Claude Sonnet 4.6 is Rivya's balanced Claude model for serious day-to-day work: long documents, careful rewrites, research synthesis, and code review threads where steadier judgment matters but Opus-level spend is not necessary.
The final task cost is rounded up to whole credits.
Example output
Example; generating model unverified · Best for discovery interviews, concept tests, and usability research planning.
Chat
Example output
Example; generating model unverified · Best for discovery interviews, concept tests, and usability research planning.
We need to interview freelance designers about how they organize client feedback. Build the guide.
Research goal: understand how feedback becomes prioritized work. Participant profile: freelance designers with active client projects. Warm-up: ask about recent project flow. Core questions: where feedback arrives, how it is triaged, what gets lost. Bias check: avoid asking whether they want our proposed feature.
Output
Research goal / Participant profile / Warm-up / Core questions / Probes / Bias checks
Best for
Supports
Online trial
Input setup
Estimated credits: 4+
Chat controls
Choose when Rivya can search the web, how much reasoning it should use, and whether to include model thoughts when the selected model supports them.
Thoughts
Let the model default decide, or explicitly include or hide thought traces when the provider supports them.
Ask a question or describe a task to start.
Prompt starters
Model examples
Browse template previews for ideas. Each caption indicates whether generation with this model has been verified.
We need to interview freelance designers about how they organize client feedback. Build the guide.
Research goal: understand how feedback becomes prioritized work. Participant profile: freelance designers with active client projects. Warm-up: ask about recent project flow. Core questions: where feedback arrives, how it is triaged, what gets lost. Bias check: avoid asking whether they want our proposed feature.
Example; generating model unverified · Best for discovery interviews, concept tests, and usability research planning.
We want users to save favorite prompts. Draft a scoped PRD without turning it into a huge project.
Problem: users lose repeatable prompts after discovery. Goal: save and reopen favorite prompt templates. Non-goals: folders, team sharing, ranking, and custom prompt marketplace. Requirements: favorite button, saved list, empty state, analytics events. Open questions: limits, auth state, and mobile placement.
Example; generating model unverified · Best for feature scoping, product specs, and engineering handoff.
The audio provider returns 401 only in production.
First split: credentials, environment variables, and provider project scope. Request check: compare auth header shape in draft and production. Provider check: confirm the production key has audio generation enabled. Next step: log redacted request metadata and test a minimal production request.
Example; generating model unverified · Structured conversation example for API Integration Debug Chat.
We need docs for managed prompt media uploads and managed storage replacement.
Audience: maintainers replacing draft public files with approved media URLs. Outline: asset contract, upload path, metadata fields, validation commands, rollback notes. Missing context: exact managed storage bucket policy and cache invalidation behavior. Next step: add one worked example for image, video, audio, and chat assets.
Example; generating model unverified · Structured conversation example for Technical Docs Outline Chat.
Build a voice guide for Rivya prompt template pages.
Voice principles: practical, evidence-led, calm, and specific. Use: workflow, model choice, example, review, saved context. Avoid: supernatural promises, zero-work phrasing, category-changing hype, uncapped claims, and guarantee-style claims. Example rewrite: replace hype with a concrete before-and-after workflow claim.
Example; generating model unverified · Structured conversation example for Brand Voice Guide Chat.
Draft an investor update about prompt library expansion.
Summary: the prompt library moved from 40 templates toward a 200-template target. Evidence: audio and chat categories now have stronger example coverage. Risk: image and video still need final media review and managed storage migration. Ask: feedback on which workflows should be prioritized for distribution.
Example; generating model unverified · Structured conversation example for Investor Update Draft Chat.
Decision fit
Decision fit
Best-fit tasks
Use it when the task looks like this
Model details
Provider
Anthropic
Category
Chat
Capabilities
Chat
Credit model
Base credits / 1M tokens: input 170, output 855
Input path
Prompt only
Prompt setup
Up to 20,000 chars
Developer access
Call Claude Sonnet 4.6 from Public API v1 after checking the model fields, reference media rules, and credit behavior.
Claude Sonnet 4.6 FAQ
Claude Sonnet 4.6 is strongest when the job sits in the middle ground between cheap chat and true flagship spend: long briefs, sensitive rewrites, research synthesis, policy-style analysis, and code-review threads where steadiness matters. It is the workhorse Claude for serious day-to-day knowledge work, not the model you pick for either throwaway chatter or maximum-stakes escalation.
On Rivya, Claude Sonnet 4.6 currently runs as a text-first chat project. That keeps it focused on long pasted documents, structured analysis, careful rewrites, and longer reasoning threads rather than trying to be a multimodal catch-all.
What matters most is the framing of the first working brief: the source material, the decision criteria, the tone bar, and what a good answer actually needs to do. Claude Sonnet 4.6 usually rewards a well-structured brief more than a clever one-line prompt, which is exactly why it tends to feel better on review and rewrite work.
It is billed by usage in the current Rivya setup at 170 input credits and 855 output credits per million tokens. That spend is usually worth it when one careful pass can replace several weak ones, especially on long documents, sensitive edits, and decision-heavy synthesis work.
Choose Claude Sonnet 4.6 when you want calmer judgment, better long-document handling, and cleaner rewrite discipline than a lighter tier usually gives you, but do not yet need the cost profile of a top-end flagship. If the job is mostly low-cost quick turns, go lighter; if it turns into a truly high-stakes analysis thread, then step higher.
This page is best for a first serious pass on long documents, careful rewrites, research synthesis, or review-style reasoning. It is a good place to test whether Claude Sonnet 4.6 is already giving you the level of judgment you want before you commit the work to a longer saved thread.
This page is the public Claude Sonnet 4.6 chat entry. It keeps the setup light, but the model behavior is still aimed at reading, analysis, pasted source material, and rewrite work rather than a throwaway Q&A option.
Before the first turn, line up the source material, the decision lens, the tone you want back, and the standard the answer needs to meet. On Claude Sonnet 4.6, the first working brief does a lot of the steering work, so clarity usually matters more than prompt cleverness.
The page is publicly visible, but the actual first send is still gated behind sign in. Usage is billed at 170 input credits and 855 output credits per million tokens, so it makes sense to shape the brief first, then sign in once you are ready to spend on a real analysis pass.
Stay here while you are still testing the first brief, the first rewrite request, or the first review pass. Open the full Studio once the thread needs saved context, attached files, repeated follow-ups, or a longer piece of work you know will keep expanding.
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