How to use Step 3.5 Flash 2603 with a signed-in browser

Step 3.5 Flash 2603 can support a browser agent only when the selected API or local runtime exposes compatible tool calling and the client can reach a browser. A useful first workflow is a read-only index of visible support-ticket IDs and their source links. The agent should open each ticket to confirm the link, then stop. Tabbit Browser keeps the signed-in session local; Tabbit CLI lets a supported local agent use its tabs.

Skill discovery in the local agent is separate from provider routing. Pi uses /skill:tabbit; clients with a /skills picker should follow that client’s documented flow. The CLI command below starts a named task and reads its current tab. It does not change a ticket.

~/.local/bin/tabbit-cli nodejs --task "Step 3.5 Flash 2603 ticket index" --request-id build-ticket-index <<'JS'
return await tabbit.observe({frames: "none", maxChars: 4000});
JS

The official StepFun repository documents the public base checkpoint and its Apache 2.0 repository license. Its agent setup names the base step-3.5-flash route. Current OpenClaw StepFun provider docs list step-3.5-flash-2603 under Step Plan at https://api.stepfun.ai/step_plan/v1, while the official model README examples name the base model. Treat the provider guide as a routing clue and confirm access and terms in your own account; the public repository does not document a separate downloadable 2603 checkpoint.

Community reports about the base Step 3.5 Flash vary by agent and inference backend. One OpenCode user described tool calls becoming unreliable after a few turns; another thread reported success after using an updated parser and quant. These posts are about the base model, not proof of 2603 behavior (OpenCode discussion, parser discussion).

Key takeaways

• Pin the exact step-3.5-flash-2603 route. Do not assume it is the rolling step-3.5-flash service name.

• The public Step 3.5 Flash weights repository does not by itself prove that the 2603 API variant has the same checkpoint or license.

• Verify current provider availability, tool calling, and the client integration before connecting a browser.

• Start with read-only ticket indexing. Never send a reply or change a ticket status in this workflow.

• Check every output link against the signed-in page before relying on the index.

OpenClaw’s StepFun provider plugin lists the exact model under Step Plan as stepfun-plan/step-3.5-flash-2603. Its standard provider list uses different model IDs. Run openclaw models list --provider stepfun-plan and verify that your regional Step Plan account can call the listed model before loading Tabbit. OpenClaw’s StepFun provider guide

Step 3.5 Flash 2603 browser use at a glance

Identify the 2603 route before wiring tools

The topic targets the dated model ID stepfun/step-3.5-flash-2603. Verify that exact ID in the provider's current catalog before following a setup example. The official StepFun open repository is named Step-3.5-Flash and documents its own public checkpoint, local serving options, and license. That repository is not enough to claim the API SKU ending in 2603 is the same downloadable checkpoint. Keep the provider route, exact model ID, checkpoint, and license as separate facts.

This distinction matters for tool calls too. A service may accept an OpenAI-compatible request but still differ in tool schema, region availability, rate limits, or enabled model IDs. Use the current first-party provider documentation and account console to confirm the exact route. If the catalog offers only step-3.5-flash or step-3.5-flash-2603 under a named product, record that precise product and endpoint. Do not silently call a sibling model a match.

Use a read-only ticket index as the first browser task

Choose a ticket queue the user is authorized to view. Define the scope in advance, such as the currently visible page of open tickets. The agent may read ticket numbers, visible subjects if needed for disambiguation, and canonical links. Do not include message bodies, email addresses, customer names, or hidden fields in the index unless the owner has specifically approved them. For this workflow, a ticket number and source URL are sufficient.

Use Tabbit CLI through its installed launcher. Run diagnose, confirm the selected Tabbit instance, and start a named task. A read-only request can have an explicit request ID for traceability. Keep your everyday tabs separate; the task's owned browser tab can reuse an existing login in that same Tabbit environment without sharing login data with the model. See Tabbit CLI quick start, the full CLI guide, and connecting an agent to an existing browser.

Prompt for links you can verify

Use this as an illustrative prompt in an authorized test or support workspace. It reads visible content only and does not change tickets.

Task: build a review index from the currently visible support queue.
Use the signed-in Tabbit Browser tab through Tabbit CLI.
Read only the visible ticket identifier and its canonical source link.
Open each link and confirm that the page shows the same identifier.
Do not read or copy message text, customer names, or email addresses.
Do not reply, assign, tag, close, or change ticket status.
Return a table with ticket ID, source link, identifier verified (yes/no), and note.
Include the number of visible rows examined. Mark inaccessible links as unverified.
Stop after producing the table and wait for a person.

Expected output is a bounded checklist with one source link per row. A plausible-looking ID without a matching source page is not verified. A link returning a login page, permission error, or unrelated ticket belongs in the unverified count. For setup choices, see the browser agent first-task checklist and MCP versus CLI.

Review named draft: Step 3.5 Flash 2603 ticket index

Keep the result as a draft named Step 3.5 Flash 2603 ticket index. A reviewer should compare the count with the visible queue, open each source URL, verify the ticket number, and check that no restricted fields were copied. Remove duplicates only when the same canonical ticket link proves they are the same record. Note the time and filter used, because queues change. The reviewer then decides whether a different approved process should handle replies or assignments.

The model's output is an index, not a ticket-system audit. Pagination, lazy loading, access filters, or stale pages can hide records. If the task asks for every ticket, first establish a supported export or a complete pagination method and validate the total count. Do not call a partial visible page a complete queue.

Decide whether the route is ready

Tabbit provides a browser workspace and local CLI handoff. It does not mean a remote model runner can see your local session automatically. Remote agents need a separately supported local bridge. Review the Tabbit Browser overview, Codex with Tabbit CLI, and the browser automation safety checklist.

When results look wrong

If the agent returns links without opening them, ask for one at a time to be verified and keep the rest unverified. If ticket numbers do not match their pages, discard those rows and check whether the source list changed while the task ran. If the agent tries to reply or alter a ticket, stop the run and inspect the client permissions. Do not treat a good text response as evidence that the model used a browser tool.

A local checkpoint, a hosted API model ID, and an agent client are separate components. Check each one. The open Step 3.5 Flash repository documents local inference, but the 2603 route requires its own current provider verification. The complete Tabbit CLI guide explains task ownership and browser access boundaries.

Decision

Use Step 3.5 Flash 2603 for a browser workflow only after the exact provider route and tool-call path are confirmed. Start with a read-only ticket index, verify each ID at its source link, and leave every ticket unchanged. If the provider's 2603 identifier is unavailable or ambiguous, pause instead of substituting a rolling sibling model.

For related setup context, see AI browser automation.

Secholar.L

Growth & Content at Tabbit

Secholar.L writes hands-on guides about AI browsers and agent workflows, comparing tools with real research and citing verifiable sources.