GPT-6.1 Sol vs Claude Opus 5.5

On September 29, 2026, OpenAI introduced GPT-6.1 Sol, an upgraded intelligence model designed to deliver reasoning and agentic execution comparable to GPT-6 Astra at one-fifth of the pricing.

OpenAI positioned the release as a direct challenge to Claude Opus 5.5 from Anthropic, delivering higher task accuracy on multi-tool workflow evaluations while operating at less than half the per-task execution cost. On complex document interpretation and multi-step business actions, GPT-6.1 Sol reduces token expense through an aggressive caching tier that charges ten cents per million cached input tokens.

For business operators evaluating foundational models for back-office automation, legal document review, and software development, this comparison alters the economics of complex autonomous systems. Choosing between GPT-6.1 Sol and Claude Opus 5.5 now hinges on tool-use reliability, document interpretation limits, and how cached token pricing fits your routine execution volumes.

GPT-6.1 Sol vs. Claude Opus 5.5: Side-by-Side

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Official Benchmark Results for GPT-6.1 Sol vs Claude Opus 5.5

Published evaluation metrics from OpenAI show GPT-6.1 Sol scoring higher than Claude Opus 5.5 across complex document parsing and cross-functional business execution benchmarks. When evaluating GPT-6.1 Sol vs Claude Opus 5.5 benchmark data, the most practical comparison for business buyers appears on GDP.pdf, an evaluation measuring performance on questions over complex documents containing tables, charts, diagrams, and fine print across finance, healthcare, legal, and seven other industries. GPT-6.1 Sol scored higher than Claude Opus 5.5 with fallbacks while operating at less than half the financial cost per task across tested reasoning parameters.

On AutomationBench 1.0.6, which evaluates end-to-end operational workflows involving 47 distinct tools across sales, marketing, support, operations, human resources, and finance, GPT-6.1 Sol scored 2.2 percentage points higher than Claude Opus 5.5 at medium reasoning effort. OpenAI noted that testing on alternative configurations often understates actual execution cost because those tests omit automated error fallbacks that occur on roughly 40 percent of tasks.

For technical research, Terminal-Bench Science 0.1 tracks data analysis, scientific simulation, and theorem proving. In that environment at maximum reasoning effort, GPT-6.1 Sol completed tasks at an average cost of $5.47 compared to $23.21 for Claude Opus 5.5, representing a cost reduction exceeding 75 percent. However, OpenAI stated that for top-tier scientific research, GPT-6 Astra remains the preferred selection, achieving the highest mark at 68.1 percent.

API Pricing and Cached Token Cost Structure

Token economics create the widest operational gap between GPT-6.1 Sol and Claude Opus 5.5 for high-volume automated systems. OpenAI set standard API prices for model identifier gpt-6.1-sol at $2.00 per million input tokens and $10.00 per million output tokens, alongside an aggressive cached input price of $0.10 per million tokens.

Cached inputs at ten cents per million tokens allow repetitive system prompts, regulatory knowledge bases, and multi-turn conversational histories to run at 95 percent less than standard input pricing. Claude Opus 5.5 operates in a higher standard pricing tier designed for bespoke complex reasoning without an equivalent ten-cent caching floor.

For teams managing automated intake or document extraction pipelines, this structural difference changes monthly operational expenses significantly. Running continuous batch analyses across large internal libraries becomes considerably less expensive under GPT-6.1 Sol when architectures maintain warm cache contexts.

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Agentic Workflows and Computer Use Capabilities

Autonomous business applications require models that handle software interfaces reliably without straying from administrative guardrails. On the OSWorld 2.0 offline benchmark for desktop operating system interactions, GPT-6.1 Sol demonstrated meaningful progression over GPT-6 Sol, finishing within 2.1 percentage points of GPT-6 Astra at maximum effort while cutting task costs roughly sevenfold.

System reliability also depends on safety controls during execution. On adversarial evaluations tracking whether a model discloses a broken search tool, GPT-6.1 Sol recorded a failure rate of 2.1 percent at maximum effort, compared to 4.9 percent for GPT-6 Sol and 28.7 percent for GPT-6 Luna. Furthermore, the model made zero attempts to bypass automated safety reviewers during testing, aligning it directly with enterprise governance standards.

• Reliable tool fallbacks prevent unattended agentic workflows from hanging on broken search integrations.
• Respecting explicit environmental restrictions ensures automated agents do not execute unauthorized operational commands.
• Support in ChatGPT Work and Codex gives engineering and operations teams immediate access for desktop workflows.

Enterprise Governance and Regulated Industry Fit

Deploying generative systems in healthcare, legal, and financial practices requires strict adherence to privacy controls, factual accuracy, and alignment criteria. On factuality evaluations using de-identified real-world conversations flagged for historical errors, GPT-6.1 Sol reduced its factual error rate to 7.7 percent at low reasoning effort, down from 11.4 percent on GPT-6 Sol.

Anthropic maintains a long-standing reputation for constitutional safety mechanisms and nuanced reasoning, making Claude Opus 5.5 a trusted choice for narrative legal drafting and policy synthesis. Conversely, OpenAI published a system card addendum for GPT-6.1 Sol documenting lower rates of unauthorized actions in agentic environments, providing verifiable governance documentation for technical risk officers.

Organizations that handle sensitive records must evaluate context constraints alongside safety. OpenAI has not published context window length or maximum output token specifications for GPT-6.1 Sol, whereas earlier iterations such as GPT-6 Sol offered up to 1.05 million tokens of context window capacity. Buyers requiring firm context size guarantees for archival analysis should verify official documentation before migrating mission-critical workloads.

The Verdict

Choose GPT-6.1 Sol if your business focuses on high-volume document workflows, continuous autonomous tool use across software interfaces, or budget-sensitive agentic deployments. Its standard pricing of $2.00 per million input tokens, $0.10 cached inputs, and demonstrated superiority on AutomationBench 1.0.6 make it the practical selection for automated operational pipelines.

Choose Claude Opus 5.5 if your priority centers on intricate legal analysis, long-form editorial nuance, or environments with established safety protocols built around Anthropic models. If you do not require rapid desktop automation and value Claude's established reasoning style for high-stakes advisory work, Claude Opus 5.5 remains a capable option.

For teams running production automation today, test a batch of multi-page technical PDFs through the gpt-6.1-sol API endpoint to evaluate whether the cached input pricing and document accuracy reduce your overall processing bills.