GPT-6.1 Sol Is Live: Near-Astra at One-Fifth the Price, One Week After the Model It Replaces

Open​AI released GPT-6.1 Sol on September 29, 2026, at its DevDay 2026 keynote — a refresh of the GPT-6 Sol tier that shipped exactly one week earlier, repriced nowhere, and aimed squarely at a model costing five times as much. The headline numbers are unchanged from the model it replaces: $2.00 per million input tokens and $10.00 per million output tokens. The number that did move is the one most agent workloads actually pay: cached input fell from $0.20 to $0.10 per million tokens, half of GPT-6 Sol's rate and a tenth of GPT-6 Astra's. Open​AI's own framing is that GPT-6.1 Sol "nearly matches" GPT-6 Astra's intelligence on agentic coding, computer use and professional work at one-fifth of Astra's standard token prices, and that is a vendor claim, not a measured one — as of September 30 no independent evaluator has published a single score for the model. What follows separates the two.

What shipped, and where the record is

The release is unusually well documented for a DevDay announcement. Within hours there was a model page at developers.openai.com, a pricing block, a system-card addendum at deploymentsafety.openai.com, and a commit in the vendor's own public API schema titled "Add gpt-6.1-sol model to model enums," timestamped 17:16 UTC on September 29. Open​AI's developer changelog, by contrast, still ends at the September 22 entry that announced GPT-6 Sol and GPT-6 Luna — the 6.1 entry has not been written up there, so the model page and the launch post are the primary record. The resolution matters because there is no second-generation snapshot to point at: the page's snapshot list contains one identifier, gpt-6.1-sol, with no dated variant, so "GPT-6.1 Sol" and the deployment you call today are the same thing.

The shape of it, side by side with what it replaces:

• Identifier — gpt-6.1-sol, a single snapshot vs gpt-6-sol with no dated variant either
• Context — 1,050,000 tokens and 128,000 max output tokens for both; the 6.1 page states both figures explicitly, where the 6.0 page documents a 922,000-token maximum input instead
• Knowledge cutoff — April 30, 2026 vs April 20, 2026
• Reasoning effort — low, medium (default), high, xhigh, max, with none and minimal unsupported vs the same ladder plus none
• Pricing — $2.00 in / $0.10 cached / $2.50 cache write / $10.00 out per million tokens vs $2.00 / $0.20 / $2.50 / $10.00
• Long prompts — above 272K input tokens the whole request is repriced at 2× input and cache rates and 1.5× output, identical on both models
• Availability — Plus, Pro, Business, Enterprise and Edu in ChatGPT Work and Codex, plus the API; explicitly not in the consumer Chat product
• Residency — US and EU data residency supported, with fast mode unavailable under EU residency
• Tools — web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP and tool search, all supported through the Responses API
• Fine-tuning — not supported on either

One API shape worth flagging before you migrate: GPT-6.1 Sol supports Chat Completions, but tool calling requires the Responses API. On GPT-6 Sol the constraint was the reverse-adjacent one — function calling in Chat Completions worked only with reasoning_effort: "none". If your stack calls tools through /v1/chat/completions, that is a code change, not a model swap.

The benchmark claims, labelled as claims

Every number in this section is Open​AI's, published in the launch post, and none of them has been reproduced by anyone outside Open​AI yet. They are worth reading in full because the pattern across them is consistent — the gains over GPT-6 Sol are real but the framing that travels is the Astra comparison, and the Astra comparison is always a cost comparison.

• DeepSWE v1.1, complex software-engineering tasks in real codebases — Open​AI reports GPT-6.1 Sol matching GPT-6 Astra at roughly one-fifth of the cost, and beating GPT-6 Sol's best score by 6.4 percentage points at a lower reasoning effort and lower cost.
• GDP.pdf, professional questions over complex PDF documents — Open​AI reports GPT-6.1 Sol scoring above Claude Opus 5.5 with fallbacks at less than half the cost per task across tested reasoning settings, and approaching Astra's state-of-the-art result at roughly one-fifth the cost per task.
• AutomationBench 1.0.6, multi-step business workflows across 47 tools — Open​AI reports 2.2 percentage points above Claude Opus 5.5 at medium reasoning effort at roughly a third of the cost, and 4.8 points above GPT-6 Sol at the same setting.
• OSWorld 2.0 offline set, long-horizon computer use — Open​AI reports a seven-point improvement over GPT-6 Sol at maximum reasoning effort at less than half the cost, and a result within 2.1 points of Astra at roughly one-seventh the cost per task.
• Terminal-Bench Science 0.1, scientific workflows — Open​AI reports GPT-6.1 Sol more than doubling GPT-6 Sol's score at maximum effort at less than half the cost per task, at $5.47 per task on average against $23.21 for Claude Opus 5.5 and $23.80 for Astra. Open​AI also states that Astra still holds the top score among models it tested at 68.1% and should be used for the hardest scientific work — a usefully un-marketing sentence.
• Factuality — on de-identified conversations where users had flagged an earlier model's error, Open​AI reports the share of responses containing a factual error falling from 11.4% to 7.7% at low reasoning effort, a reduction of about 32%, with the error rate staying within 1.9 points of Astra's across tested settings at less than one-fifth the cost per task.

Two of those deserve translation. First, the factuality evaluation runs on prompts deliberately chosen because a previous model got them wrong; Open​AI says so in the post, and it means the 11.4% and 7.7% describe a hostile slice of traffic, not your traffic. Second, the paragraph that reads most strongly for the model is the one about search-tool transparency in the safety addendum: on tasks built to elicit failures at maximum reasoning effort, GPT-6.1 Sol fails to tell the user that its search tool is broken in 2.1% of cases, against 4.9% for GPT-6 Sol, 1.5% for GPT-6 Astra and 28.7% for GPT-6 Luna. That is the kind of number that decides whether an agent is deployable, and it is also, again, the vendor's own.

The independent record is empty, and that is the story's other half

Artificial Analysis — the third-party board this blog treats as the neutral reference — has no GPT-6.1 Sol entry. Its model URLs for the 6.1 Sol slug return 404, and the string does not appear in the live leaderboard HTML. What the board does have is a full evaluation of GPT-6 Sol at maximum reasoning effort: an Intelligence Index of 48, a cost of $1.06 per index task, 77 million output tokens generated while running the index against a board median of 88 million, and a Coding Agent Index of 57 at $2.99 per task. Those figures are the nearest independent anchor for the model GPT-6.1 Sol replaces, and they are what any 6.1 claim should eventually be measured against.

Until that entry appears, anyone quoting a 6.1 Sol score against another model is quoting Open​AI against Open​AI. That is not a reason to distrust the launch post; it is a reason to run your own evaluation set before you move a production path. The vendor gives an unusually concrete instruction on this itself: Open​AI tells developers already using GPT-6 Sol to review the migration guidance before switching, and the migration guidance tells them to compare configurations on representative tasks rather than assuming the highest effort is the best trade-off.

The cached-input cut is the part that actually changes the bill

A price list that does not move but a cache rate that halves is easy to read past, and it is the most consequential line in the release for anyone running agents. Cached input at $0.10 per million tokens is 5% of the uncached input rate, half of GPT-6 Sol's cached rate, and — against GPT-6 Astra's $1.00 cached input — a tenth of the flagship's. Agent loops that resend a stable prefix thousands of times live almost entirely on that line. The same 200-million-token agent month that would bill roughly $40 in cached input on GPT-6 Sol bills about $20 on GPT-6.1 Sol; on GPT-6 Astra the same cached traffic is $200.

The arithmetic never gets that clean in production, because cache reads only bill at that rate when the prefix actually hits, and Open​AI notes that changing reasoning effort or the available tool set no longer invalidates a cache entry the way it once did — which is the quiet fix that makes the cache line usable in an agent loop at all. The other threshold to watch is unchanged: cross 272,000 input tokens and the entire request reprices at twice the input and cache rates and 1.5× the output rate. Batch and Flex run 50% below standard, fast mode runs at 2× standard, and regional processing adds a 10% premium where it is available.

What has not shipped, and what that means for routing

Open​AI says GPT-6.1 Sol Ultrafast arrives "in the coming days" in Codex at up to 8× faster token generation, with no price attached. That is a promise, not a product, and it is worth naming as such: there is no Ultrafast rate card for the 6.1 tier today, and Open​AI has not said whether the 8× figure is measured against standard speed or against fast mode, which is itself priced at 2× standard. Treat the announcement as a scheduling signal and price it when a number appears.

On our side, honestly: GPT-6.1 Sol is not on OrcaRouter's routes yet. The public catalogue endpoint returns "model not found" for openai/gpt-6.1-sol today, and our Open​AI slate carries GPT-6 Astra, GPT-6 Sol and GPT-6 Luna. What is callable on OrcaRouter right now is GPT-6 Sol at Open​AI's own list price with zero markup — the $2.00 / $0.20 / $10.00 rate card, and the 272K repricing rule, passed through exactly as the vendor lists it. That matters more than usual this week for a specific reason: the same pass-through means if Open​AI cuts the 6.1 tier's price, or attaches a number to Ultrafast, the change is live on our side the same day it is live on Open​AI's, with no second contract to renegotiate. When 6.1 Sol lands on the routes it will appear in the catalogue at the vendor's price like everything else, and until then the honest answer is that the model it replaces is the one you can call.

The practical version of the decision: if you are already on GPT-6 Sol and your workload is cache-heavy, agentic, or long-horizon, the 6.1 refresh improves exactly the dimensions you are paying for, and the migration is a model-string change plus a Chat-Completions-to-Responses move if you use tools. If your workload is short-prompt generation with little reuse, the release changes almost nothing for you — same input price, same output price, same window. And if you were waiting for a third-party score before committing, that wait is still open.