Claude Opus 5.5 vs GPT-6 Sol: Pricing, Performance, and TCO
GPT-6 Sol costs $2 per million input tokens and $10 per million output, exactly half of Claude Opus 5.5's $4/$20. But sticker price masks the real operating cost. On automation benchmarks, Opus 5.5 scores 40% higher on some workloads, while Sol excels when effort settings are tuned down. For invoice extraction, Sol's lower cost per task ($0.13 vs $0.55 at standard settings) is compelling. For customer support with reasoning, Opus 5.5's stronger language understanding and coding catches errors Sol misses—reducing rework. Both launched September 22, 2026. Neither is universally cheaper: the winner depends on your input size, reasoning load, and how often you retry failed tasks.
In short
- Sol costs exactly half Opus 5.5's token price ($2/$10 vs $4/$20), but Opus scores 40% higher on some automation and coding benchmarks.
- Task costs differ more than token costs: Sol at low effort runs $0.13 per task; Opus 5.5 at low effort runs $0.55, but Opus delivers higher accuracy on reasoning-heavy work.
- Sol is cheaper on simple, high-volume work (invoice extraction, classification, summaries); Opus 5.5 wins on multi-step reasoning and customer-facing tasks with low tolerance for failure.
- Long-context requests over 272K tokens on Sol incur 2x the rate; Opus 5.5 holds pricing flat across 1 million tokens—a major cost difference for knowledge work.
- Data residency constraints favor Opus 5.5: Sol's EU residency is available only with Standard processing, not Batch or Fast mode; Opus offers multi-region options through Bedrock.
Pricing, head to head
On the OpenAI and Anthropic APIs, the raw token cost is unambiguous. GPT-6 Sol costs $2 per million input tokens and $10 per million output; Claude Opus 5.5 costs $4 and $20. Sol is exactly half the list price on both lines.
Cache reads align between them: Sol charges $0.20 per million cached input tokens, as does Opus 5.5. Cache writes differ: Sol is $2.50 per million, Opus is $5. Opus offers batch API pricing at $2/$10, matching Sol's standard rate—useful if you can defer tasks by a few hours. Sol's batch equivalent runs at 50% of standard.
Where the models diverge in cost is on long context. Any request over 272,000 input tokens on Sol incurs 2x the input rate and 1.5x the output rate for the entire request. Opus 5.5 keeps the same $4/$20 price across its full 1 million-token context window. For knowledge workers handling large documents or multi-file code reviews, this changes the bill significantly.
Performance and task costs
Token price is not task price. Artificial Analysis reports Sol's lowest cost per task at $0.13 (low effort), versus Opus 5.5 at $0.55 (low effort, adaptive reasoning). At maximum settings, Opus reaches $0.95 per task while Sol reaches $0.40. OpenAI frames this as Sol costing 9% of Opus 5's (the older model) cost on automation work—a real win, but against last year's Opus, not Opus 5.5.
On AutomationBench, which tests agents across sales, marketing, finance, HR and support workflows, Opus 5.5 scores 40% in native benchmarks, while Sol at xhigh effort reaches 33.2%. On FrontierCode 1.1, Opus leads 54.4% to Sol's 49.3%. For pure coding velocity, Opus 5 still outscores Sol on DeepSWE and OSWorld, though Sol is faster and cheaper per task when effort is dialed down.
For invoice extraction (a single-pass task), Sol at low effort is the clear pick—lower cost, adequate accuracy for structured fields. For customer support tickets requiring multi-turn reasoning and correction loops, Opus 5.5's higher first-pass quality often means fewer human reviews and faster resolution. That compounds across volume.
Reasoning controls and effort
Opus 5.5 runs adaptive thinking always on, with five effort levels: low, medium (default), high, xhigh, and max. You cannot disable thinking entirely. Sol offers reasoning effort from none through max; you can turn it off to save money and latency on simple tasks. This matters for routing: a classification or short summary on Sol with reasoning=none might finish for $0.01; the same on Opus 5.5 still carries thinking overhead.
On complex multi-step tasks, Opus 5.5's default medium effort often outperforms Sol's xhigh. Cost math: Opus 5.5 medium might run $0.30 per task while Sol xhigh runs $0.27. The gap is 10%, and Opus typically wins on quality. Test both on your actual workflows before deciding.
Integrations and deployment
Opus 5.5 is available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex, and Microsoft Foundry. Sol is available through the OpenAI API and Amazon Bedrock. Both support Amazon Bedrock, so if your team runs on AWS already, you can switch between them with minimal friction.
Data residency differs. Opus 5.5 offers US-only inference at 1.1x pricing, and through Bedrock supports US, EU, AU, JP, and global endpoints with zero-data-retention (ZDR) support by default. Sol offers EU data residency only with Standard processing—not with Batch, Flex, or Fast mode. If your workflow needs fast processing and EU residency, Sol is constrained; Opus is not.
Opus 5.5 on Google Cloud offers multi-region endpoints (US or EU) or regional guarantees with dedicated data residency. Both models deprecate periodically (Anthropic guarantees Opus 5.5 through at least September 22, 2027), but neither commits to permanent availability.
Breaking changes and migration
Opus 5.5 introduced several breaking changes. Computer use requires the computer_toolset_20260801 toolset on the Claude API and Google Cloud. Integrations using the older toolset will fail until updated. Preserved thinking (a safeguard preventing extraction of reasoning blocks) applies to accounts created after August 31, 2026; existing integrations can continue editing thinking content until they migrate. Fast mode is a research preview, available only on the Claude API, not on Bedrock or other cloud partners.
Sol has no published breaking changes from GPT-5.6 Sol. Function calling works only when reasoning_effort is set to none; if you use tools with reasoning, OpenAI recommends the Responses API instead of Chat Completions. That's a routing constraint, not a breaking change for existing integrations.
Total cost of ownership beyond tokens
Setup and testing: both models require a sandbox environment first. Budget 2–4 weeks to benchmark your actual workloads. Most teams find success by routing simple tasks to Luna (GPT-6) or Haiku (Anthropic's cheaper tier) and reserving Sol or Opus for complex work. This hybrid approach cuts spend 30–50% compared to using one model for everything.
Vendor lock-in: both are closed-source, available only through managed APIs. Switching between them costs engineering time (a few days to a week for routing logic and fallback handling), but no licensing fees or model retraining. Long-term cost risk: both labs have cut prices in September 2026, signaling margin compression. Neither is likely to raise prices soon, but neither guarantees flat rates forever.
Breakage and retries: Opus 5.5's higher task success rate reduces costly retries. Sol's lower cost per attempt means you can afford more retries. For customer-facing automation (replies, refunds, escalations), Opus 5.5's accuracy premium justifies the price. For internal batch processing (document labeling, data enrichment), Sol's cost advantage and acceptable accuracy make it the default.
Support and SLAs: Anthropic offers support tiers starting at $100/month for startups. OpenAI offers no published SLA for Sol in the public API; enterprise contracts are available but not disclosed. If guaranteed uptime or direct support matters, Anthropic has a clearer offering.
Custom-built and open-source alternatives
The crossover point for self-hosting open-source models (Llama 3.3 70B, Qwen 3.6, or GLM-5.2) falls between 10–30 million tokens per day, depending on infrastructure. Below that, managed APIs win. Above that, your own GPU cluster (or rented inference), plus 0.5–1.0 FTE of DevOps/MLOps, becomes cheaper by year two.
Open models now approach Sol and Opus on coding and reasoning tasks. GLM-5.2 (MIT-licensed) reaches Opus-level performance on some benchmarks at zero per-token cost—just infrastructure. Qwen 3.6 Plus and Mistral Medium 3.5 offer frontier-adjacent results on agent work for a single GPU or cloud rent. The downside: no vendor support, your build, your uptime.
For a five-person operations team doing 2–5 million tokens per day, managed APIs (Sol or Opus 5.5) are simpler and lower risk. For a 50-person engineering firm burning 50 million tokens daily on code generation and data extraction, a self-hosted Llama cluster plus a fallback to Sol for edge cases is often the economics winner.
Migration and final decision
If you are switching from Claude Opus 5 to either model: Opus 5.5 is the drop-in upgrade (same API, improved output). Sol is cheaper but may require task-level effort tuning and fallback routing for hardest cases.
If you are switching from GPT-5.6 Sol: GPT-6 Sol is a straightforward upgrade (same API, half the price, same or better performance). Testing it against Opus 5.5 is worth a day or two if your workflows touch multiple business units (code, reports, support).
Start with a one-week pilot on your heaviest automated task. Log completion quality (accuracy, retries, human corrections), total token spend, and wall-clock time. Run the same task on both models if you can. Let the numbers—not the benchmarks—decide.
How AiStaffo would automate this
AiStaffo automates back-office tasks like invoice processing, ticket categorization, and data reconciliation. Both Opus 5.5 and Sol can power these workflows, but the choice shapes your cost and reliability. Sol excels at high-volume extraction—routing invoices to the right GL account, flagging out-of-policy expenses, auto-completing expense reports. Opus 5.5 shines where reasoning matters: customer escalations, anomaly detection, multi-document reconciliations where a misclassification costs more than the token spend. AiStaffo's automation connects your documents, emails, and databases to whichever model fits the task, switching effort levels and models automatically. A hybrid approach—Sol for routing and extraction, Opus 5.5 for judgment calls—cuts spend 30–40% versus running one model everywhere. Book a free automation audit to see which model (or combination) wins on your actual workloads.
Questions people ask
Which model is cheaper for invoice processing?
Do I need to rewrite integrations to switch between them?
What's the difference if my requests are over 272K tokens?
Which handles customer-facing automation better?
Does data residency matter to me?
Should I self-host an open model instead?
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