Claude Opus 5.5 and GPT 6 Sol were both announced on September 22, 2026. Their positioning makes the choice interesting: Opus aims to deliver stronger results on demanding work, while Sol brings advanced reasoning to a more affordable price point.
The independent results cited in the research favor Opus 5.5 for complex coding and professional knowledge work. GPT 6 Sol offers a compelling alternative for frequent iterations, large workloads and projects where operating costs matter.
Architecture and capabilities
Both models accept text and images and generate text responses. Both support tools and large context windows, but neither provider publishes its parameter count or enough architectural information to establish the exact model topology.
| Feature | Claude Opus 5.5 | GPT 6 Sol |
|---|---|---|
| Release date | September 22, 2026 | September 22, 2026 |
| Architecture | Proprietary; exact topology undisclosed | Proprietary; exact topology undisclosed |
| Parameter count | Undisclosed | Undisclosed |
| Reasoning | Adaptive thinking always enabled; medium by default | none, low, medium, high, xhigh and max; medium by default |
| Context window | 1 million tokens | 1.05 million tokens |
| Maximum standard output | 128K tokens | 128K tokens |
| Input and output | Text and images in; text out | Text and images in; text out |
| Knowledge cutoff | June 2026 | April 20, 2026 |
| Provider API input price | $4 per million tokens | $2 per million tokens |
| Provider API output price | $20 per million tokens | $10 per million tokens |
| Cached input | $0.20 per million tokens | Discounted cached input |
| Public customization | No public fine tuning or downloadable weights documented | Fine tuning unsupported; weights unavailable |
These are the providers’ published API prices. OpenCheese pricing may differ.
Transparency around training remains limited. Anthropic lists a June 2026 training cutoff but does not disclose the total training corpus, compute budget or proportions of public and proprietary data.
OpenAI says Sol and Luna were trained using methods similar to GPT 6 Astra. Its documentation describes filtered datasets and reinforcement learning, but does not disclose Sol’s total training tokens, training compute or source percentages.
Benchmark comparison
Artificial Analysis provides the most useful direct comparison in the supplied research because it evaluates both models through a shared framework. Its Intelligence Index v4.3.2 combines ten evaluations covering coding, reasoning, agentic work, long context, professional documents and factual reliability.
| Benchmark | Claude Opus 5.5 | GPT 6 Sol | How to interpret it |
|---|---|---|---|
| Artificial Analysis Intelligence Index, max | 58 | 48 | Shared evaluation favors Opus |
| Terminal Bench 4.0, Artificial Analysis | 59.6% | 44% | Opus leads on agentic terminal tasks |
| Humanity’s Last Exam, Artificial Analysis | 61.4% | Not reported in the cited Sol summary | No complete direct comparison |
| AutomationBench, provider results | 40.0% | 33.2% at xhigh | Configurations differ |
| OSWorld 2.0 | 81.8% partial | 60.5% offline at xhigh | Different variants; scores are not directly comparable |
| DeepSWE 1.1 | Not specified in the cited release comparison | 68.8% at max | No corresponding Opus result |
| MMLU, HumanEval and BIG bench | No fresh official launch result identified | No fresh official launch result identified | Missing results do not mean zero |
Anthropic additionally reports 54.4% on FrontierCode 1.1, 67.7% on Humanity’s Last Exam with tools and an Elo score of 1846 on GDPval AA v2.1.
OpenAI’s launch charts frequently compare Sol with Claude Opus 5 or Fable 5.1. Those comparisons do not establish how Sol performs against the newer Opus 5.5.
The research also found no matched launch comparison that establishes a clear winner in multilingual performance or mathematics. Anthropic documents multilingual support, but the absence of a comparable Sol benchmark does not imply that Sol lacks those capabilities.
Factual accuracy and hallucinations
OpenAI reports that Sol made roughly half as many errors as GPT 5.6 Sol on an internal dataset drawn from conversations where users flagged mistakes. That dataset deliberately emphasizes problematic cases and does not represent typical usage.
Artificial Analysis reports that Sol’s hallucination rate on the difficult AA Omniscience benchmark fell from 92% for its predecessor to 60%. Sol attempted 83% of questions and achieved 54% accuracy. These are specialized benchmark measurements, not a claim that 60% of ordinary responses contain hallucinations.
Anthropic reports that 16 of 18 Opus 5.5 research reports passed its factuality threshold in a particular evaluation. Artificial Analysis also places Opus 5.5 at the top of its AA Omniscience evaluation.
Neither set of results establishes a universal hallucination rate or an overall winner in following instructions.
Safety and safeguards
Anthropic reports external evaluations by METR and Frontier Design, improved resistance to prompt injection and its strongest automated behavioral audit result to date. Sensitive requests involving cybersecurity, biology or frontier model development may be routed to other Claude models. This can make production behavior differ from an unrestricted benchmark run.
OpenAI classifies Sol as having High capability in cybersecurity and biological or chemical tasks under its Preparedness Framework, while placing it below High for AI self improvement.
In a simulation involving 50,319 internal tasks similar to Codex workloads, OpenAI recorded 42 flags with severity 3 or higher for Sol, compared with 66 for GPT 5.6 Sol. Some flags related to exfiltration increased, illustrating why safety requires more than a single score.
Speed and operating costs
Anthropic reports more than 30% higher output speed than Opus 5. Its optional Fast mode offers up to 2.5 times the output throughput at $8 per million input tokens and $40 per million output tokens. The improvement mainly concerns generation speed rather than the time before the first response token.
Artificial Analysis measured 84.6 output tokens per second for Opus 5.5 at high effort. For Sol at max effort, it measured 104.4 tokens per second and 107.18 seconds to the first token, including the initial reasoning phase. Because the effort settings differ, these figures are not a controlled speed comparison.
The cost comparison is clearer:
| Artificial Analysis configuration | Intelligence Index | Approximate cost per task |
|---|---|---|
| Claude Opus 5.5, max | 58 | $5.98 |
| Claude Opus 5.5, high | 54 | $1.82 |
| GPT 6 Sol, max | 48 | $1.06 |
Opus at high effort retains a higher index score than Sol at max while costing substantially less than Opus at max. Choosing the right reasoning setting can therefore make a significant difference to practical value.
Neither provider discloses the exact hardware configuration behind these APIs.
Which model should you choose?
The research supports Opus 5.5 for demanding coding and professional work where stronger results justify a higher cost. Sol is attractive for frequent iterations and large workloads where lower prices and adjustable reasoning matter.
Opus 5.5 is offered through Anthropic’s API, AWS, Google Cloud and Microsoft Foundry. Sol is available through OpenAI’s API and was introduced in ChatGPT Work and Codex for eligible paid plans.
For your own deployment, compare both models on the same tasks, tools and budgets. A leaderboard can guide the decision, but your workload determines the practical result.
You can try Claude Opus 5.5 at opencheese.dev right now!