OpenAI or Anthropic for your Home Assistant?

If you are wiring OpenAI or Anthropic into Home Assistant in 2026, the practical answer is less dramatic than the frontier-model marketing makes it sound: use local Assist where plain control works, use a budget cloud model for ordinary natural-language requests, and save a flagship model for automation generation or complex reasoning you are willing to verify afterward.

That is not a preference for cheap models as a philosophy. It is what the Home Assistant-specific benchmark data points toward. On the Home LLM Leaderboard, Claude 3.7 Sonnet scores 89.4% on the assist task and 100.0% on assist-mini, while gpt-4o-mini scores 85.9% and 98.5% on the same tasks; the gap is real, but it is not the kind of gap that should make someone rebuild a working house-control pipeline around provider loyalty alone.[1]

The benchmark that matters is the Home Assistant one

General chatbot comparisons are a poor fit for this decision. A model that writes a nice essay or explains a wiring standard can still be annoying when asked to turn on the kitchen lights, and a model that handles a simple light command may still produce a broken automation. The useful split is by workload.

The Home LLM Leaderboard is useful because it tests Home Assistant-style tasks directly and reports separate task families. Its runs are dated through 2026.7.4, and the published samples are not enormous, so the scores should be read as measured signals rather than permanent laws of model quality.[1]

The assist and assist-mini results are the part to keep in view when someone says a Home Assistant conversation agent needs a flagship model. In the measured Home Assistant tasks, Claude 3.7 Sonnet does beat gpt-4o-mini. It just does not beat it by enough, on these ordinary-control tasks, to make the provider race the main decision.

That matters in a real household. The person standing in a dark kitchen does not care whether the model is better at long-context legal analysis. They care whether “turn on the counter lights” maps to the right exposed entity, whether the assistant asks for clarification instead of guessing, and whether the command returns quickly enough that nobody reaches for the wall switch.

Device control and automation writing are different jobs

The automation-generation result is the exception that keeps this from becoming a “just use the cheapest thing” article. GPT-5.6 Luna’s 88.3% automation score sits above Claude 3.7 Sonnet’s 81.7% in the leaderboard’s automation task, while the same research context flags weaker device-control behavior for Luna.[1] That combination is exactly why routing beats ranking.

A Home Assistant setup has at least four different assistant jobs hiding under one interface:

• Plain control: “turn on the kitchen lights,” “set the thermostat to 70,” “check the garage status.” Use local Assist first when the command pattern is simple and the entities are named cleanly.
• Typical natural-language control: messy phrasing, room nicknames, follow-up questions, and small bits of context. This is where a budget cloud model such as gpt-4o-mini is often enough, based on the assist scores.
• Automation generation: creating or editing YAML, choosing triggers and conditions, or turning a household rule into a working automation. This is where a stronger model can be worth paying for, but the output still needs review.
• Troubleshooting and explanation: reading an error, explaining why an automation did not fire, or suggesting which entities to expose. Treat this as advisory, not as unattended control.

The Home Assistant OpenAI Conversation documentation is blunt about a detail that gets skipped in model debates: only exposed entities are available to the assistant.[3] That is not just a privacy setting. It is a performance and cost setting. A model cannot reliably choose the right light if the exposed list is a junk drawer of old helpers, test sensors, duplicate devices, and half-renamed switches.

Before changing providers, it is usually worth cleaning the surface area the model sees. Expose the entities a household member would reasonably ask for. Hide diagnostic sensors, stale helpers, and automations that should never be voice-triggered. Rename entities so that room and device names match spoken language. This is dull work, but it fixes more daily assistant failures than upgrading from one impressive model name to another.

The same discipline applies when using a model to write automations. If you already maintain copyable, verified Home Assistant automations, keep the model close to that level of specificity: hub, protocol, entity name, date checked, and expected behavior. A recipe-style workflow like verified smart-home automations is a better input pattern than “make my house more efficient.”

Pricing only helps if it stays dated

Model names and prices move too quickly to be blended into one imaginary 2026 lineup. The clean way to compare cost is to label the price sheet. SmartHomeScene’s May 2026 Home Assistant AI guide listed these token prices, with input and output charged separately per 1 million tokens.[2]

For occasional households, the cost question is usually not “free versus ruinous.” SmartHomeScene estimated a typical Home Assistant AI interaction at roughly 500 to 2,000 tokens including context, and described occasional use as landing from a few cents to a couple of dollars per month.[2] The more practical lever is how much context you send every time.

That brings the decision back to exposed entities. Hundreds of exposed entities can inflate every request, even when the user only wants one lamp. A smaller, cleaner exposed set lowers token use, reduces the model’s opportunity to choose the wrong target, and makes the assistant easier to audit when it behaves oddly.[3]

Adoption explains the default path, not the better model

OpenAI is more common in Home Assistant installs than Anthropic, but that should be read as familiarity and path dependence, not proof of better behavior. Home Assistant analytics put the OpenAI integration at 4.5% of active installs and the Anthropic integration at 1%.[4] The OpenAI Conversation integration was introduced in Home Assistant 2023.2, while the Anthropic integration was introduced in 2024.9.[3][5]

That gap matters in a practical way. More OpenAI users means more examples, more forum posts, and more people who have already tripped over the same configuration issue. It does not mean OpenAI should automatically handle every workload. Home Assistant’s Anthropic integration page says integrations ship a recommended model that balances price, accuracy, and speed, which is the right kind of framing for this problem.[5]

Gemini and local Ollama belong in the same mental map without becoming co-headliners here. Gemini is relevant when its free or rate-limited access fits a household’s tolerance for constraints. Local Ollama is relevant when privacy, local availability, or tinkering value outweighs cloud-model convenience. Neither changes the OpenAI-versus-Anthropic conclusion for the measured assist tasks: the ordinary-control gap is small enough that configuration quality and routing matter more than the logo.

Contain the assistant before you trust it

Home Assistant’s own AI-agent guidance is appropriately cautious: AI output cannot be completely trusted, and hallucination risk has to be designed around rather than wished away.[6] That is especially important in a smart home because the output is not just text. It can become a light, lock, alarm mode, fan, garage door, or automation change.

The safest configuration is not the one that assumes the model will behave. It is the one that limits what the model can touch, routes harder tasks to a place where review is natural, and keeps the boring deterministic path available when the model is unnecessary.

• Keep plain voice control boring: use local Assist for commands it can parse reliably.
• Expose only the entities a household member should be able to control or query.
• Use a budget cloud model for ordinary natural-language flexibility before paying flagship rates for every request.
• Send automation generation to the stronger model only when the task justifies it, then review the automation before enabling it.
• Keep provider, model, price sheet, and benchmark date together in your notes. Do not mix a May 2026 price with a July 2026 model lineup and call it one comparison.

This same habit helps with hardware decisions too. If a device or sensor depends on a hub, bridge, or protocol path before an AI assistant ever sees it, check that layer first. A hub-focused compatibility matrix such as the smart flood sensor hub compatibility guide is closer to the real failure point than another abstract model ranking.

A defensible 2026 Home Assistant setup

For everyday Home Assistant control, start with local Assist. If the household’s commands are predictable and entity names are clean, there is no reason to send every light command to a frontier model.

For normal conversation-agent use, a small cloud model is the sensible next step. The Home LLM Leaderboard’s assist results put gpt-4o-mini close enough to Claude 3.7 Sonnet that the budget model deserves to be treated as a serious default, not a compromise made only for people who do not care about quality.[1]

Anthropic remains a reasonable choice when its measured behavior, response style, or a specific household preference justifies the higher dated price. The benchmark does not say Anthropic is bad. It says the ordinary-control lead is too small to carry the whole OpenAI-or-Anthropic decision by itself.

For automation generation, use the best measured model you are willing to pay for, and treat the output as a draft. This is the workload where the leaderboard shows more separation, and it is also the workload where a confident mistake can leave you with an automation that looks plausible while doing the wrong thing at 2 a.m.

So the useful OpenAI-versus-Anthropic smart home assistant comparison is not a universal model ranking. It is a routing plan: local first, small cloud next, flagship only when the task earns it, with costs dated, entities contained, and failure modes visible.

References

1. home-assistant-datasets reports, allenporter/home-assistant-datasets.
2. Home Assistant AI: Everything You Need To Know, SmartHomeScene, May 2026.
3. OpenAI Conversation, Home Assistant.
4. Home Assistant Analytics, Home Assistant.
5. Anthropic, Home Assistant.
6. AI agents for the smart home, Home Assistant, June 7, 2024.