GPT-6.1 Sol review (2026): an honest verdict on OpenAI's near-Astra model — what it does well, the pricing, and where it stops for creators.
As a model, GPT-6.1 Sol is one of the best value plays OpenAI has shipped: it holds GPT-6 Sol's low token price ($2/$10 per million input/output) while, OpenAI says, nearly matching flagship GPT-6 Astra on agentic coding, computer use, and professional work — Astra-level results at one-fifth Astra's standard price. Scored on its own terms it earns high marks for accuracy, agentic performance, and cost. The honest caveat is category, not quality: it is a text and reasoning model. It generates no video or images and publishes nothing, and it is Codex/ChatGPT-Work-first, so if you need finished, distributable content, the model is one input to a longer job — not the tool that gets it done.
GPT-6.1 Sol is OpenAI's upgrade to GPT-6 Sol, announced September 29, 2026 at DevDay — a week after GPT-6 Sol itself. The pitch is unusually restrained for a model launch: not a new headline capability, but flagship-adjacent intelligence at a fraction of flagship cost. OpenAI says it reaches nearly the same level as GPT-6 Astra on agentic coding, computer use, and professional work, while charging about one-fifth as much for standard tokens.
I run Kompozy, a content generation and publishing engine, so I will state the framing plainly: Kompozy is not a competitor to this model — it runs on models like it. That is exactly why I can score GPT-6.1 Sol on its own terms, without an axe to grind. The place the two touch is a single boundary I will return to: a language model hands you text and stops, and turning text into published content is the job Kompozy exists for.
Two things anchor the verdict. First, as a model it is strong and cheap — a near-flagship accuracy and agentic profile at GPT-6 Sol's price, which OpenAI frames as a permanent value shift rather than a promotion. Second, the ceiling for a creator is a category boundary: GPT-6.1 Sol reasons, writes, and codes, but renders no media and publishes to no platform, and its headline benchmarks are self-reported and its training data undisclosed.
Everything below reflects GPT-6.1 Sol as announced around its September 2026 launch, verified against OpenAI's announcement and independent reporting. Pricing, benchmarks, and rollout are still settling and vary by plan and region, so confirm current details on OpenAI's own pages before relying on any single figure.
GPT-6.1 Sol is a language model in OpenAI's GPT-6 family, positioned as a value upgrade to GPT-6 Sol and pitched against the flagship GPT-6 Astra on capability. It keeps GPT-6 Sol's standard price — $2 per million input tokens and $10 per million output tokens — and halves cached input to $0.10 per million. Because GPT-6 Astra sits at $10 input and $50 output, GPT-6.1 Sol's standard prices are exactly one-fifth of the flagship's. The differentiator OpenAI leads with is that you no longer need to pay flagship rates for most agentic and professional work. On its internal factuality measure the error rate lands within roughly 1.9% of Astra across reasoning settings, and at low reasoning effort dropped from 11.4% on GPT-6 Sol to 7.7%. OpenAI also cited parity with Astra on a software-engineering benchmark and a near-match on a computer-use test at maximum reasoning. Access is through the API as gpt-6.1-sol and inside ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users — not yet standard Chat — with an "Ultrafast" variant promised in the days after launch.
GPT-6.1 Sol fits developers and knowledge workers who want near-flagship reasoning, coding, and computer-use at a fraction of the flagship's cost, and anyone drafting or synthesizing text at volume who wants frontier-level accuracy without frontier prices. Its Codex and ChatGPT-Work rollout makes it especially relevant to people wiring a model into an agentic or professional workflow. It is not for creators who want a single tool that produces finished video, images, or scheduled posts — that is a different category the model feeds into, not one it occupies.
| Dimension | Score | Why |
|---|---|---|
| Agentic coding & professional work | 4.6 / 5 | OpenAI reports near-Astra results on agentic coding and professional tasks, and parity with the flagship on a software-engineering benchmark. |
| Factual accuracy | 4.6 / 5 | The headline improvement — an error rate within about 1.9% of Astra, and a drop from 11.4% to 7.7% at low reasoning effort versus GPT-6 Sol. |
| Cost efficiency | 4.8 / 5 | Near-flagship capability at one-fifth Astra's standard price, keeping GPT-6 Sol's $2/$10 and halving cached input to $0.10 — an aggressive value shift. |
| Computer use | 4.4 / 5 | OpenAI cites a near-match with Astra on a computer-use test at maximum reasoning, a genuine strength for agentic workflows. |
| Availability & ecosystem | 4.2 / 5 | API plus ChatGPT Work and Codex across paid tiers, but not yet in standard Chat; the Ultrafast variant was still to come at launch. |
| Transparency & documentation | 3.5 / 5 | OpenAI shared benchmark figures, but training data is undisclosed and the headline numbers are self-reported; rollout is staged. |
| Value for content teams | 4.0 / 5 | An excellent, cheap drafting and research layer — but you still need a production and distribution workflow around it to ship anything. |
Pricing is the whole story, and it is the strongest thing about the launch. GPT-6.1 Sol keeps GPT-6 Sol's $2 per million input tokens and $10 per million output tokens, and halves cached input to $0.10. What changed is what you get for that price: OpenAI now claims near-flagship intelligence on agentic and professional work, which at GPT-6 Astra's $10/$50 makes GPT-6.1 Sol exactly one-fifth the cost for comparable results. For anyone running agentic coding, computer use, or high-volume drafting, that is a real and durable cut in the cost of frontier-adjacent output.
The catch for a creator is that token cost is usually a small slice of what it actually costs to ship content. The expensive parts are producing the media — video, carousels, images — editing to brand, and getting each piece scheduled and published across platforms, none of which a language model touches. So paying one-fifth for near-flagship drafting is welcome, but it lowers the cheapest input, not the labor-heavy output. Judge the value accordingly: as a model, GPT-6.1 Sol is priced aggressively and honestly; as a lever on your content budget, it is one small, upstream part.
| Use case | Fit | Why |
|---|---|---|
| Drafting scripts, outlines, and hooks | Strong | It reasons well, is accurate, and is cheap enough to over-generate variations and keep the best raw copy to build on. |
| Research synthesis from transcripts and sources | Strong | Document understanding and multi-step reasoning turn a messy source into a clean set of post angles. |
| Agentic coding, debugging, and technical explainers | Strong | This is its core strength — OpenAI cites near-Astra results and software-engineering parity. |
| Fact-sensitive reference writing | Strong | The near-flagship factuality profile makes it a safer choice where accuracy matters, though you should still verify. |
| Generating finished video or images | Weak | It is a text and reasoning model — it produces no media of any kind. |
| Publishing and scheduling to social platforms | Weak | The model returns text; it logs into nothing and posts nowhere. |
| Consistent brand voice across a content calendar | Weak | There is no persona or brand-governance layer — voice consistency across many outputs is left to you. |
| Running a multi-format content pipeline end to end | Weak | A model is one input; the make-it, brand-it, and ship-it steps require a content engine on top. |
Here is the honest boundary: Kompozy is not an alternative to GPT-6.1 Sol — it runs on models like it. GPT-6.1 Sol is close to the best value you can buy at the copy layer right now, and Kompozy is happy to consume that value: it uses an OpenAI or Claude model to draft the words, governed by a Persona Brief so the output reads like you. Near-flagship accuracy at one-fifth the price makes that drafting step better and cheaper, which is a straightforward win — and on the Founding tier you can bring your own key to capture it directly.
Where the two part ways is what happens after the text exists. A language model, however accurate and cheap, hands you a draft and stops — it renders no talking-head video, no brand-exact carousel, no quote card, and it publishes to no platform. Kompozy is the layer that turns that draft into a finished, on-brand, multi-format content calendar — Persona Shorts, clipped shorts, carousels, photo posts, blogs, and newsletters — and schedules and publishes it across nine platforms behind a per-post review. So the choice between "a near-flagship, cheaper model" and "a content engine" is not really a choice: you want the model inside the engine, doing the drafting while the engine does the production and distribution it was never built to do.
As a model, yes — it offers near-flagship intelligence on agentic and professional work at one-fifth of GPT-6 Astra's standard price, keeping GPT-6 Sol's low $2/$10 rate. It is not worth it as a content tool, because it generates no media and publishes nothing. Judge it as a drafting, coding, and reasoning layer, not as an end-to-end content solution.
It keeps GPT-6 Sol's standard token price but improves accuracy and agentic performance to near-flagship levels, halves cached input to $0.10 per million, and OpenAI reports a lower factual-error rate. At low reasoning effort the rate dropped from 11.4% on GPT-6 Sol to 7.7%, and it matched flagship Astra on a software-engineering benchmark.
OpenAI listed it at $2 per million input tokens and $10 per million output tokens, with cached input at $0.10 — the same standard rate as GPT-6 Sol, and exactly one-fifth of GPT-6 Astra's $10/$50. Confirm current figures on OpenAI's own pricing pages, as details vary by plan and region.
OpenAI says it nearly matches Astra on agentic coding, computer use, and professional work — within about 1.9% of Astra on its internal factuality measure and at parity on a software-engineering benchmark. These are self-reported figures, so treat them as OpenAI's claims and verify against your own use; Astra remains the top tier.
No. GPT-6.1 Sol is a text and reasoning model. It drafts, codes, reads documents, and executes multi-step workflows, but it produces no video, images, or audio. To turn its output into avatar video, carousels, quote graphics, or other media, you pair it with a content engine like Kompozy that generates those formats.
No. The model returns text; it does not log into accounts, schedule, or post anywhere. Publishing across platforms is a separate job. A tool like Kompozy takes drafts from a model and generates, schedules, and publishes finished posts across the eight social platforms plus blog and email.
Not exactly — they are complementary. Kompozy is a content generation and publishing engine that runs on models like GPT-6.1 Sol for the drafting step. The model handles reasoning and copy; Kompozy adds the media generation, brand governance, scheduling, and multi-platform publishing that a model does not do.