per merged code change, or cost per correctly resolved support case.
There is another important signal in the raw score: 33.2% is not “the agent reliably automates a third of your company.” AutomationBench contains end-to-end workflows across six functions, and its score applies to its own simulated tasks, tools, and fixed success criteria. The leaderboard says it uses more than 600 held-out tasks, 47 simulated apps, and around 500 API endpoints. That breadth makes it more interesting than a trivia quiz, but it is still one benchmark with one tested environment.
What the benchmark does well, and what it cannot tell you
Zapier describes isolated environments containing CRM records, emails, calendars, and other business data. It evaluates the final state against fixed criteria rather than asking another language model to judge how convincing the agent sounded. This is a useful design choice: an agent that claims “I booked the meeting” does not pass unless the benchmark environment actually contains the booking.
Still, a score is conditional on its harness. Tool descriptions, available actions, prompt design, retries, model settings, and safety behavior can all change results. Anthropic notes that its Opus 5.5 AutomationBench evaluation was run by Zapier during early access and without fallback models; safety refusals counted as failures. Its launch announcement also says some benchmark margins are less predictive of real-world differences at this capability level. OpenAI likewise warns that its research or API evaluations may differ from production ChatGPT because system prompts and available tools can differ.
Read the numbers as evidence about tested setups, not as a guarantee about your stack. In particular, compare equal task sets, equivalent tool permissions, consistent retry budgets, and comparable reasoning settings whenever you run your own trial.
GPT-6 Sol vs Claude Opus 5.5 on coding: the direct comparison is incomplete
It is tempting to turn this into a simple coding contest. The available public evidence does not support a clean, current head-to-head across the same coding suite. Anthropic reports Opus 5.5 at 66.4% on Terminal-Bench 4.0, 54.4% on FrontierCode v1.1 Main, and 57.8% on CursorBench 4.0. Its comparison table includes GPT-6 Astra on some tests and GPT-5.6 Sol on others, but does not provide a GPT-6 Sol result for those rows. It would be misleading to fill that gap with an unrelated AutomationBench score.
Anthropic’s scores also have different effort and evaluation notes. For Terminal-Bench, Opus 5.5 is reported at xhigh; GPT-6 Astra’s figure is OpenAI-reported at high effort. For FrontierCode, Anthropic reports Opus 5.5 at 54.4%, while its detailed task-cost discussion uses default medium effort. These are useful results, but model, harness, effort, and safety conditions must travel with the percentage.
The practical conclusion is that teams selecting a coding agent should benchmark their own representative tickets: a small bug, a multi-file change, a test-writing task, and a codebase-level investigation. Score accepted patches, test quality, regressions, review time, and total cost. A model that writes more code is not necessarily the one that gets a safe patch merged faster.
Safety and agent autonomy belong in the comparison
Price and benchmark score are not enough when an agent can edit files, use a terminal, or change business records. OpenAI says Sol and Luna improve on their predecessors in challenging alignment evaluations, including lower rates of misleading claims about coding work. Anthropic says Opus 5.5 improves on its automated behavioral audit and prompt-injection resistance versus Opus 5. These are vendor-reported results, and the companies use different evaluation suites, so they do not establish a direct safety ranking.
For a real deployment, test whether the model follows the scope of a task, asks before consequential actions, reports incomplete work honestly, resists instructions found inside untrusted content, and recovers safely from tool failures. Limit permissions, require human review for high-impact changes, log tool calls, and build idempotent actions where retries could duplicate work. Better model behavior can help, but it does not replace application-level authorization and audit controls.
A practical model-selection test for your team
- Choose 20 to 50 real tasks. Include common, difficult, and failure-prone examples. Remove secrets and personal data or replace them with safe test fixtures.
- Freeze the conditions. Use the same tools, context, task wording, timeout, retry allowance, and success rubric. Record model version and reasoning effort.
- Run each task several times. Model outputs vary. Track completion, correctness, safety, and whether a human accepts the result without repair.
- Measure full cost and time. Capture tokens, cache behavior, tool charges, retries, latency, review time, and downstream rework.
- Route by task risk and value. Send predictable, low-risk work to the cheaper model if it meets your quality threshold. Escalate ambiguous or high-impact tasks when the stronger result is worth the added cost.
- Re-test after changes. Providers update models, pricing, prompts, and tools. Keep a small regression suite so a model switch does not silently reduce quality.
For more context on how OpenAI is positioning the wider family, see our earlier analysis, GPT-6 Astra Didn’t Break AI. It Revealed What Was Already Broken.
The decision: buy the result your workload needs
GPT-6 Sol has a strong case where volume makes per-call cost matter and your own acceptance tests show that it completes the task well. Claude Opus 5.5 has a strong case where its higher benchmark scores on a specific workflow translate into less supervision, less rework, or better outcomes. In Zapier’s current table, Sol’s task cost is dramatically lower, while Opus 5.5 Max scores higher. That is a meaningful trade-off, not a universal verdict.
Start with list prices to estimate a ceiling, then measure cost per accepted outcome on your own tasks. The model with the lowest price per token may not be the cheapest to operate, and the model with the top benchmark score may not earn its premium on routine work. A small, repeatable evaluation is more useful than choosing from a launch-day headline.