I have talked to a lot of people, and everyone is facing the same dilemma after the release of GPT-5.6 and Fable. ChatGPT Work or Claude Cowork: which one offers the most bang for the buck?
Both tools started as coding agents. OpenAI merged the standalone Codex app into a unified ChatGPT desktop app. The app now features three distinct modes: Chat (for standard Q&A), Work (for long-form, autonomous cross-app assignments), and Codex (for developer workflows). Anthropic did the same, although more recently, Cowork is now a toggle in the home interface.
The biggest differences are the models underneath them and the harnesses coordinating their work. Both can handle local files, operate desktop apps and browsers, and run scheduled workflows.
The practical choice comes down to three things: which model you trust, how much work your plan allows before you hit its usage limits, and how reliably the harness can plan, execute, recover from errors, and return something you can actually ship. And all of them are interlinked.
Given how much GPT and Fable have improved over the previous SOTA, both work for almost all tasks you could throw at them. But the subscriptions are expensive. For any meaningful work, $100 5x plans are the minimum. And no one wants to have multiple of those subscriptions.
I was in the same dilemma a week back. After spending hours testing both tools at their highest settings, I made this comparison to show where each one excels and where it falls short.
How I Tested ChatGPT Work vs Claude Cowork
To compare how each performs on real-world tasks. I used the Composio Golden Eval benchmark. It measures an agent's ability to complete tasks in business applications. The eval contains 47 scenarios and 94 trials: one run per scenario with Claude Cowork and Claude Fable 5, and one with ChatGPT Work and GPT-5.6 Sol at high reasoning.
Both of them used Composio MCP to access third-party SaaS apps such as HubSpot, PostHog, and Zendesk.
The tasks covered:
Analytics queries. Calculate event totals, funnels, retention, and cohort counts in PostHog using exact filters.
CRM reconciliation. Match records across Salesforce and HubSpot, remove duplicates, and calculate open pipeline by account or owner.
Spreadsheet operations. Update approved rows, apply range filters, build pivot tables, and reconcile records in Google Sheets.
Support operations. Identify SLA breaches in Zendesk, calculate PagerDuty incident metrics, and escalate tickets based on account tier.
Engineering workflows. Reconcile issues across GitHub, Jira, and Linear; inspect CI runs; clean up merged branches; and enforce release checks.
Batch actions. Label Gmail messages, schedule Calendar events, update Airtable records, and transition Jira issues without modifying excluded records.
History and access audits. Review Drive permissions, GitHub collaborators, Gmail history, Salesforce field changes, and PagerDuty incident timelines.
Guarded writes. Update the specified Notion blocks, calendar occurrence, feature flag, or support ticket without changing adjacent records.
…39 more
I scored each task on five measures:
Whether it passed the expected checks.
Whether the result used the required filters and returned exact values.
Whether it changed only the intended records.
How it recovered from tool or API failures.
How much time, usage, and repair the task required.
This separates model errors from harness errors. A model can understand the task and still fail because the harness chose the wrong tool, skipped pagination, lost state, or made an extra write.
Alongside the benchmark results, I kept notes on subjective stuff:
User experience: how easy it was to start, steer, approve, and review a task. Inline plots, shareable artifacts, etc
Model feel: how each model behaved, interpreted instructions, handled ambiguity, and made trade-offs.
Async reliability: whether tasks kept running, resumed across devices, and finished without intervention.
Extensibility: how easily each product connected to apps and reused skills, plugins, or MCP tools.
On Composio Golden Eval
Composio Golden Eval measures whether an agent can complete tasks in business applications, not whether it can describe the steps. The report contains 47 scenarios and 94 trials, with each scenario run once through Claude Code and once through Codex CLI. Both used Composio MCP to access the applications.
Benchmark setup
Claude run | OpenAI run | |
|---|---|---|
Harness | Claude Cowork | ChatGPT work |
Model | Claude Fable 5 | GPT-5.6 Sol |
Scenarios | 47 | 47 |
Tool layer | Composio MCP | Composio MCP |
Verification | Output and state checks | Output and state checks |
Reasoning setting | High | High |
Context length | 1 million tokens | 1.05 million tokens |
Each scenario starts with seeded records and ends with verifier checks. A task passes only when the agent returns the expected values and makes the required state changes. Similar tags, duplicate records, existing values, and excluded records test whether the agent stays within scope.
What the scenarios test
Capability | Example tasks | What can fail |
|---|---|---|
Exact queries | PostHog aggregates, Datadog logs, Salesforce pipeline reports | Broad filters, wrong totals, or similar tags included |
Cross-app reconciliation | Match Salesforce and HubSpot records; compare GitHub and Linear issues | Identity mismatches or stale state |
Batch updates | Label Gmail messages, transition Jira issues, update Airtable records | Skipped records or changes to excluded records |
Pagination | Collect Slack threads, GitHub runs, Zendesk exports, and cohort members | Results from later pages omitted |
History reconstruction | Audit Gmail, PagerDuty, Drive, and Salesforce changes | Events missing or ordered incorrectly |
Guarded writes | Edit one calendar occurrence, Notion block tree, or feature flag | Adjacent records changed or approval rules ignored |
Failure recovery | Retry rejected queries and continue after tool errors | Lost state, repeated writes, or an incorrect fallback |
Results
Metric | Claude Code · Fable 5 | Codex CLI · GPT-5.6 Sol |
|---|---|---|
--- | ---: | ---: |
Coverage | 47/47 | 47/47 |
Passed | 47/47 | 45/47 |
Success rate | 100% | 96% |
Quality score | 100% | 96% |
Average runtime tokens | 276,957 | 223,779 |
Average turns | 6.1 | Not recorded |
Average provider calls | 4.5 | 6.5 |
Average recorded tool calls | 5.1 | 6.5 |
Average agent time | 84.4 seconds | 79.6 seconds |
Verifier failures | 0 | 2 |
Fable completed every case. Sol used about 19% fewer runtime tokens and finished about 6% faster on average, but failed two verifier checks.
GPT-5.6 Sol failures
Scenario | Expected | Reported | Failure |
|---|---|---|---|
Zendesk SLA classification | Three breached tickets | 95 breached tickets from 97 inspected | The result included tickets outside the seeded set |
PostHog cohort count | 231 people; 181 with email | 231 people; 186 with email | The total was correct, but the email count was five too high |
Reading the result
The benchmark rewards exact execution. A plausible answer fails if the agent skips a page, includes an extra record, or modifies the wrong object. This makes it useful for evaluating the model and the harness together: tool selection, state management, retries, pagination, and write control all affect the score.
Fable 5 was more reliable on this benchmark, passing all 47 scenarios. GPT-5.6 Sol was faster and used fewer runtime tokens, but passed 45 of 47.
The result favours Fable when exact filtering and controlled writes matter most, and Sol when speed and token use matter more and the output can be verified.
The Models: GPT 5.6 vs Claude Fable 5
Claude Fable 5 has an edge over GPT 5.6 Sol in terms of absolute reliability. However, Fable 5 is a token guzzler, and you’d run out of limits faster than GPT 5.6. Not that GPT 5.6 would show more mercy; it definitely is the more efficient of the two.
The models have similar context limits. Claude Fable 5 supports a 1-million-token context window and up to 128,000 output tokens. GPT-5.6 Sol supports a 1.05-million-token context window and up to 128,000 output tokens. The usable context inside Cowork or ChatGPT Work can be smaller because each product manages conversation history, tool results, and compaction.
I personally prefer GPT 5.6 sol for its personality. It is straightforward and to the point. For analysis and crunching data, Fable is a beast and has almost zero error rate.
Also, yes, ChatGPT has image generation models; they definitely add value. Claude doesn’t have one yet. If it’s something you need for minor edits and image generation, this does make a difference.
The runtime: Codex lineage vs Claude Code lineage
Anthropic has been running this loop in public longer. Cowork uses the same agentic architecture that powers Claude Code, minus the terminal. Sub-agent coordination, parallel workstreams, and long-horizon task decomposition: all of it was hardened by developers working on it for months before the non-technical (Cowork) user opened the app.
OpenAI arrived at the same conclusion from the same direction. More than five million people use Codex weekly, and more than one million of them use it for work that has nothing to do with software. The coding tool had already become a general work agent, even though not by design.
ChatGPT Work is the rebrand of the accidental use case into a product.
In spite of having similar origin stories, where they differ is in what the loop or the tool is allowed to touch or not.
Cowork on desktop points to a folder and works directly with the file system. It reads, edits, and creates files in place, and every file it makes shows up in Finder or File Explorer immediately, no download link, no expiry. Deleting anything requires your approval. Permissions are scoped to the folders you name and nothing else.
ChatGPT Work operates in a cloud environment with a built-in browser that navigates sites, fills out forms, takes actions, and returns completed artefacts. The desktop app adds on-device computing, so it can run your apps and move files, but the centre of gravity stays in the cloud.
Related: Claude Code vs OpenAI Codex
ChatGPT Work vs Claude Cowork: Async Workflows
For cloud-only work, there is little practical difference. Both products can run scheduled tasks and continue long-running work without the user having to watch the session. A report that pulls from Slack, Salesforce, and Google Sheets can run while the computer is off, provided the required services are available through cloud connectors.
The difference becomes apparent when a task requires a specific computer.
Claude separates cloud and desktop-dependent work. Cloud sessions run on Anthropic’s servers and continue after the app closes. Tasks that use local files, the browser, or computer controls require the computer to remain awake and Claude Desktop to stay open. Dispatch provides one persistent thread across mobile and desktop, then routes development work to Claude Code and other tasks to Cowork. It is a remote-control workflow, not a way to use an offline computer.
ChatGPT Work also runs long tasks in the cloud and supports one-off and recurring schedules, as well as change monitoring. On desktop, it can use local files, applications, and computer controls. OpenAI does not document those local capabilities as remaining available after the computer turns off, so they should not be treated as unattended cloud operations.
Workflow | Claude Cowork | ChatGPT Work |
|---|---|---|
Cloud and SaaS task | Runs without the computer | Runs without the computer |
Recurring schedule | Supported | Supported |
Change monitoring | Not documented as a separate scheduling mode | Supported |
Local files or desktop apps | Requires the computer and Claude Desktop | Requires access to the computer |
Phone-to-desktop control | Dispatch provides a persistent routed thread | Tasks can be started and reviewed across devices |
The practical rule is the same for both: cloud tasks can run unattended; local tasks need the computer that holds the files or applications. Claude offers the clearer phone-to-desktop workflow through Dispatch. ChatGPT offers a broader cloud scheduler that includes change monitoring.
Claude Cowork vs ChatGPT Work: User Experience
ChatGPT Work uses the same interface as ChatGPT and Codex, so its projects, conversations, files, and controls are familiar. It can return documents, spreadsheets, presentations, and analyses, and ChatGPT Sites can create, preview, publish, and share working websites or lightweight web apps.
Claude Cowork handles visual output through Artifacts. A standard Claude Artifact places a document, app, tool, visualization, or working HTML page in a separate panel where you can view, revise, and share it. Claude can also render charts, diagrams, and interactive visuals inline in the conversation.
Cowork adds Live Artifacts on the desktop app. These are persistent interactive HTML dashboards that can refresh with current data from connected apps. Live Artifacts are available on paid plans and currently appear only in Claude Desktop, not on web or mobile. Cowork can also write the underlying HTML and other files directly to a selected local folder.
Now you can even add MCP connectors to Artifacts to build dashboards fetching data from your apps. Which is cool.
The difference is where the output appears. Claude is better at showing a chart or interactive explanation next to the response and keeping HTML tools and dashboards in its Artifacts view. ChatGPT is better when the result needs to become a published Site or shared web app. Both produce working HTML; Claude emphasises inline visuals, Artefacts, and connected dashboards, while ChatGPT emphasises previewing, hosting, and sharing Sites.
Task review is also different. Cowork exposes more of the plan and makes it easier to steer work while it runs. ChatGPT Work keeps the interface simpler and places more emphasis on the completed artefact and execution trail.
Verdict
Claude Cowork is better for inline visuals, Artifacts, and dashboards connected to app data. ChatGPT Work is better when the output needs to become a hosted Site with a shareable URL. Choose Cowork if you want to inspect and refine the work inside the agent; choose ChatGPT Work if publishing the result is part of the task.
Claude Cowork vs ChatGPT Work: Pricing and Usage Limits
ChatGPT Work is available on every ChatGPT plan. Claude Cowork requires a paid Claude plan. In both products, long-agent tasks consume more of the plan than standard chat does.
Individual plans
Plan | Price | What it includes |
|---|---|---|
ChatGPT Free | $0 | Limited Work access on desktop, limited Codex, uploads, research, and image generation |
ChatGPT Go | $8/month | Work and Codex for lightweight use |
ChatGPT Plus | $20/month | Work and Codex across supported surfaces, GPT-5.6 Sol, Terra, and Luna, cloud integrations, scheduled tasks, image generation and editing, and the option to buy more credits |
ChatGPT Pro 5x | $100/month | Everything in Plus with five times the Work and Codex usage |
ChatGPT Pro 20x | $200/month | Everything in Plus with twenty times the Work and Codex usage |
Claude Free | $0 | Claude Chat with limited usage; no Cowork |
Claude Pro | $20/month or $200/year | Cowork, Claude Code, Claude Design, projects, more models, and Microsoft 365 support; Fable 5 uses pay-as-you-go usage credits |
Claude Max 5x | $100/month | Everything in Pro with five times the Pro capacity per session and higher output limits |
Claude Max 20x | $200/month | Everything in Pro with twenty times the Pro capacity per session and higher output limits |
How usage is metered
ChatGPT: Work and Codex share the same usage limits and credits. The model, reasoning level, task length, tool calls, and image generation affect consumption. Plus users can buy credits after reaching the included limit. Pro increases the shared limit by 5x or 20x; it does not make usage unlimited.
Claude: Cowork draws from the same plan limits as other Claude features and consumes those limits faster than Chat. Max increases capacity by 5x or 20x over Pro. Fable 5 has a separate constraint: on Max, up to 50% of the weekly plan limit can be used with Fable at no extra cost. After that, it uses pay-as-you-go credits. On Pro, Fable uses credits from the start.
The comparable tiers are therefore Plus versus Pro at $20, and ChatGPT Pro versus Claude Max at $100 or $200. The subscription price does not tell you how many agent tasks it will complete. Run one representative task, record its usage, and use the result to estimate the workflow's cost.
Verdict
You can definitely feel that ChatGPT Work gets you more for similar pricing than Claude does. This is given that Fable is the pricier of the lot. For the 20x Plan, prefer Claude Cowork for $20, ChatGPT for $ 100, or ChatGPT for me, but you won’t go wrong with Claude.
Claude Cowork vs ChatGPT Work: Extensibility and Connectors are not the same as context
OpenAI confirmed its new plugin directory is built on the Model Context Protocol, the standard Anthropic published.
OpenAI's Unified Plugins Directory features Google Drive, SharePoint, Slack, Teams, Gmail, Outlook, Salesforce, Adobe, Zoom, LinkedIn, GitHub, Canva, and Dropbox at launch, with a much longer tail behind them, all callable by typing @ in the prompt.
Anthropic's plugin system bundles skills, connectors, slash commands, and sub-agents into a single install, and the whole thing is in markdown and JSON. Anthropic open-sourced eleven of them for roles like sales, legal, finance, and marketing. Skills fire automatically when relevant. Slash commands sit there for when you want them explicitly.
There is honestly no difference here. Both of them support MCP, Skills, Plugins, etc.
Comparing Claude Cowork and ChatGPT Work
Claude Cowork’s weaknesses:
Requires a paid plan.
Has no native image-generation model, although it can create inline visuals and HTML Artifacts.
Fable 5 consumes plan limits quickly. It requires usage credits on Pro and after reaching its included Max allowance.
Live Artifacts are limited to Claude Desktop and do not appear on web or mobile.
ChatGPT Work’s weaknesses:
Work and Codex share the same usage allowance, so heavy use of one reduces capacity for the other.
Task consumption varies by model, reasoning level, tools, and output type, with no fixed cost per task.
Its Microsoft 365 write workflows are less developed than Claude’s.
Some features, including Sites and computer use, remain in beta or have plan-dependent limits.
Both support scheduled work, connected applications, local computer access, and cloud execution. Their main constraints are usage limits, approval interruptions, and the boundary between cloud tasks and tasks that require a computer to remain online.
Should You Use Claude Cowork or ChatGPT Work?
There is no overall winner. Claude Cowork is the stronger choice for exact tool use, local-file workflows, Microsoft 365, inline visuals, and dashboards that stay connected to app data. ChatGPT Work is the stronger choice for speed, broader output formats, image generation, cloud scheduling, and publishing work as a hosted Site.
Priority | Pick | Why |
|---|---|---|
Exact filtering and controlled writes | Claude Cowork | Fable 5 passed 47 of 47 benchmark scenarios |
Speed and lower token use | ChatGPT Work | GPT-5.6 Sol finished faster and used fewer runtime tokens |
Local files and desktop workflows | Claude Cowork | Works directly with selected folders and exposes more of the task while it runs |
Cloud and scheduled workflows | ChatGPT Work | Supports recurring tasks and monitoring for changes |
Charts and connected dashboards | Claude Cowork | Inline visuals, Artefacts, Live Artefacts, and MCP-connected dashboards |
Images and published web apps | ChatGPT Work | Native image generation and hosted Sites with shareable URLs |
Extensions and SaaS access | Tie | Both support MCP, plugins, skills, and connected applications |
At $20, I would choose ChatGPT Work. Claude Pro includes Cowork, but Fable 5 uses pay-as-you-go credits, while ChatGPT Plus includes the GPT-5.6 family, image generation, scheduled work, and Sites.
At $100, ChatGPT Work remains the better general-purpose package. Choose Claude Max instead if your work depends on local files, Microsoft 365, connected dashboards, or the reliability advantage shown in the benchmark.
At $200, I would choose Claude Cowork for sustained, high-stakes agent work where incorrect reads or writes cost more than extra usage. ChatGPT Work remains the better option when image generation, publishing, and cloud-first workflows matter more.
My final verdict: ChatGPT Work is the better default for most people. Claude Cowork is the better specialist tool for file-heavy, data-heavy, and accuracy-sensitive work.