Choosing a model

OpenBrowse puts Anthropic and OpenAI models behind one API, with a repair layer per provider for each family's failure modes. You pick the model per session, and if you send none the instance's DEFAULT_MODEL decides. Where that is unset the default follows whichever provider is actually configured, so an OpenAI-only instance runs gpt-5.6-terra and an Anthropic-only instance runs claude-sonnet-5. Where both keys are present OpenAI wins. The v3 API and the dashboard's run form both read the same setting, so a request gets the same model whichever door it came through.

The short answer

These are also the levels a session runs at when you send no reasoningEffort at all, so the top row is what an out-of-the-box instance does with a request that names neither a model nor a depth.

The finding worth knowing

On browser tasks, the two families want opposite ends of the reasoning dial.

OpenAI models do better with less reasoning. They spend less time planning ahead and more time reacting to the page actually in front of them, which is the correct instinct when the environment is a live DOM that changes under you. Anthropic's 5-series models lean towards rabbit holes and need reasoning time to refocus on the goal.

The measured effect is large. On the reference extraction task:

• gpt-5.6-terra at none finished in 11 steps and 1m 47s. The same model at high took 17 steps and 5m 05s, burning 2.1x the tokens for 2.75x the cost, and produced the same 14 records.
• claude-sonnet-5 cost $0.40 at high and $0.51 at none, and was 1m 16s slower with reasoning switched off.

So the default instinct, that more reasoning means better results, is wrong for half of the models here. That is why an omitted reasoningEffort resolves to the level measured above rather than to whatever the provider does unprompted: on gpt-5.6-terra those two differ, and inheriting the provider's would triple the bill for the same fourteen records.

Full numbers are on the benchmarks page.

Two things called thinking

Browser thinking is how the platform works: the see, plan, next and thinking cards in the live feed. It describes the agent's step loop and cannot be switched off.

Model reasoning is the chain-of-thought the LLM provider exposes, such as Anthropic extended thinking or OpenAI reasoning effort. This is the one you control, per session, with reasoningEffort.

The reasoning ladder, per model

reasoningEffort accepts default, none, low, medium, high, xhigh and max, validated per model at request time. An invalid value is rejected with a 422 naming the valid set for that model.

Sending default, or omitting the field, is not the same as letting the provider decide. OpenBrowse resolves the level itself and sends it explicitly, using the benchmark-backed pick for that model where there is one and the provider's own default where there is not. The two columns below are separated because they genuinely disagree on four of the eleven models, and the dashboard's dropdown labels them the same way: Default marks the level a session actually runs at, and Provider default appears beside it only where the provider would have chosen differently.

A few provider mechanics behind that table:

• The Claude 5 family and Opus 4.8 use adaptive thinking, and on the 5-series models the provider's own choice is adaptive thinking at high. This is why none must be an explicit choice there, and why claude-fable-5 and claude-mythos-5, which do not accept a disabled configuration, reject it with reasoning cannot be disabled on claude-fable-5.
• The older Claude models (opus-4.7, opus-4.6, sonnet-4.6) use fixed thinking budgets: low, medium and high map to 2,048, 8,192 and 16,384 thinking tokens.
• OpenAI models run against the Responses API, which accepts the full none to max ladder (the chat completions endpoint rejects max, which is why OpenBrowse does not use it).

Long context variants

Every Anthropic model name accepts a [1m] suffix, for example claude-opus-4.8[1m] or claude-sonnet-5[1m], which requests the provider's 1M-token context window for that session. The suffix is stripped before the model is resolved, so it works on the 5-series names as well as the 4.x ones. Useful for runs that read many large pages into a single conversation; the long-context tier is priced higher by the provider.

Dotted and hyphenated spellings of the 4.x names are interchangeable, so claude-opus-4.8 and claude-opus-4-8 resolve to the same model.

Migrating from thinkingLevel

Requests written for Browser Use Cloud's thinkingLevel field keep working: disabled, low, medium and high map onto reasoningEffort (disabled becomes none). Sending both fields in one request is rejected, as are the retired spellings thinkingEffort and modelThinkingEffort, each with an error naming reasoningEffort as the replacement.

Supported models

OpenAI: gpt-5.6-sol, gpt-5.6-terra, gpt-5.6-luna

Anthropic: claude-mythos-5, claude-fable-5, claude-opus-5, claude-sonnet-5, claude-opus-4.8, claude-opus-4.7, claude-opus-4.6, claude-sonnet-4.6.

Every one of those also accepts the [1m] suffix, so claude-sonnet-5[1m] and claude-opus-4.6[1m] are both valid. The suffix is stripped before the name is resolved and the long-context beta is requested for whichever Anthropic model it resolved to, so there is no per-model exception list to check.

Google: not yet.

Browser Use Cloud's v3 model enum has 22 names and OpenBrowse implements 8 of them, so a client that names a model explicitly may be naming one that fails here with a 422 rather than falling back. The cloud's default, claude-opus-4.7, is supported, so this only bites callers who set model themselves. See migrating from Browser Use Cloud for the mapping in both directions.

DEFAULT_MODEL in the instance's .env decides which model a request that names none gets, and it is editable from the dashboard's Settings page. Leave it unset and the instance falls back to the default for whichever provider key it holds, reporting gpt-5.6-terra when neither key is configured. The dashboard's picker narrows the same way, offering only the families whose key is present, though an instance with no keys at all still gets the full list so that a fresh install renders a usable form. Installation lists it alongside every other variable.

Anthropic models need ANTHROPIC_API_KEY configured and OpenAI models need OPENAI_API_KEY; a session naming a model whose key is missing fails at launch with a message saying which variable to set.

Setting it

const session = await client.sessions.create({
  task: "Capture every open vacancy and return the full schema for each.",
  model: "gpt-5.6-terra",
  reasoningEffort: "none",
  maxCostUsd: 3.0,
  outputSchema: mySchema,
});

maxCostUsd is a hard cap checked after every step; cost control covers it, along with CLOUD_MAX_COST_FACTOR for callers whose budgets were priced for a hosted service.

A note on budget models

gpt-5.6-luna at max was the cheapest full extraction in the benchmark at $0.22, but it took 17 minutes and 36 steps. It is also the most prone to hallucination when the prompt is broad, though the answer-store guards refuse ungrounded values at the boundary either way. Pair it with a tightly scoped prompt and it is genuinely good value; point it at something vague and it will wander.