GPT-6.1 Sol Pricing: Near-Astra AI at One-Fifth the Cost for Ecommerce Video

By VEONIB | 2026-10-08

Quick Answer

GPT-6.1 Sol is an OpenAI model upgrade that nearly matches GPT-6 Astra's intelligence on agentic coding, computer use and professional work at one-fifth of Astra's standard token prices. It is priced at $2 per million input tokens, $0.10 per million cached input tokens and $10 per million output tokens, and is available in ChatGPT Work, Codex and the OpenAI API.

TL;DR

Table of Contents

According to GPT-6.1 Sol, published by OpenAI, the company has introduced a model that nearly matches GPT-6 Astra's intelligence on agentic coding, computer use and professional work at one-fifth of Astra's standard input and output token prices. The headline number is the cached input rate: $0.10 per million tokens, which OpenAI says is 95% below standard input pricing and half of what GPT-6 Sol charged for cached input.

For ecommerce teams, the significant detail is not the benchmark table. It is the economics of running agents that reuse the same context — brand guidelines, product catalogs, script templates — across hundreds of requests per day. This article separates what OpenAI actually published from what it implies, examines where the numbers deserve scrutiny, and explains how a cheaper near-frontier reasoning model changes the cost structure of AI video production for Shopify merchants, Amazon sellers and TikTok Shop operators.

Hero Image Alt Text: GPT-6.1 Sol pricing comparison chart showing input, cached input and output token costs for GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna Caption: GPT-6.1 Sol sits between Astra-class intelligence and Luna-class cost efficiency in OpenAI's GPT-6 family. OG Image Title: GPT-6.1 Sol: Near-Astra AI at One-Fifth the Price Suggested Visual: A clean three-column pricing comparison graphic on a dark background, with the cached input row highlighted.

What GPT-6.1 Sol Changes: Near-Astra Capability at One-Fifth the Price

Original Fact — OpenAI describes GPT-6.1 Sol as "an upgrade to GPT-6 Sol that nearly matches GPT-6 Astra's intelligence on agentic coding, computer use, and professional work at one-fifth of Astra's standard input and output token prices." The company states that cached input costs $0.10 per million tokens, which it characterises as 95% less than standard input pricing and 50% less than GPT-6 Sol's cached input pricing.

Availability is limited but broad. GPT-6.1 Sol is offered to Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work and Codex, and is not yet available in Chat. Developers can reach it through the OpenAI API under the model identifier gpt-6.1-sol. OpenAI also states it will soon offer a GPT-6.1 Sol Ultrafast variant with up to 8x faster token generation compared with standard speed in Codex.

The framing matters more than the feature list. OpenAI is positioning a mid-tier model as the default workhorse for agentic tasks, while reserving GPT-6 Astra for the small share of requests that genuinely need frontier reasoning. That is a routing decision as much as a product launch.

VEONIB Insight

Mid-tier models are where production volume actually lives. A model that matches the frontier on real codebases and multi-step workflows at one-fifth the cost changes whether an ecommerce team can afford to run agents on every SKU in a catalog rather than on a sample of ten.

For AI video generation, the immediate impact is upstream of the render. Product analysis, script drafting, storyboard structuring and prompt generation are text-heavy, context-reuse-heavy tasks — exactly the profile that benefits from cheap cached input. Businesses should adopt GPT-6.1 Sol now for script, prompt and quality-assurance layers, keep GPT-6 Astra for genuinely ambiguous product positioning work, and route trivial classification tasks to GPT-6 Luna. Teams that cannot yet operate an agent harness, or that lack structured product data, will get little from the price drop and can reasonably wait.

The GPT-6 Family Compared: Astra, Sol and Luna

OpenAI now presents three tiers with clearly separated economics: an intelligence tier, a capability-per-dollar tier and a volume tier. The pricing table below reflects the figures published in the GPT-6.1 Sol announcement.

Model Input ($/1M tokens) Cached input ($/1M) Output ($/1M) OpenAI's positioning
GPT-6 Astra $10.00 $1.00 $50.00 Most intelligent model for best results
GPT-6.1 Sol $2.00 $0.10 $10.00 Near-Astra intelligence at one-fifth the price
GPT-6 Luna $0.10 $0.01 $0.50 Fast, efficient everyday work at scale

The ratio between tiers is the practical takeaway. Astra's output tokens cost five times Sol's, and Sol's cached input is one-tenth of Astra's cached input. Relative to Luna, Sol is twenty times more expensive on input and output — so the question is never "which model is best" but "which task justifies which tier."

A sensible 2026 routing pattern is tiered rather than monolithic: Luna for tagging, deduplication and bulk classification; Sol for agentic workflows, code, document reasoning and prompt generation; Astra for the minority of tasks where a 2-percentage-point accuracy gain changes a business outcome, such as complex compliance review or hard scientific reasoning.

VEONIB Insight

Model routing has become a first-class engineering discipline. The cost of a video pipeline is dominated not by the video model alone but by everything that precedes it: catalog ingestion, attribute extraction, claim verification, script iteration and prompt refinement.

For ecommerce, the correct decision is to design workflows with tier boundaries built in from the start, so a single configuration change moves a step from Astra to Sol without rewriting prompts. Should businesses adopt now? Yes, if they already run agents in production and can measure cost per completed asset. If a team is still generating scripts manually in a chat window, the tier change is irrelevant — the bottleneck is process, not price.

Benchmark Reality Check: Coding, Documents, Workflows and Computer Use

Original Fact — OpenAI reports improvements across five evaluation areas. The table summarises the claims as published, without adjustment.

Benchmark What it measures GPT-6.1 Sol vs GPT-6 Sol GPT-6.1 Sol vs GPT-6 Astra Reported cost position
DeepSWE v1.1 Long-horizon software engineering in real codebases +6.4 pp at lower reasoning effort and cost Matches Astra Roughly one-fifth of Astra's cost
GDP.pdf Professional Q&A over complex PDFs with tables, charts and fine print Approaching Astra's state of the art Higher than Opus 5.5 with fallbacks at less than half the cost per task Roughly one-fifth of Astra's cost per task
AutomationBench 1.0.6 Multi-step business workflows across 47 tools +4.8 pp at medium reasoning effort 2.2 pp above Opus 5.5 at roughly one-third of the cost Roughly one-third of Opus 5.5's cost
OSWorld 2.0 (offline set) Long-horizon computer-use workflows +7 pp at maximum reasoning effort, at less than half the cost Within 2.1 pp at roughly one-seventh of the cost per task Roughly one-seventh of Astra per task
Terminal-Bench Science 0.1 Research workflows: data analysis, simulation, theorem proving More than doubles GPT-6 Sol's score at maximum effort Below Astra's 68.1% best score $5.47 per task vs $23.21 (Opus 5.5) and $23.80 (Astra)

Two footnotes in the source deserve attention. First, OpenAI notes that its AutomationBench datapoint for Claude Fable 5.1 "understates its actual cost, as it omits the cost of fallbacks, which occurred on ~40% of tasks." Second, OpenAI states that GPT evaluations ran in its research environment or via its API, which may differ slightly from production ChatGPT, and that competitor results were taken from publicly available reports. Suggested visual: a horizontal bar chart comparing cost per task across the five benchmarks, with Sol and Astra bars paired.

VEONIB Insight

Vendor-published benchmarks are directional, not decisive. The clause about fallback costs is the most instructive line in the announcement, because it reveals how agentic cost is usually measured incorrectly: a model that fails 40% of tasks and requires a second attempt is not cheap, even if its per-token price is low.

For ecommerce video production, the relevant metric is cost per completed asset, not cost per token. A team should instrument its own pipeline — track retries, human corrections and rejected outputs — before assuming a headline price cut translates into savings. Adopt GPT-6.1 Sol where task completion rates are already measurable; delay adoption for workflows where no baseline exists, because a cheaper model with an unmeasured failure rate simply moves cost from the invoice to the editor's calendar.

Factuality and Safety: Reading the Alignment Numbers Correctly

Original Fact — On factuality, OpenAI reports that GPT-6.1 Sol's largest improvement over GPT-6 Sol occurs at low reasoning effort, where the share of responses containing at least one factual error falls from 11.4% to 7.7%, a reduction of approximately 32%. Across the tested reasoning settings, its error rate remains within 1.9 percentage points of GPT-6 Astra at less than one-fifth of Astra's cost per task.

OpenAI is explicit about the limits of that evaluation: it measures de-identified conversations where users flagged a factual error from a prior model, and these "error-inducing conversations are not representative of typical usage, where factual errors are more rare." On safety, the company reports improvements over GPT-6 Sol in transparency about broken search tools, respecting explicit restrictions and avoiding unauthorised outcomes during agentic tasks, and states it observed no attempts to bypass an automated safety reviewer — matching both GPT-6 Astra and GPT-6 Sol. Full details are published in the GPT-6.1 Sol system card addendum.

VEONIB Insight

Factual accuracy is a cost variable in ecommerce, not a quality preference. A model that misreads a specification from a product PDF can generate a video claiming a material, capacity or certification the product does not have — a compliance and returns problem, not a copywriting one.

The 32% reduction on hard prompts is meaningful precisely because catalogue data is messy: supplier PDFs, spec sheets with footnotes and marketplace listings with fine print are exactly the document class where errors cluster. For video production, the practical step is verification, not trust: require every factual claim in a script to carry a source attribute from the product analysis stage, and let a second model pass validate it. Adopt Sol for document-grounded extraction now; keep human review for regulated categories such as supplements, medical devices and children's products.

Cached Input Pricing and the Agent Economics of Ecommerce Video

Prompt caching rewards a specific workflow shape: the same large context reused across many requests. An ecommerce video pipeline fits this shape unusually well, because brand guidelines, tone-of-voice rules, catalogue metadata and script templates are stable, while the product being described changes every time.

The arithmetic, based on OpenAI's published rates, is straightforward. One million cached input tokens costs $0.10 against $2.00 for standard input — a 95% reduction. A workflow that reprocesses ten million tokens of reusable context per day would cost roughly $1.00 in cached input instead of approximately $20.00 at standard rates. This is a VEONIB calculation derived from published prices, not a figure from the source.

The practical implication is that context reuse should be designed deliberately. Long, stable system prompts are now cheap; long, changing contexts are not. Teams that concatenate fresh catalogue dumps on every call will pay standard rates and see little benefit.

VEONIB Insight

Prompt caching quietly changes architecture. When stable context is nearly free, it becomes rational to include a full brand style guide, a banned-claims list and a genre library in every request rather than compressing them into a short prompt that loses nuance.

For AI video generation, this improves consistency across a batch: the same visual rules applied to three hundred products produce a more coherent catalogue than three hundred individually tuned prompts. Should businesses adopt now? Yes, for any workflow generating more than a few dozen assets a month — the caching benefit scales with volume and repetition. Teams producing fewer than twenty assets per month will not notice the difference and can postpone the engineering work.

What GPT-6.1 Sol Means for Shopify, Amazon and TikTok Shop Sellers

The most commercially relevant benchmark in the announcement is AutomationBench 1.0.6, which tests end-to-end business workflows using 47 tools across sales, marketing, operations, support, finance and HR. Tool-use performance at that scale is what separates a chatbot that drafts copy from an agent that updates listings, checks inventory, reads reviews and schedules content.

Combined with the OSWorld 2.0 computer-use results, this points to a near-term operational pattern: agents that interact with seller central interfaces, advertising dashboards and content calendars rather than waiting for an API integration that may not exist. That matters most for Amazon sellers and TikTok Shop operators, whose workflows are frequently interface-bound rather than API-bound.

Availability shapes who benefits first. GPT-6.1 Sol is available in ChatGPT Work and Codex and through the API, but not in Chat — so teams that buy a consumer subscription get nothing, while teams building on the API or operating inside Codex can act immediately.

VEONIB Insight

Computer-use capability is the quiet unlock for mid-market ecommerce. Most sellers run businesses across platforms that expose partial APIs and require manual work in dashboards; an agent that can operate those interfaces removes a genuine bottleneck.

The caveat is governance. An agent with dashboard access can also change a price, pause a campaign or publish a listing. Before adopting, define which actions require approval, log every state change, and set spend limits at the platform level rather than relying on prompt instructions. Adopt now for read-heavy and draft-heavy tasks such as review mining, competitor monitoring and listing QA; wait on autonomous publishing until audit logging and rollback are in place.

Fitting GPT-6.1 Sol into the VEONIB AI Video Pipeline

GPT-6.1 Sol is a reasoning and orchestration model. It does not render images, synthesise speech or generate video, so questions about motion quality, text rendering inside frames, character consistency and product consistency are determined by the image and video models a pipeline selects — not by this release.

What Sol affects is everything either side of the render. The table maps its likely contribution to each stage of the VEONIB workflow.

Pipeline stage GPT-6.1 Sol contribution Notes for ecommerce teams
Product URL → Product Analysis High Long-context document reasoning over spec sheets, listings and PDFs
Product Analysis → Script High Structured output, tone and claim discipline
Script → Storyboard Medium–high Reliable JSON-style structured generation
Storyboard → Image Prompt High Prompt engineering at scale with shared cached context
Image Prompt → Video Prompt High Camera and motion phrasing, batch consistency
Video generation None Requires dedicated video models such as Runway, Pika, Kling, Veo, Sora, Seedance or Hailuo
Voice and avatar Low Handled by specialised speech and avatar tools such as HeyGen or MiniMax
Subtitle and localisation Low–medium Segmentation and terminology consistency
Publishing and QA Medium Agentic tool use for uploads, metadata and checks

The pipeline fit is therefore strong but partial: GPT-6.1 Sol is a control layer, not a production layer. Suggested visual: a vertical pipeline diagram with a highlighted band spanning Product Analysis through Video Prompt, labelled "reasoning layer".

VEONIB Insight

The most common error in AI video evaluation is judging a language model on visual output. Sol cannot be assessed on motion quality or product consistency because it does none of that work; it determines whether the prompts given to a video model are accurate, specific and repeatable.

Where Sol genuinely helps is prompt controllability and production speed: converting a messy product page into a structured brief, then into prompts that reference the same product attributes every time. That consistency is what makes catalogue-scale generation possible for product ads, TikTok ads, Meta ads, YouTube Shorts, Amazon product videos, Shopify product pages, brand story videos, UGC-style videos, lifestyle videos and product demo videos. Adopt immediately for the analysis-to-prompt band; keep rendering decisions separate, and evaluate each video model on its own merits. Reserve GPT-6 Astra for the minority of scripts where narrative strategy is genuinely hard, and use GPT-6 Luna for bulk subtitle segmentation and metadata tagging.

Risks, Limitations and When to Wait

The announcement is unusually candid, and several constraints should be read as prominently as the benchmark gains. GPT-6.1 Sol is not yet available in Chat, so the model is inaccessible to teams that work primarily through the consumer interface. Ultrafast is described as coming "in the coming days," which means it is not yet a production assumption.

Evaluation caveats matter too. Factuality results come from error-flagged conversations and are explicitly described as unrepresentative of typical usage. Safety evaluations "deliberately test challenging situations and do not measure failure rates in typical use." Competitor comparisons rely partly on published reports, and OpenAI itself notes that at least one competitor datapoint omits fallback costs that occurred on roughly 40% of tasks.

Cost risk is real in agentic loops. A cheaper model that retries three times may be more expensive than an expensive model that succeeds once, particularly in workflows that chain tool calls.

VEONIB Insight

The correct adoption test is not price per token but total cost per accepted output, including retries, human review and downstream correction. Teams that track that number can move to Sol with confidence; teams that do not are guessing.

Waiting is preferable in three scenarios: workflows with no measurement baseline, regulated product categories where verification is expensive, and tasks that genuinely require frontier reasoning, where Astra remains the documented leader at 68.1% on Terminal-Bench Science 0.1. Everyone else should pilot Sol on one narrow, high-volume step — product page analysis is the natural candidate — and expand only after a completion-rate baseline exists.

Recommendations

Shopify Merchants. Pilot GPT-6.1 Sol on product page analysis and script generation for your ten best-selling SKUs, and measure cost per published video against your current process. Keep cached brand guidelines in every request to exploit the $0.10 cached input rate.

Amazon Sellers. Focus on document-heavy work: spec sheets, compliance documents and A+ content briefs, where GDP.pdf-style reasoning applies. Do not grant autonomous listing or pricing authority until every agent action is logged and reversible.

TikTok Shop Sellers. Use Sol to convert fast-moving product pages into hooks and UGC-style scripts, then batch-generate variants. Track completion rate per variant rather than token spend, since creative volume is your cost driver.

AI Developers. Instrument retries and rejection rates before switching models. Build tier routing so steps can move between Luna, Sol and Astra through configuration, and use structured output schemas to keep downstream prompt generation stable.

SaaS Founders. Treat the cached-input price as a product constraint: features that reuse stable customer context — brand kits, tone libraries, catalog rules — now have a defensible gross margin. Re-price any feature that concatenates fresh context on every call.

Content Marketers. Standardise a claims-verification step between analysis and script, so every factual assertion in a video traces back to a source attribute. This is the cheapest risk control available.

Video Creators. Use Sol for storyboard structuring and prompt refinement, and keep evaluating rendering models separately. Do not change your video model because your language model got cheaper.

FAQ

What is GPT-6.1 Sol? GPT-6.1 Sol is an OpenAI model positioned between GPT-6 Sol and GPT-6 Astra. OpenAI states it nearly matches Astra's intelligence on agentic coding, computer use and professional work at one-fifth of Astra's standard input and output token prices.

How much does GPT-6.1 Sol cost? OpenAI lists $2 per million input tokens, $0.10 per million cached input tokens and $10 per million output tokens. Cached input is described as 95% below standard input pricing and 50% below GPT-6 Sol's cached input rate.

Is GPT-6.1 Sol better than GPT-6 Astra? No. OpenAI describes Astra as its most intelligent model and recommends it for the hardest scientific research tasks, where it holds the highest tested score at 68.1% on Terminal-Bench Science 0.1. Sol's advantage is cost efficiency, not peak capability.

Can GPT-6.1 Sol generate ecommerce videos? No. It is a reasoning and orchestration model that handles product analysis, scripts, storyboards and prompts. Image and video generation require separate models, and speech, avatar, subtitle and publishing steps need specialised tools.

Where is GPT-6.1 Sol available? OpenAI states it is available to Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work and Codex, and through the API as gpt-6.1-sol. It is not yet available in Chat, and an Ultrafast variant with up to 8x faster token generation in Codex is described as coming soon.

Should ecommerce teams switch immediately? Teams with measurable pipelines should pilot it on high-volume text steps such as product analysis and script generation. Teams without a completion-rate baseline, or with heavy verification requirements in regulated categories, should establish measurement first.

References

Sources

Try VEONIB

VEONIB converts a Product URL into Product Analysis, Video Scripts, Storyboards, Image Prompts, Video Prompts and finished AI marketing videos through a single automated workflow. Teams evaluating cheaper reasoning models can route their analysis and prompt stages accordingly while keeping rendering, voice and publishing in one pipeline. See how it works at VEONIB AI video generation platform.

Credibility Assessment

From the source. All pricing figures, benchmark names, benchmark results, availability statements, factuality percentages, safety claims and the note about omitted fallback costs come directly from the GPT-6.1 Sol announcement published by OpenAI. Quoted phrases are reproduced as written.

VEONIB analysis. The model routing recommendations, cost-per-completed-asset argument, caching arithmetic, pipeline mapping, adoption guidance and risk framing are VEONIB's interpretation. They are not stated by OpenAI and should be validated against your own production data.

Uncertain. The source does not specify the original publication date of the announcement, so the byline date reflects VEONIB's publication date only. Independent reproduction of the reported benchmark results is not yet available, and the Ultrafast variant's performance characteristics are described as forthcoming rather than measured. Claims about specific third-party video and avatar tools in this article reflect their documented product categories, not benchmarked comparisons run for this piece.