Claire Donovan
OpenAI's GPT-6.1 Sol is a pricing event disguised as a model release. This is a GPT-6.1 Sol review focused on the number that matters: price. List application programming interface (API) pricing sits at $2 per million input tokens, $10 per million output tokens, and $0.10 per million cached input tokens. That is exactly one-fifth of OpenAI's GPT-6 Astra list prices of $10 and $50 (OpenAI, 2026). Artificial Analysis (2026) measured Sol one point below Astra on its Intelligence Index while the cost per max-effort task fell from $3.26 to $0.72. That gap reshapes what you can afford to build.
What Is GPT-6.1 Sol?
GPT-6.1 Sol is the cheap OpenAI model built for near-flagship work without the flagship bill. It accepts text and image input and outputs text only, with a 1,050,000-token context window and a 128,000-token max output. Knowledge cutoff is April 30, 2026 (OpenAI, 2026). That is enough context for large codebases, but agent traces still get cut at the output cap.
OpenAI replaced GPT-6 Sol with this model only seven days after that release, and the cached-input discount rose from 90 percent to 95 percent below the standard input rate (Artificial Analysis, 2026). The earlier Zovora breakdown on GPT-6.1 Sol: Near-Frontier AI at One-Fifth the Price has the launch mechanics. This is not a consumer toy. It is aimed at developers who run API workloads.
How Does GPT-6.1 Sol Compare to Astra?
On the Artificial Analysis Intelligence Index, Sol lands one point behind Astra. The gap that matters is per-task cost: $0.72 on Sol versus $3.26 on Astra at maximum reasoning effort. Sol is also 31 percent cheaper per task than GPT-6 Sol and 64 percent cheaper than GPT-5.6 Sol (Artificial Analysis, 2026).
OpenAI claims Sol matches Astra on the DeepSWE v1.1 software-engineering benchmark at roughly one-fifth the cost and comes within 2.1 percentage points on OSWorld 2.0 computer-use tasks at roughly one-seventh the cost per task (WION, 2026). An independent Sonar evaluation across 4,444 Java tasks found Sol cut reasoning tokens by 51.4 percent versus GPT-6 Sol, from 2.64 million to 1.28 million, while functional pass rate improved from 83.09 percent to 85.29 percent (Orca Router, 2026). That is a real efficiency gain, not just a cheaper sticker.
Not every number deserves trust. OpenAI's Common Business-Oriented Language (COBOL) legacy-code migration test showed Sol at 86.6 percent at low reasoning effort versus 5.4 percent for GPT-5.6 Sol, and 97.9 percent at higher effort, at 61 percent of GPT-5.6 Sol's API cost. OpenAI designed, ran, and scored that evaluation, and it has not been externally verified (Dataconomy, 2026). Orca Router (2026) suggests treating 86.6 percent as the realistic operating point rather than the near-saturated 97.9 percent score.
Near-Astra also does not mean beating every rival. Jon Krohn (2026) reported Astra tied Anthropic's Claude Fable 5.1 at 53 on Artificial Analysis's independent Intelligence Index at launch. So GPT-6.1 Sol is a cost-efficiency story, not a new capability frontier.
Why Is OpenAI Releasing GPT-6.1 Sol?
OpenAI is pushing out the cost-efficiency Pareto frontier. Artificial Analysis (2026) concluded that for a given level of intelligence, no model is currently cheaper per task. That matters more than a headline benchmark for a company defending developer market share. Zovora covered the earlier price shift in GPT 6.1 Sol Cut Near-Frontier AI Costs by 80% and Changed Who Gets to Build.
DeepLearning.AI's The Batch (2026) noted independent evaluations place OpenAI's new models at or near the top of many leaderboards at lower cost and time per task than the few models that beat them. Rivals are already matching Sol's $2 and $10 introductory pricing, so OpenAI has to keep cutting cost instead of selling raw capability. The strategy is simple: make near-Astra cheap enough that developers default to OpenAI for volume work.
When Will GPT-6.1 Sol Be Available?
It is already live. The model replaced GPT-6 Sol on September 29, 2026, one week after that model's release (Artificial Analysis, 2026). There is no waitlist and no staged rollout. Availability depends on your account tier.
Qatar News Agency (QNA, 2026) reported GPT-6.1 Sol is available through the API and in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users. It had not yet landed in the standard consumer ChatGPT model picker as of early October 2026. If you are on a free consumer plan, you will not see it until OpenAI moves it downstream.
Where Can You Access GPT-6.1 Sol?
The cheapest direct access is the API. You call it and pay for tokens, including reasoning tokens billed as output (OpenAI, 2026). If you do not want to manage an API, ChatGPT Work and Codex surface Sol inside Plus, Pro, Business, Enterprise and Edu accounts (QNA, 2026).
Watch the multipliers. Prompts over 272,000 input tokens cost 2x standard input and cached rates, and 1.5x output rates. Fast mode costs 2x Standard, Ultrafast costs 6x, Batch and Flex cost 50 percent less, and regional processing adds a 10 percent premium (OpenAI, 2026). The sticker price is not your checkout price if you send long documents or need low latency. If you want to push costs even lower, Zovora tested running a 125B AI model locally for $0.00004 per token in I Ran a 125B AI Model on My Gaming GPU for $0.00004 Per Token - Here's Why That Changes Everything.
Who Should Use GPT-6.1 Sol?
If you are building a side project, freelancing, or working at an early-stage startup, this model changes your math. A typical short coding request of 10,000 input tokens and 2,000 output tokens costs about $0.04 on Sol versus $0.20 on Astra. A freelancer running hundreds of document-heavy tasks per week pays about $0.72 per task on Sol versus $3.26 on Astra (Artificial Analysis, 2026).
The model handles text and image inputs only, so voice or video pipelines still need another model. Cheaper near-expert coding accelerates automation of routine development and operations work, so the durable skill becomes judgment: verifying AI output, understanding business rules, and knowing when to pay for the stronger model.
OpenAI's own documentation recommends testing Sol against Astra on your own tasks before relying on either (OpenAI, 2026). Autonomous agents still carry real failure risks. OpenAI disclosed an internal test where a Sol agent suppressed automated security alerts after misinterpreting an instruction not to send Slack messages (Dataconomy, 2026). That is not a reason to avoid Sol. It is a reason to review anything that touches access controls.
Bottom line: GPT-6.1 Sol is not a breakthrough. It is a discount that lands close enough to Astra for most real work. A $50 budget buys roughly 69 Sol max-effort tasks versus 15 Astra tasks. That is a side project weekend, not an enterprise line item. Test it on your own workload. Watch the multipliers. And do not let vendor benchmarks replace your own pass rate.
