Faraday Machines vs OpenAI: Frontier Models or Private AI

Two Different Products

OpenAI rents access to its own models, through ChatGPT seats or its API, running in its cloud. Faraday Machines, a Toronto company, installs open-weight models on Apple Mac Studio M5 Max hardware (128 GB per unit) in your office. You pay once and the data stays in the building. OpenAI has the stronger models and, for light use, the lower bill. Faraday is stronger on where data goes and what next year’s bill will be.

OpenAI vs Faraday On-Premises, Head to Head

OpenAI details are as of October 19, 2026.

OpenAI Still Leads on the Hardest Benchmarks

Start with what OpenAI does better. On the Artificial Analysis Intelligence Index v4.3.x, which Artificial Analysis rescaled on September 7, 2026, GPT-6 Astra scores 53 and GPT-6.1 Sol 52. The best open-weight model, MiMo-V2.6-Pro, scores 46. That is seven points behind Astra and 12 behind the leader, Anthropic’s Claude Opus 5.5 at max effort.

Scores and cost per task: Artificial Analysis v4.3.x. API prices: vendor list prices. Low-bit builds are Unsloth’s and trade some quality, most noticeably on long or precise work.

The gap to the very top widened on v4.3, but the cost gap grew too: MiMo-V2.6-Pro runs at about $0.13 per index task, roughly a twenty-fifth of Astra’s $3.26. GLM-5.3-Flash, which fits Faraday Growth at 4-bit, scores 42 at $0.25 per task, level with Claude Opus 5.5 at low effort ($0.55) and OpenAI’s older GPT-5.6 Terra ($1.40).

Faraday’s standard tiers sit a step below the open frontier. Starter runs Qwen3.8-27B and NVIDIA’s Nemotron 3.5 Super VL (26 on v4.3) at 4-bit. Growth adds GLM-5.3-Flash and MiMo-V2.6-Flash (38). Scale adds MiniMax M3, plus DeepSeek V4.1 Flash and GLM-5.3 as low-bit builds.

Measured in Time, the Lead Is About Four Months

Time is another measure: how long open models take to match what the best closed model could already do.

The counterpoints are real. Epoch warns that public benchmarks may flatter open models, and the LessWrong analysis says the gap has widened since January 2025. Taken together, open models sit within about a season of the frontier on public tests, and further back on the hardest ones.

For most business work that lag matters less than it sounds. Drafting, document work, analysis and routine coding don’t need the top 5% of capability. The hardest multi-step reasoning and long agentic runs do, and that is where GPT-6 Astra is worth paying for.

Where Your Data Goes on OpenAI

OpenAI’s business terms are better than many buyers assume. The gaps are in retention, residency and jurisdiction.

1. Training — OpenAI doesn’t train on ChatGPT Business, Enterprise or API data by default. Individual ChatGPT accounts differ: OpenAI may train on those unless the user opts out, which matters when staff use personal accounts for work (more on that risk).
2. Retention — API inputs and outputs are kept for up to 30 days for abuse monitoring. Zero Data Retention removes that for eligible customers on approved endpoints, but isn’t self-serve, and per a third-party summary the Agents API doesn’t support it.
3. Court orders — In 2025, during the New York Times lawsuit, a US court ordered OpenAI to preserve ChatGPT logs. Per a 2026 guide, the order ended in September 2025. The lesson remains: a court in another country can order your prompts kept.
4. Residency — Canada is one of ten regions where eligible ChatGPT Enterprise, Edu and Healthcare and API customers can store content at rest; ChatGPT Business isn’t named. Per a developer-docs summary, that is storage, not processing, and only for new workspaces and projects. Regional endpoints reportedly carry a 10% surcharge for models released since March 5, 2026.
5. Jurisdiction — OpenAI is a US company, subject to the US CLOUD Act wherever the data sits. Residency isn’t sovereignty, as our Canadian AI sovereignty article explains.

On a Faraday install, prompts, documents and logs stay on hardware in your office, and you set the retention. No US provider holds a copy for a US court to reach. For firms bound by client confidentiality, PIPEDA, PHIPA or Quebec’s Law 25, that is often the deciding point (see our compliance page).

What It Costs a 10-Person Team

For light chat use, ChatGPT Business is cheaper. Standard seats cost $20 per user per month billed annually, or $25 monthly, with a two-seat minimum: $2,400–3,000 a year for ten people. Faraday Growth costs $19,999 US one-time, including hardware and 12 months of support, or roughly seven to eight years of standard seats.

OpenAI seat prices as reported by Elephas; premium seats went on sale August 25, 2026. ChatGPT Enterprise has no published price. Faraday doesn’t publish support renewal prices, so its three-year figure covers the hardware and first year of support only. API usage is billed separately on OpenAI and isn’t included.

Heavy use flips it. Ten premium seats cost $12,000–15,000 a year, so Growth equals 16 to 20 months of them. API users pay per token: GPT-6 Astra lists at $10/$50 per million input/output tokens, and Artificial Analysis puts it at $3.26 per index task, or $3,260 per thousand. On hardware you own, the bill doesn’t grow with use.

Prices also move both ways. OpenAI cut GPT-6.1 Sol to $2/$10 and GPT-6 Luna to $0.10/$0.50, cuts it calls permanent. On September 22, 2026, OpenAI and Anthropic both released cheaper frontier models amid a price war, Fortune reported. Yet each OpenAI flagship has cost more than the last, from GPT-5.5 at $5/$30 to GPT-6 Astra at $10/$50. Either way, the vendor sets the price.

When OpenAI Is the Better Choice

OpenAI is the better buy when:

• Your team uses AI lightly, mostly for chat, and the data isn’t confidential.
• You need the strongest model for hard reasoning, engineering or long agentic work.
• You build on OpenAI-specific tools such as the Agents API and can accept US-only residency for that work.
• You want to start this week without buying hardware.

The Sensible Setup: Use Both

For many 10–200 person businesses the choice isn’t either-or: keep a frontier cloud model for the hardest tasks, and run sensitive and high-volume work on hardware you own.

1. Client files and regulated data — On-premises. Contracts, financials and personal information covered by PIPEDA, PHIPA or Law 25 stay in the office.
2. High-volume drafting and document work — On-premises, on models such as GLM-5.3-Flash, with no per-token meter.
3. Routine coding — On-premises, on Qwen3.8-27B for fast, lightweight work, or on DeepSeek V4.1 Flash for agentic coding, which runs on Scale as a 1- to 3-bit build.
4. The hardest reasoning and agent tasks — GPT-6 Astra or GPT-6.1 Sol (52, at $2/$10) through the API, with non-confidential inputs.
5. Occasional general questions — A few standard ChatGPT Business seats for staff whose questions don’t involve client data.

The split also hedges against access changes. In June 2026, a US Commerce Department order made Anthropic disable its Fable 5 model for everyone; access returned only after the order was lifted on June 30. Open weights on your own hardware keep running whatever happens to a vendor’s endpoint.

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