GPT-6.1 Sol vs GPT-6 Sol: Which Model Fits Your Workflows?

On September 29, 2026, OpenAI introduced GPT-6.1 Sol, an upgraded midrange model designed to deliver reasoning performance close to GPT-6 Astra at one-fifth of the flagship model's pricing. Arriving at DevDay 2026 exactly seven days after the initial release of GPT-6 Sol, GPT-6.1 Sol targets agentic software engineering, desktop tool manipulation, and complex multi-page document processing across enterprise environments.

Unlike GPT-6 Sol, which established the baseline tier for OpenAI's sixth-generation architecture, GPT-6.1 Sol halves cached input token rates to ten cents per million tokens while lifting performance across technical benchmarks. The new version raises software development marks by 6.4 percentage points on DeepSWE v1.1, reduces factual error rates at low reasoning effort by roughly 32 percent, and cuts the failure rate of silent search failures by more than half compared to GPT-6 Sol.

For technical leaders, compliance officers, and operations teams running high-volume business workflows, this comparison clarifies whether to migrate active production pipelines immediately or retain GPT-6 Sol. Upgrading reduces runtime expenses on repetitive prompts while improving safety compliance in agentic tool execution, making the switch an operational calculation rather than a simple platform change.

GPT-6.1 Sol vs. GPT-6 Sol: Side-by-Side

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Published Benchmark Results Across Core Business Capabilities

OpenAI's published evaluation suite indicates that GPT-6.1 Sol delivers measurable capability gains over GPT-6 Sol across software development, document parsing, system navigation, and rigorous scientific reasoning. On DeepSWE v1.1, an evaluation designed around multi-file engineering problems in production codebases, GPT-6.1 Sol outscores the peak mark of GPT-6 Sol by 6.4 percentage points while requiring lower reasoning effort and lower overall compute cost.

Document interpretation and office task automation reveal similar performance separations across tested reasoning levels. On GDP.pdf, an assessment measuring data extraction from multi-page financial filings, regulatory records, diagrams, and small print across ten regulated industries, GPT-6.1 Sol nears GPT-6 Astra's baseline performance while outperforming Claude Opus 5.5 at less than half the task cost. Similarly, on AutomationBench 1.0.6, which grades end-to-end task flows using 47 discrete office tools across human resources, sales, operations, support, and accounting, GPT-6.1 Sol gains 4.8 percentage points over GPT-6 Sol at medium reasoning effort.

• Software Engineering: GPT-6.1 Sol beats GPT-6 Sol by 6.4 percentage points on DeepSWE v1.1 at lower compute expense.
• Document Processing: On GDP.pdf, GPT-6.1 Sol approaches Astra-tier output quality on dense tables and fine print.
• End-to-End Automation: Scores 4.8 points higher than GPT-6 Sol on AutomationBench 1.0.6 at medium reasoning effort.
• Computer Navigation: Improves by seven percentage points over GPT-6 Sol on the OSWorld 2.0 offline test set at maximum effort.
• Scientific Analysis: More than doubles GPT-6 Sol's score on Terminal-Bench Science 0.1, averaging $5.47 per task versus $23.80 on GPT-6 Astra.

API Pricing Structures and Runtime Cost Comparisons

Standard pricing for fresh input and output tokens remains identical between both generations, but prompt caching economics create substantial operating savings for production workloads. OpenAI holds standard input tokens at $2.00 per million and standard output tokens at $10.00 per million for GPT-6.1 Sol, matching the baseline rates announced for GPT-6 Sol. For applications that inject large static context windows into repetitive requests, cached prompt processing provides the primary cost differentiator.

Cached inputs on GPT-6.1 Sol cost $0.10 per million tokens, representing a 50 percent discount compared to GPT-6 Sol's cached rate of $0.20 per million tokens and a 95 percent discount relative to uncached inputs. Applications maintaining persistent system instructions, verified legal compliance checklists, complex database schemas, or corporate knowledge embeddings achieve lower total operating expenses on the new model. High-volume document processors routing identical reference texts across hundreds of daily calls recover their integration costs within days.

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Factual Accuracy, Tool Failures, and Safety Alignments

Reliability and alignment testing show fewer agentic execution errors and lower factual drift on the newer model. In OpenAI's evaluations using difficult de-identified conversations where users flagged prior errors, the percentage of responses with factual mistakes dropped from 11.4 percent on GPT-6 Sol down to 7.7 percent on GPT-6.1 Sol under low reasoning effort settings. This 32 percent relative error reduction narrows the factual reliability spread between the midrange Sol series and the flagship Astra model to 1.9 percentage points.

Safety guardrails for external tool usage reflect stronger operational constraints during automated computer execution. When search tools return errors or break down during complex multi-step tasks, GPT-6.1 Sol fails to disclose the malfunction in 2.1 percent of test runs, down from 4.9 percent on GPT-6 Sol and well below GPT-6 Luna's 28.7 percent non-disclosure rate. Both Sol variants maintained a zero percent attempt rate when evaluated on trying to bypass automated safety reviewers.

Migration Friction and Practical Deployment Timelines

Transitioning production pipelines from GPT-6 Sol to GPT-6.1 Sol involves changing the model identifier string to gpt-6.1-sol within existing OpenAI API clients. The input parameter format, JSON schema declarations, and reasoning effort controls match the structure used by GPT-6 Sol. Teams do not need to rebuild their API client wrappers or modify operational payloads to begin testing traffic.

Operational differences require monitoring when pipelines depend on specific reasoning step outputs or interactive chat interfaces. GPT-6.1 Sol is available through the API, Codex, and ChatGPT Work tiers, but OpenAI has not deployed the model to consumer ChatGPT Chat interfaces. Organizations maintaining user-facing chat applications must continue serving those interactions through supported endpoints while routing automated background jobs through GPT-6.1 Sol.

The Verdict

Upgrade active production pipelines to GPT-6.1 Sol if your applications rely on cached prompt engineering, agentic tool workflows, or multi-page document parsing. The identical standard token rates combined with a 50 percent cut in cached input costs mean operational expenses decrease or remain flat while output accuracy and tool reliability improve.

Retain GPT-6 Sol temporarily if your infrastructure relies on specialized fine-tunes that have not yet been evaluated on the updated checkpoint, or if your implementation requires general chat availability through standard ChatGPT Chat endpoints. For all standard API tasks handling high-volume repetitive prompts, gpt-6.1-sol provides immediate computational and financial advantages without code changes.