Comparisons  ·  AI Tools

OpenCraft AI vs ChatGPT:Comparison, Pricing, and Use Cases(2026)

OpenAI's closed GPT-6 lineup vs open multi-provider models, subscription limits vs pay-as-you-go, compared field by field.

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By Mahesh ● 13 min ● 26 October 2026

Updated October 26, 2026 | 13 min read

ChatGPT is the default answer to “what AI assistant should I use.” It is also the product people search for alternatives to. So this comparison matters, and it is worth doing properly.

ChatGPT is a single-vendor assistant with the most feature-dense consumer surface in the category and a mature coding agent (Codex). OpenCraft AI is a multi-model platform: open-weight models from several providers, execution in an isolated sandbox, and project-level memory.

This page works through each documented field for both products. Every fact was checked against provider documentation on October 26, 2026.

OpenCraft AI vs ChatGPT: Quick Answer

  • ChatGPT: frontier-class closed models from one vendor, the most feature-dense consumer assistant, and a mature coding agent (Codex).
  • OpenCraft AI: frontier-class open-weight models from multiple providers, automatic failover, sandboxed execution, and cross-session project memory.
  • Pricing is the opposite model. ChatGPT is subscription with usage limits. OpenCraft AI is pay-as-you-go with no rate limits.
  • Neither is a general upgrade of the other. The sections below show where each fits.

What ChatGPT and OpenCraft AI Actually Are

ChatGPT (OpenAI)

A general-purpose assistant and agent platform from OpenAI. It runs only OpenAI models.

It ships as a web app, iOS and Android apps, and a desktop app. Codex covers CLI, cloud, desktop, and IDE. ChatGPT Work, the API, and Azure OpenAI round out the surface.

OpenCraft AI

A multi-model agent platform from OpenCraft AI. The in-product assistant is called Ocai. It runs open-weight models from multiple providers.

You choose the model per request, or the platform fails over between providers automatically. Work runs in an isolated cloud sandbox.

The product has two surfaces: a web app and a desktop app. The desktop app is documented as a taskbar-resident client, so the assistant is available without a browser tab.

Reference Profiles: ChatGPT and OpenCraft AI

Both profiles use the same fields in the same order, so you can compare them line by line.

ChatGPT

Company: OpenAI
Product: ChatGPT
Primary category: General-purpose AI assistant and AI agent platform

Models:

  • Single provider: OpenAI only (as of October 26, 2026).
  • Current flagship: GPT-6 Astra (top tier), GPT-6.1 Sol (mid), GPT-6 Luna (fast/light).
  • All latest models document 1.05M-token context and 128K max output.
  • Specialized: GPT-5.6 Cyber (security), GPT-Rosalind (life sciences), GPT-Image-2.5 (image), GPT-Live / GPT-Realtime (voice).
  • Legacy: GPT-5.5 (retired from consumer surfaces August 2026), GPT-5.6 Terra and Luna, GPT-5.1 (removed from ChatGPT March 11, 2026).
  • Users choose models manually in the app and API.
  • No automatic cross-model failover.
  • No third-party models.

Pricing:

  • Free $0.
  • Go (low-cost tier, price varies by region).
  • Plus $20/mo.
  • Pro $200/mo.
  • Business, Edu, Enterprise: per seat, custom.
  • API per MTok: GPT-6 Astra $10/$50, GPT-6.1 Sol $2/$10, GPT-6 Luna $0.10/$0.50.
  • ChatGPT Work and Codex are included in every plan.
  • Usage limits apply on subscription plans.

Context window & conversation context:

  • All latest models document 1.05M-token context and 128K max output.
  • Projects add workspace context: files and custom instructions shared across a project’s chats.
  • Cross-chat memory on paid plans.
  • For heavy users, usage limits are the practical constraint, not the context window.

Integrations:

  • Custom GPTs.
  • Projects.
  • Connectors and apps.
  • ChatGPT agent (its own virtual computer, browsing).
  • Codex (CLI, cloud, desktop, IDE).
  • API, Azure OpenAI.

Agent capabilities:

  • ChatGPT agent: multi-step tasks on its own virtual computer, shifting between reasoning and action.
  • Workspace agents (evolution of GPTs, powered by Codex): reports and work tasks.
  • Codex: long-horizon coding tasks in CLI, cloud, and desktop apps.
  • Computer use tool on GPT-6 models.
  • Scheduled tasks, including agent requests.

Memory:

  • Cross-chat memory on paid plans.
  • Projects provide project-scoped context.
  • Temporary chats do not create or update memories.

File handling:

  • Uploads: PDFs, images, code, spreadsheets, common document formats.
  • Projects hold files as workspace context.
  • Codex reads and writes files in local and cloud repos.
  • ChatGPT Slides (October 16, 2026).

Research:

  • Web access: built-in web search on GPT-6 models.
  • Multi-source research: Deep Research (legacy mode removed March 26, 2026).
  • Citation generation: yes.
  • Deep research: now part of ChatGPT Work and Codex.
  • Research outputs: cited answers and reports.

Coding:

  • Codex is the dedicated coding agent: CLI, cloud, desktop, IDE.
  • Long-horizon coding tasks.
  • GPT-6 Astra positioned for complex coding.
  • Code execution in the agent’s virtual computer.

Automation:

  • Scheduled tasks, including agent requests.
  • Workspace agents for recurring work tasks.
  • Codex long-horizon tasks.

Privacy & data use:

  • Per OpenAI’s published policy (updated October 20, 2026).
  • Individual (Free, Plus, Pro): content may be used for model training by default.
  • Opt out via “Improve the model for everyone” (Settings > Data controls) or the Privacy Portal.
  • Even after opting out, feedback (thumbs up/down) may train on the whole conversation for individual plans.
  • Business (Business, Enterprise, Edu, API): no training on inputs or outputs by default.
  • Temporary chats: no training, no memory.
  • Codex: separate “Include environments” setting.

Deployment:

  • Web (chatgpt.com), iOS, Android, desktop (macOS).
  • Codex (CLI, cloud, desktop, IDE).
  • API, Azure OpenAI.

Ideal use cases:

  • General-purpose chat and reasoning.
  • The most feature-dense consumer assistant (Custom GPTs, Projects, memory, Slides).
  • Agentic coding (Codex).
  • Multi-step tasks with scheduled automation.
  • Long-document analysis within a 1.05M-token window.

Limitations:

  • Rate limits on the Plus plan are a documented pain point for heavy users.
  • Single provider, no cross-provider failover.
  • Fast model churn (GPT-5.2 through 5.5 in under a year, GPT-6 now).
  • Consumer privacy: training on by default, opt-out.
  • Vendor lock-in: older model lines are deprecated.

Last verified: October 26, 2026

OpenCraft AI

Company: OpenCraft AI
Product: OpenCraft AI (in-product assistant name: Ocai)
Primary category: Multi-model AI agent platform

Models:

  • Open-weight models from multiple providers (as of October 26, 2026).
  • Ocai Swift, Ocai Core, Ocai Ultra (OpenCraft’s own models).
  • Ocai Ultra is positioned as frontier-class deep reasoning in the product documentation.
  • DeepSeek v4 Pro.
  • Kimi K3 (documented as massive-context).
  • Product documentation also lists DeepSeek v4 Flash and Z-ai/GLM-5.2.
  • Any specific open model on request.
  • Canonical current list: opencraftai.com/models.
  • User selection per request.
  • Automatic failover between providers.

Pricing:

  • Pay-as-you-go credits only.
  • No subscription, no tiers.
  • $1 = 10 credits (also INR, RUB, CNY at fixed rates).
  • 100 free credits to start, no card required.
  • No rate limits.
  • Credits never expire.
  • Rates in credits per 1M tokens (input/cached/output).
  • Ocai Swift 0.5/0.25/4.0.
  • Ocai Core 0.786/0.393/1.572.
  • Ocai Ultra 5.0/2.5/31.5.
  • DeepSeek v4 Pro 6.6/3.3/19.8.
  • Kimi K3 26/13/130.
  • If the balance runs out, usage pauses until top-up.
  • No retroactive charges.

Context window & conversation context:

  • Per-model context window numbers are not published as documented figures.
  • Kimi K3 is documented as having a “massive context window.”
  • Long-running context is sustained at the product level.
  • Named projects carry their own preferences, memory, files, and conventions.
  • Collections provide persistent file storage.
  • File storage is git-versioned.
  • Cross-session memory carries decisions and context forward.

Integrations:

  • File-based integration rather than SaaS connectors.
  • Uploads: PDF, Word, Excel, CSV, JSON, PowerPoint, HTML, code, images.
  • Web pages and YouTube (transcripts).
  • Cloud Files for permanent storage.
  • Workspace export: PDF, Word, HTML, plain text.
  • Shareable read-only chat links.
  • No documented native SaaS connectors (Google Drive, Slack, Microsoft 365) at time of verification.

Agent capabilities:

  • Isolated sandbox: code execution (Python, shell), file creation and editing.
  • Git versioning on files.
  • Subagents: one request decomposes into parallel subtasks.
  • Agentic execution: goal hand-off, end-to-end completion.
  • Safe to run unattended.
  • Adaptive model routing: on error or stall, the request fails over to the next provider.

Memory:

  • Cross-session: narrative journal of what happened and what was decided.
  • Semantic recall across past conversations (not keyword matching).
  • Preferences: global and per-project.
  • Decisions and project context carried forward.
  • Per-project memory isolation.

File handling:

  • Uploads: PDF, Word, Excel, CSV, JSON, PowerPoint, HTML, code, images.
  • Collections: grouped, reusable file hubs.
  • File versioning: every save is a git commit, full history.
  • Shareable file URLs.
  • Workspace: editable documents with AI editing, export to PDF/Word/HTML/text.

Research:

  • Web access: Assistant mode, real-time web search.
  • Multi-source research: Deep Research mode.
  • Citation generation: Deep Research delivers cited reports.
  • Deep research: Deep Research (autonomous).
  • Deep Dive (fast root-cause analysis).
  • Source controls: File Assistant mode restricts the assistant to uploaded documents only.
  • Research outputs: cited reports in chat, pushable to the workspace as exportable documents.

Coding:

  • Sandboxed execution: Python, shell, interpreted languages.
  • Data science stack: pandas, numpy, scipy.
  • Build, test, and debug in real time.
  • Visualizations: matplotlib, plotly.
  • Read, write, and organize files.
  • Execution happens in the cloud sandbox, not on the user’s machine.

Automation:

  • Unattended long-running execution: “run it overnight,” “keep working until it’s done.”
  • Autonomous mode for delegated goals.
  • Scheduled/recurring (cron-style) tasks: not documented at time of verification.

Privacy & data use:

  • Per the provider’s public statements (October 26, 2026).
  • “Your conversations never train models. Zero training on your information.”
  • SOC 2 compliance and encryption claimed.
  • “Complete data control with clear deletion policies.”
  • A standalone privacy policy document was not located on opencraftai.com at time of verification.
  • Statements above are from the product page and product documentation.

Deployment:

  • Cloud web app (opencraftai.com).
  • Desktop app: opencraftai.com/download, documented as a taskbar-resident client.
  • No documented public API, mobile app, or self-hosted option at time of verification.

Ideal use cases:

  • Multi-step professional workflows.
  • Multi-model tasks where failover matters.
  • Autonomous research that ends in a cited report.
  • File/code-based work: analysis, document processing, code execution.
  • Workflows requiring persistent project context across sessions.

Limitations:

  • No documented public API or self-hosted deployment.
  • No documented native SaaS connectors.
  • Integration is file-based.
  • No documented scheduled/recurring automation.
  • Per-model context window numbers not published as documented figures.
  • Lineup is open-weight: no closed frontier models (Claude, GPT, Gemini).
  • Ocai Ultra is the frontier-class option in the lineup.
  • Younger product with a smaller third-party ecosystem than Claude, ChatGPT, or Gemini.

Last verified: October 26, 2026

OpenCraft AI vs ChatGPT: Side-by-Side

ChatGPT OpenCraft AI
Type General-purpose assistant and agent platform Multi-model agent platform
Models OpenAI only (GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna) Multi-provider open-weight (Ocai Swift/Core/Ultra, DeepSeek v4 Pro, Kimi K3, + any open model)
Frontier-class model Yes (closed) Yes (open-weight, Ocai Ultra)
Model failover No Yes, automatic
Documented context window 1.05M tokens / 128K output per model Not published per model (Kimi K3: “massive”); project workspace sustains long work
Runs code Yes (agent virtual computer; Codex local/IDE) Yes (isolated cloud sandbox)
Creates files Yes (in-app; Codex in repos) Yes (versioned, shareable)
Cross-session memory Yes (paid plans) + Projects Yes, project-level (semantic recall, per-project)
Subagents No (single agent) Yes (parallel, per request)
Unattended execution Via Codex / API Yes, documented
Scheduled/recurring tasks Yes (scheduled tasks, incl. agent requests) Not documented
Native SaaS connectors Yes (Custom GPTs, Projects, connectors, Azure) No (file-based)
Rate limits Yes (subscription usage limits) No (credit balance only)
Pricing model Subscription + API per-token Pay-as-you-go credits only
API Yes Not documented
Desktop app Yes (macOS) Yes (taskbar-resident)
Mobile apps Yes (iOS, Android) Not documented
Deployment Web, mobile, desktop, CLI, API, Azure Cloud web app + desktop app

OpenCraft AI vs ChatGPT: Field by Field

Models: closed single-vendor lineup vs open multi-provider lineup

ChatGPT’s lineup is closed and single-vendor. GPT-6 Astra is OpenAI’s flagship for complex reasoning and coding.

GPT-6.1 Sol balances intelligence and cost. GPT-6 Luna covers high-volume, cost-sensitive work.

You choose the model, and the product does not route between models for you. If Astra has a bad day on your task, you wait for OpenAI to fix it or ship a new model.

OpenCraft AI’s lineup is open-weight and multi-provider. It runs its own Ocai line (Swift, Core, Ultra), DeepSeek v4 Pro, and Kimi K3.

Any specific open model is available on request. Ocai Ultra is positioned as frontier-class deep reasoning in the product documentation.

So both lineups reach the frontier tier. The difference is closed versus open, single-vendor versus multi-provider, and what happens when a model stalls. In OpenCraft’s chain, a stalled or errored model hands the request to the next provider, and the task continues.

That trade runs both ways. A closed single-vendor lineup means one deprecation policy, one pricing page, and one point of failure. A multi-provider open lineup means you can name the model you want, and no single vendor gates your workflow.

AI model sovereignty is the longer version of that argument. OpenAI’s own GPT-5.5 retirement is the example. If you already juggle model subscriptions, how to use multiple AI models without micromanaging any of them covers the setup.

Context window & conversation context

ChatGPT documents the numbers: 1.05M-token context and 128K max output on every latest model (as of October 26, 2026). That is the largest documented window in this comparison. Projects add workspace context on top: files and custom instructions shared across a project’s chats.

OpenCraft AI does not publish per-model window numbers. Kimi K3 is documented as having a massive context window. The product-level mechanism for long work is the project workspace: named projects with their own preferences, memory, and files; collections as persistent storage; git-versioned files; and cross-session recall.

The two approaches solve different problems. A 1.05M-token window means ChatGPT can hold a very large document or conversation in one interaction. Whether the work survives the interaction is a separate question.

ChatGPT’s answer is Projects plus paid-plan memory. OpenCraft’s answer is the project workspace.

Each side has a verifiable gap. For ChatGPT’s heavy users, usage limits are the practical constraint, not the context window. OpenAI documents usage limits on subscription plans, and the Plus plan is where they bite hardest.

We covered why the $20 plan stalls on real work. For OpenCraft, you cannot verify from published documentation that a single 1M-token document fits in one request. If you have felt context window amnesia in a long task, persistence is the fix, and both products now address it in different ways.

Agent capabilities

Both products execute work. The comparison is where each execution surface is strongest.

ChatGPT’s agent surface is the most mature in the consumer category. The ChatGPT agent runs multi-step tasks on its own virtual computer, shifting between reasoning and action.

Workspace agents (powered by Codex) take on work tasks like preparing reports. Codex handles long-horizon coding in CLI, cloud, and desktop apps.

OpenCraft AI’s agent surface is strongest where the work is a mix of files, analysis, and deliverables. The sandbox runs code and creates files, git-versioned. One request can spawn parallel subagents, so research, analysis, and drafting happen at the same time.

Model failover is built in, so a long task does not die because one provider timed out.

The practical split: if you want a consumer agent that handles scheduled, recurring, and work tasks out of the box, ChatGPT is further along. If the work is a batch of files in and a deliverable out, and you want parallel subagents and failover, OpenCraft’s sandbox is built for that.

It keeps running while you do something else. Which AI is better than ChatGPT? runs ten tools through this question.

Memory

ChatGPT has cross-chat memory on paid plans. Projects add project-scoped context. Temporary chats do not create or update memories.

OpenCraft AI carries work context across sessions at the project level. A journal records what happened and what was decided. Recall is semantic across past conversations.

Preferences layer globally and per-project. Projects are isolated from each other, so switching does not mix context.

The two memories do different jobs. ChatGPT’s memory personalizes the assistant to you and scopes it to a project. OpenCraft’s memory holds the state of the work: decisions, files, and where a project left off.

If your value is in the work rather than the preferences, the second kind is the one that compounds. We wrote about why retrieval was never the hard part in agent memory.

Research

ChatGPT: built-in web search on GPT-6 models, Deep Research (legacy mode removed March 26, 2026), and citations throughout. Deep research now lives inside ChatGPT Work and Codex.

OpenCraft AI: four modes. Assistant for real-time web search. Deep Research for autonomous multi-source investigation with cited reports.

File Assistant to lock the assistant onto your documents only, with no web access. Deep Dive for fast root-cause analysis.

Two details matter. File Assistant mode is a source-control feature: when the answer has to come from your files and nothing else, you can enforce that. And Deep Research outputs can be pushed to the workspace as exportable documents, so a research run ends in a file, not a chat thread.

ChatGPT’s models are positioned for frontier reasoning over sources. For file-grounded research where the sources are your own documents, OpenCraft’s mode structure fits better. Claude vs ChatGPT is the companion comparison for the closed-model side of this trade.

Coding

Codex is OpenAI’s dedicated coding agent. It runs in the CLI, cloud, desktop, and IDE, and it handles long-horizon coding tasks. GPT-6 Astra is positioned for complex coding.

If your work is code in repos and you live in an IDE, this is the more direct tool.

OpenCraft AI executes code in a cloud sandbox: Python, shell, the data science stack, build/test/debug in real time, visualizations, and git versioning on everything it writes. It does not touch your local machine.

For data work, scripts, analysis pipelines, and code-adjacent deliverables, the sandbox covers the full cycle: run, test, debug, visualize, and save. For day-to-day repo development, it complements a tool like Codex rather than replacing it.

Automation

This is where the two products diverge most clearly.

ChatGPT documents scheduled tasks, including agent requests. Workspace agents handle recurring work tasks.

Codex runs long-horizon tasks. If you want “run this every Monday,” ChatGPT is the one that documents it.

OpenCraft AI documents unattended long-running execution: “run it overnight” and “keep working until it’s done,” in a sandbox that cannot damage your system. Scheduled recurring tasks are not documented at time of verification.

So ChatGPT wins the scheduled-recurring question on paper. OpenCraft wins the run-to-completion question. Neither is a cron scheduler in the developer sense.

Integrations

ChatGPT has the broader documented surface. Custom GPTs and Projects structure the consumer experience. Connectors and apps extend it.

Codex lives in your terminal, cloud, and IDE. Deployment reaches Azure OpenAI.

OpenCraft AI integrates through files: uploads (PDF, Word, Excel, CSV, JSON, PowerPoint, HTML, code, images), web pages, YouTube transcripts, collections, and Cloud Files. There are no documented native connectors to Google Drive, Slack, or Microsoft 365.

If your workflow needs the assistant to live inside your SaaS stack, that is a real gap to weigh. If your workflow is file-based, the gap does not apply.

Privacy & data use (factual, sourced)

ChatGPT, per OpenAI’s published policy (updated October 20, 2026): for individual plans (Free, Plus, Pro), content may be used for model training by default. You opt out via “Improve the model for everyone” in Settings, or the Privacy Portal. Even after opting out, feedback (thumbs up/down) may train on the whole conversation for individual plans.

For business plans (Business, Enterprise, Edu, API), inputs and outputs are not used for training by default. Temporary chats are never used for training and do not create memories. Codex has a separate “Include environments” setting.

OpenCraft AI, per the provider’s public statements (October 26, 2026): “Your conversations never train models. Zero training on your information.” SOC 2 compliance and encryption are claimed, along with “complete data control with clear deletion policies.”

One caveat: we could not locate a standalone privacy policy document on opencraftai.com at time of verification. The statements above trace to the product page and product documentation.

The postures are genuinely different, and this is the field where the three major products disagree most. ChatGPT consumer trains by default (opt-out). Claude does not train by default (opt-in).

OpenCraft states conversations never train models. Read each primary policy before deciding.

Pricing

ChatGPT, as of October 26, 2026: Free, Go (low-cost, region-dependent), Plus $20/mo, Pro $200/mo, Business/Edu/Enterprise per seat. The API is per-token (Astra $10/$50, Sol $2/$10, Luna $0.10/$0.50 per MTok). ChatGPT Work and Codex are included in every plan.

Usage limits apply on subscription plans.

OpenCraft AI, as of October 26, 2026: credits only. The rate is $1 = 10 credits, with per-model pricing in credits per 1M tokens (0.5 input on Ocai Swift to 26 on Kimi K3).

100 free credits to start, no card required. No rate limits, and credits never expire.

If the balance runs out, work pauses until a top-up. There are no retroactive charges.

The math depends on volume. Light users typically pay less on ChatGPT Plus. Heavy users on ChatGPT often pay for Pro and still hit usage limits, which is the pattern documented here.

On pay-as-you-go, cost scales with actual usage, with no ceiling. The best Claude alternative without rate limits post covers the cost side of this comparison.

Deployment

ChatGPT: web, iOS, Android, desktop (macOS), Codex (CLI, cloud, desktop, IDE), the API, plus Azure OpenAI for cloud deployment.

OpenCraft AI: a cloud web app and a desktop app. The desktop app is documented as a taskbar-resident client, so the assistant is available without a browser tab. No public API or self-hosted option is documented at time of verification.

Where ChatGPT Wins Against OpenCraft AI

  • Most feature-dense consumer assistant. Custom GPTs, Projects, memory, and Slides.
  • Documented scheduled tasks, including agent requests.
  • Largest documented context window. 1.05M tokens and 128K output per model (as of October 26, 2026).
  • Mature coding agent. Codex runs in CLI, cloud, desktop, and IDE.
  • Broadest deployment surface. Web, mobile, desktop, CLI, API, Azure.

Where OpenCraft AI Wins Against ChatGPT

  • No rate limits. The only limit is the credit balance. No usage window, no weekly pool.
  • Multi-model failover. If a model stalls, the next provider in the chain takes over mid-task.
  • Frontier-class open-weight models. Ocai Ultra is positioned as frontier-class deep reasoning, with no single-vendor gate.
  • Project-level memory. Cross-session, per-project, with semantic recall and per-project isolation.
  • File-based execution with versioning. Code runs, files are created, every save is a git commit.
  • Parallel subagents. One request decomposes into parallel subtasks.
  • Unattended execution. Long tasks run overnight in an isolated sandbox.
  • Privacy posture. Conversations never train models, versus ChatGPT’s opt-out default.

Limitations: ChatGPT and OpenCraft AI

ChatGPT’s are documented by OpenAI itself. Usage limits apply on subscription plans, and the Plus plan is where they bite hardest for heavy users.

The lineup is single-provider, with no cross-provider failover. The model lineup has turned over fast (GPT-5.2 through 5.5 in under a year, GPT-6 now), and older lines are deprecated.

OpenCraft AI’s are documented in its own product pages and documentation. There is no public API or self-hosted option, no native SaaS connectors, and no documented scheduled automation.

Per-model context window numbers are not published. The lineup is open-weight, so it does not include closed frontier models (GPT, Claude, Gemini), though Ocai Ultra is positioned as frontier-class.

A younger product also means a smaller third-party ecosystem than ChatGPT.

Ideal Use Cases for ChatGPT and OpenCraft AI

ChatGPT: general-purpose chat and reasoning. The most feature-dense consumer assistant. Agentic coding (Codex).

Multi-step tasks with scheduled automation. Long-document analysis within a 1.05M-token window.

OpenCraft AI: multi-step professional workflows. Multi-model tasks where failover matters. Autonomous research that ends in a cited report.

File and code work: analysis, document processing, code execution, data visualization. Workflows that need persistent project context across sessions, where re-explaining context every conversation is the real cost.

OpenCraft AI vs ChatGPT: FAQ

Is OpenCraft AI a ChatGPT alternative?

It depends what you need ChatGPT for. If you need OpenAI’s closed models specifically (GPT-6 Astra, GPT-6.1 Sol), no: OpenCraft AI runs open-weight models and does not include OpenAI’s models.

If you need frontier-class reasoning without a single-vendor dependency, Ocai Ultra is the frontier-class model in OpenCraft’s lineup. If you need what most people actually use ChatGPT for (multi-step work, research, file handling, and not hitting a limit mid-afternoon), that is what OpenCraft AI is built for.

Can OpenCraft AI use ChatGPT models?

No. The model chain is open-weight models from multiple providers: Ocai Swift/Core/Ultra, DeepSeek v4 Pro, Kimi K3, and any specific open model on request.

OpenAI’s models are closed, so they are not in the chain. If you need GPT models specifically, use ChatGPT or the OpenAI API.

Which is cheaper for heavy daily use?

ChatGPT: $20 to $200 per month subscription with usage limits, or per-token API pricing. OpenCraft AI: per-credit pricing, no subscription, no rate limits.

Light users typically pay less on ChatGPT Plus. Heavy users on ChatGPT often pay for Pro and still hit the limits.

On pay-as-you-go, cost scales with actual usage. The usage limits analysis has the numbers for ChatGPT’s side.

Does OpenCraft AI have cross-session memory?

Yes, at the project level: a journal of decisions and progress, semantic recall across past conversations, and preferences that layer globally and per-project. Projects are isolated from each other, so switching context does not leak.

Is ChatGPT private?

Per OpenAI’s published policy: for individual plans, content may be used for model training by default. You opt out via Settings or the Privacy Portal. Business plans do not train on inputs or outputs by default.

The primary policy has the full conditions.

Can I use both?

Yes. ChatGPT for general-purpose chat, consumer agents, and IDE-based coding. OpenCraft AI for file-heavy workflows, autonomous research, and work that outlives a single conversation.

They cover different surfaces of the same work.

References

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