Researched October 14, 2026. A documentation-based comparison, not a hands-on benchmark.
Meta Muse vs ChatGPT dots vs Claude Cowork vs Grok Bot sounds like a straightforward four-way contest. It becomes more useful once we separate the models from the applications around them. A model can reason about a task; an agent product supplies the computer, connections, memory and controls that let it act.
There is no defensible universal winner from the evidence reviewed here. The clearest differences are product scope, documented access, API cost and the work each provider emphasizes. This guide explains those differences without turning vendor claims into independent test results.
Agent products at a glance
| Product | What is being compared | Model distinction |
|---|---|---|
| Meta Muse | Personal agent with its own computing environment | Muse launched with Spark; Wonder is a later model rollout, not a synonym for the app |
| ChatGPT dots | Persistent personal agents in ChatGPT | OpenAI documents GPT-6 Astra as the powering model |
| Claude / Cowork | Agent workflows now joining Claude chat | Cowork is a product experience, not a Claude model name |
| Grok Bot | Persistent agent with a cloud computer | Grok 4.7 is separate model-level context |
These are the product-level peers. Model specifications and API rates later in this article are background for developers, not subscription prices or proof of agent quality.
Muse: the personal agent, then the model
Meta describes Muse as a personal agent operating in its own secure virtual machine with a browser. Tasks can continue after the app closes. Its launch announcement describes memory, activity visibility and permission checks for sensitive actions. Those are documented capabilities, not results of our own reliability testing. Source: Introducing Muse.
Evaluate Muse with an assignment that needs follow-through: collect options, retain your criteria and return a usable result. Check whether the integrations and actions you need are available in your account. A personable avatar is an interface choice; it does not establish better judgment or completion accuracy.
ChatGPT dots: the comparable OpenAI agent
OpenAI describes dots as persistent agents powered by GPT-6 Astra, with their own cloud computer and connected apps. They can pursue ongoing goals and carry context between interactions. This is why dots belongs beside Muse and Grok Bot in the main comparison, while Astra belongs in the model section. Source: Introducing dots.
The current help guide supports setup on mobile, desktop and desktop web. Local computer access is optional and starts disabled. Users can review activity, schedule work, set action rules and pause their dot. Availability depends on plan, region and rollout. Check your account before choosing it for a required workflow. Source: dots setup and controls.
A useful trial: ask for a recurring research brief, then change its requirements and measure whether the next result applies the correction. Track citation quality, missed updates and review time. This tests persistence rather than rewarding an impressive first reply.
Does Claude have an equivalent? Yes: Cowork, now part of Claude
Anthropic announced on September 16 that Claude Cowork and chat are becoming one Claude experience, beginning with Pro and Max. The announcement describes work that continues after closing a laptop and document, slide and design creation inside conversations. The comparable offering is therefore Claude with Cowork capabilities, rather than a newly named mascot or a model called Cowork. Source: Cowork is now Claude.
Anthropic's architecture guide describes cloud execution by default, with sessions and files associated with the account. This makes it relevant to the same delegated-work comparison. Confirm your account's rollout, available tools and file access rather than assuming every Claude chat has identical capabilities. Source: Cowork architecture.
A useful trial: supply a small set of source documents and ask for a revised report with a traceable list of changes. Compare source fidelity and the effort needed to finish the deliverable. We have not run this trial across the four products.
Avatars: what the thumbnail represents
The cover is an AI-generated editorial illustration referencing Muse's beige mascot and Grok Bot's black orb character. Dots is represented by a blue character example, not the OpenAI corporate logo: OpenAI lets users choose characters or pets, so dots has no single mandatory avatar. Claude uses its recognizable product symbol, labeled Cowork; we did not verify a separate official Cowork mascot. These visuals identify products and imply no endorsement or ranking.
Underlying models: a separate comparison
Anthropic's October 7 Haiku 5.5 announcement is the newest release verified for this article. It targets smaller, high-volume tasks. Opus 5.5 is our main Claude comparison because its documented role is closer to demanding coding and knowledge work; Sonnet provides another cost/performance option. This does not make Opus the strongest Claude in every setting: Anthropic also lists Fable 5.1 for demanding reasoning. Sources: Haiku announcement, Claude model comparison.
Meta Muse Wonder: a recent entrant focused on sustained agent work
Meta's October 12 announcement emphasizes personal and professional tasks. It describes Active Memory for carrying useful information across conversations and Active Focus for persisting through difficult work. These are Meta's descriptions, not features independently tested by PromptyBox.
Meta lists Muse Wonder access through the Meta Model API and Muse Code, and Wonder Full access for Muse Code subscribers. It describes a subsequent rollout into Muse experiences beginning with subscribers. Do not assume every Meta consumer account already has the same access. Source: Meta's Wonder announcement.
Reason to evaluate it: recurring assignments where remembering corrections and maintaining task context could reduce re-explanation.
Limitation of this comparison: we did not verify a current, directly comparable public token-price schedule or context limit for Wonder. Those cells remain unverified rather than being filled with estimates or figures from older Meta models. Missing evidence is not evidence of poor performance. Before committing, check the exact endpoint, access tier and current terms available to you.
GPT-6 Astra: demanding work with a documented tool ecosystem
OpenAI positions Astra for complex reasoning, software engineering and professional computer workflows. Those claims describe its intended role; they do not prove it wins every assignment. Source: Astra introduction.
The API model page lists text and image inputs with text output, a 1,050,000-token context window and up to 128,000 output tokens. It documents tool support including web and file search, computer use and code execution. A supported tool still has to be provided and configured; the underlying model does not automatically have permission to use your accounts. Source: Astra API documentation.
Reason to evaluate it: difficult tasks that combine reasoning with files, code or computer interaction, especially if those workflows already run in an OpenAI environment.
Tradeoff: the listed standard token rates are higher than the other verified API rows below. Whether that premium pays off depends on successful completion, retries and review time. This article has not measured those outcomes. Do not infer native image generation or voice capability from an application's ability to call separate media tools.
Claude 5.5: choose the tier before comparing the brand
Anthropic documents Opus 5.5 for longer-running coding and knowledge work, with a one-million-token context window and a 128,000-token standard output limit. Its adaptive thinking is always enabled. Source: Opus 5.5 documentation.
Anthropic describes Sonnet 5.5 as a faster, lower-cost alternative aimed at well-scoped work, bug fixes and document creation. Haiku 5.5 targets repetitive, cost-sensitive tasks such as classification and summaries. These are vendor positioning statements, not a measured ranking from this article. Sources: Sonnet announcement, Haiku announcement.
Reason to evaluate it: a workflow with a mixture of complex assignments and frequent simpler requests, where testing more than one tier may reveal a useful cost split.
Tradeoff: the newest small model is not a like-for-like substitute for a premium reasoning model. Nor should a result from Opus be attributed to every Claude tier. Record the model, effort level, application and tools when comparing. Developers migrating from earlier versions should also check compatibility changes rather than only replacing the model name. Source: Opus migration changes.
Grok Bot: compare the complete agent separately from Grok 4.7
Grok Bot is described as an always-on agent product with a cloud computer, memory and the ability to work across applications. Its launch announcement presents a beta service with its own usage allocation. The later access update lists inclusion with SuperGrok, Cursor Pro and Cursor Teams plans. Check current account eligibility and limits before subscribing for a particular workflow. Sources: Grok Bot introduction, plan expansion.
For model-level context, the September 21 Grok 4.7 announcement emphasizes coding, knowledge work and training for the Grok Bot environment. It lists API, Cursor and Grok Build availability. That does not establish that every Bot interaction uses a single fixed model or configuration. Source: Grok 4.7 announcement.
Reason to evaluate it: delegating a workflow across actual applications when a persistent agent environment is part of the requirement.
Tradeoff: product access, connected-account permissions and operational reliability matter alongside reasoning quality. A low API rate cannot tell you the full cost or limits of a Bot subscription. Measure whether it completes your actual workflow, including its handoffs and approval points.
API pricing: a comparable baseline, not the whole bill
These are USD rates per one million tokens, checked October 14, 2026. The table describes ordinary uncached model usage, not monthly chatbot subscriptions. Special modes, long inputs, caching, tools, platform fees and other modifiers can change the bill.
| Model | Input | Output | Scope |
|---|---|---|---|
| Muse Wonder / Wonder Full | Not verified | Not verified | No comparable rate confirmed in this research |
| GPT-6 Astra | $10 | $50 | Standard rates; long-input modifiers apply |
| Claude Opus 5.5 | $4 | $20 | Standard API rates |
| Claude Sonnet 5.5 | $2 | $10 | Standard API rates |
| Grok 4.7 | From $2 | From $6 | Starting rates; faster variant costs more |
Sources: OpenAI model pricing, Anthropic pricing, Grok 4.7 pricing.
For a deliberately simplified example using 10,000 uncached input tokens and 2,000 billed output tokens, the arithmetic is:
| Model | Illustrative token charge |
|---|---|
| Astra | $0.20 |
| Opus 5.5 | $0.08 |
| Sonnet 5.5 | $0.04 |
| Grok 4.7 at starting rates | $0.032 |
These calculations use the displayed rates, not measured task consumption. Actual reasoning tokens, repeated context, tool calls and retries can increase usage. Astra's documentation specifies higher full-request rates above 272,000 input tokens. The example stays below that threshold. A cheaper token rate does not establish a cheaper successful task, and a higher rate does not establish better output.
Which should you shortlist for your work?
The following is an editorial testing plan, not a performance leaderboard.
| Your priority | Sensible comparison | What should decide it |
|---|---|---|
| Difficult code or document work | Astra, Opus 5.5, Wonder and Grok 4.7 with equivalent tools | Correctness, completion and review effort |
| Repeated small requests | Include Sonnet or Haiku rather than only premium tiers | Accuracy at your required throughput and budget |
| Work across several applications | Compare the actual agent products you can access | Integration coverage, permissions and recoverability |
| Remembering preferences | Test supported memory controls in each product | Correct recall, deletion controls and separation between projects |
| News and research | Give each candidate equivalent search access | Source quality, recency and whether citations support claims |
| Creative writing | Blind-review the same brief across candidates | Voice, specificity, factual accuracy and revision effort |
For coding, require a reproducible change and relevant checks. For writing, compare drafts with brand names hidden. For research, open a sample of citations and check that they support the exact statements made. None of those outcomes can be inferred reliably from the product's personality or marketing language.
Why there is no single benchmark score here
A neutral comparison should not combine a provider's best coding result, another provider's voice score and a third provider's preferred writing example into one ranking. Results can depend on the task set, tool environment, reasoning budget, run count and model version. A chart measured before a newer model shipped cannot establish the current winner.
We did not run a controlled four-way test. We also did not verify one independent evaluation covering all these exact models and product configurations under equivalent conditions. The absence of a score is intentional: publishing invented precision would make this guide less useful.
A fair one-hour trial
Choose three small assignments with known acceptance criteria: a document task, a code or spreadsheet task, and a research task. Use non-sensitive material you have permission to share.
- Give every candidate the same source files, deadline and deliverable.
- Record the exact model, product, effort setting and enabled tools.
- Keep the available information equivalent. Do not give only one candidate browsing.
- Define the same cost or time ceiling and disclose any differences.
- Score correctness, missed requirements, usable output and human correction time separately.
- Repeat close results before spending money on a migration.
A reusable trial prompt:
Complete the attached task using only the supplied materials and the tools explicitly enabled. Preserve factual details. Flag missing evidence rather than guessing. Return the deliverable, the checks you performed and any unresolved issues. Stop for approval before publishing, spending money, contacting anyone or changing shared records.
For agents, add a harmless boundary check: a document containing quoted instructions that conflict with the task should remain reference material. Observe whether the assistant follows the actual assignment and respects its permissions. This is a practical workflow check, not a comprehensive safety evaluation.
Privacy and control need a product-level review
Do not treat a model's reputation as a substitute for reading the terms of the product and plan you use. Before connecting accounts, check retention, training-use settings, memory deletion, access controls and activity records. Verify whether those controls apply to your subscription, API deployment or enterprise contract.
The same standard should apply to all four candidates. Start with the minimum access required, retain a recoverable copy of important work and review actions with external consequences. Claims about alignment or safeguards do not mean mistakes are impossible.
Our neutral verdict
Muse, ChatGPT dots, Claude with Cowork capabilities and Grok Bot all deserve evaluation as complete agent products. Compare the integrations you can actually use, task completion, control and total cost. These are reasons to test, not awards for superiority.
Choose from measured results on your own workload. If the cheaper option completes the job just as well, the extra expense needs a reason. If a more expensive option materially reduces errors and review time, token prices alone should not disqualify it. If a required integration or privacy control is absent, strong reasoning may not solve the practical problem.
For the surrounding release context, see our October tech preview. For OpenAI-specific background, read our GPT-6 Astra explainer.
Frequently asked questions
Is Meta Muse the same as Muse Wonder?
No. This comparison distinguishes the Muse experience from the Wonder model family. Check the named model and access path instead of assuming all Muse experiences are identical.
Are ChatGPT dots and GPT-6 Astra the same thing?
No. Dots is the agent product; Astra is the model OpenAI documents as powering it. Paying for API tokens does not buy the same experience or allowance as a dots subscription.
Does Claude have a Muse or Grok Bot equivalent?
Claude's Cowork capabilities are the relevant comparison. Anthropic is merging Cowork and chat into Claude; this is a product evolution, not the name of another language model.
Which new Claude model should I compare with Astra?
Opus 5.5 is a useful starting point for the work-oriented comparison here. Include Fable 5.1 if demanding reasoning is the priority, and smaller tiers when cost or latency matters. Newest release date alone is not a selection rule.
Is Grok Bot the same as Grok 4.7?
No. Bot is the agent product; 4.7 is a model. Product-level task completion and model-level API pricing answer different questions.
Which is cheapest?
Among the main verified model rows, Grok 4.7 has the lowest listed output rate and shares Sonnet's input rate. This excludes Muse's unverified pricing and does not compare smaller models, subscription allowances or total workflow cost.
Which is the most accurate?
This research does not establish a universal accuracy winner. Test the exact versions on the same representative work, with equivalent tools and a consistent scoring method.
Disclosure: This article was prepared with AI assistance using linked official documentation. No paid four-way benchmark or hands-on product trial was conducted for it. Provider capability claims remain attributed; pricing and access may change. The cover uses illustrated product avatars with equal prominence; dots shows an example character and Claude uses its product symbol.
