Comparing Hermes Agent, OpenClaw, and Claude Cowork

I'd heard a lot about the gstack repo from the CEO of Y Combinator, so I got curious and installed it to try. What surprised me most wasn't the polished workflows — it was the genuinely different mindset behind them. That mindset shows up in the very first command: /office-hours, with six questions that don't ask about code at all, only the things most people haven't thought through before they start building. What is gstack and why did Garry Tan build it gstack is an open-source toolkit by Garry Tan, CEO of Y Combinator, built primarily for Claude Code. The core idea: instead of using AI as a plain code writer, Garry Tan wanted to turn Claude into a small AI agent team, where each member handles a different role — from product direction and security review to testing and release. The entire workflow runs in an ordered loop: Think → Plan → Build → Review → Test → Ship → Reflect. More specifically, gstack splits Claude Code into 23 specialized roles, and the output of each step is automatically passed to the next — no manual handoff needed. Some of the standout commands: /office-hours 6 questions that force you to rethink your feature before writing a single line of code /plan-ceo-review checks whether you're overbuilding or underbuilding relative to what's actually needed /review catches serious bugs that standard automated checks miss /qa opens a real browser, performs real interactions, finds real bugs /cso runs an automated security audit against international standards /ship syncs, tests, pushes code and opens a pull request in a single command How effective is gstack? Garry Tan says his working speed in 2026 is roughly 810 times faster than in 2013, measured by lines of completed code per day (11,417 vs 14). In 60 days, he shipped 3 production services and over 40 features — all while running Y Combinator full-time. Andrej Karpathy, co-founder of OpenAI, confirmed a similar trend, sharing that he hasn't typed a single line of code himself since December 2025 thanks to AI agents. But among all those commands, /office-hours stands out for the opposite reason from the rest, it doesn't help you work faster and it helps you avoid building the wrong thing from the start. Why Garry Tan puts /office-hours first Garry Tan placed /office-hours at the top of the workflow based on a simple observation: most products fail not because of poor code, but because they build the wrong thing. Teams spend weeks on a feature nobody needs, or build the right feature for the wrong audience, or solve a problem users already handle better another way. The command has two modes: Startup mode for founders and people building real products with real users, and Builder mode for side projects, hackathons, and open source. This article focuses on Startup mode, where the 6 questions are most directly applicable. 6 questions that stop you from building the wrong thing These aren't 6 questions to answer quickly and move on. They're designed to make you think honestly, because the more truthful your answers, the more accurately Claude can match what you actually need — saving you a significant amount of time later. You can read the full original prompts at office-hours/SKILL.md.tmpl. Demand reality: Is there a real need? Original question: "Who specifically has this problem? How are they solving it today?" Not "users in general" or "the marketing team" — the goal is to name one real person, ideally by name, who is actively struggling with a specific problem. If you can't name someone like that, you don't yet understand what they actually need. Concrete example: Instead of "users want better task management," it should be: "Minh, a project manager at a 20-person company, copy-pastes between Notion and Google Sheets every Monday morning because the two tools don't sync." Apply this to your own situation accordingly. Status quo: What are they using instead? Original question: "What is their current workaround? How much better do you need to be for them to switch?" Everyone is already solving their problem somehow — whether with Excel, sticky notes, or a WhatsApp group. If their current solution is good enough, they have no reason to migrate their data and learn an entirely new platform. Your solution needs to be meaningfully better before they'll even consider switching. Desperate specificity: Who needs this badly enough? Original question: "Who needs a solution badly enough to use your ugly beta version today?" This is the question that separates nice-to-have from must-have. If you can't find anyone willing to use an incomplete, rough, buggy version right now, the problem you're solving isn't urgent enough. Real early users are people who need a solution badly enough to tolerate an unpolished product — as long as it's moving in the right direction. Narrowest wedge: What is the smallest possible piece? Original question: "What is the smallest thing you could launch tomorrow? Not the full vision — the smallest piece." Not the first full-featured version — something even smaller than that. This question typically cuts 80% of the scope people add because they think "might as well do it while I'm here." It's a trap many builders fall into, including myself. Launch the smallest meaningful piece first, listen to real users, then decide whether to expand. Common mistake: Many people confuse "smallest piece" with "first full-featured version." The narrowest wedge truly means one small thing that solves one specific problem for one specific group of users — nothing more. Observation and surprise: Have you watched real people use it? Original question: "Have you watched real people use your product? Did they use it in ways you didn't expect?" This question is best saved for the second iteration onward, once you have something to test. Rather than asking for feedback through messages or surveys, sit and watch directly — or review screen recordings. The most valuable insights usually don't come from what users say, but from what they do that you didn't design for, or what they skip that you thought was important. Note: If you're in your first iteration and don't have a product yet, you can skip this question and come back after launching the smallest piece in step 4. Future-fit: The 2 to 3 year view Original question: "In 2-3 years, will what you're building still be relevant — or is the trend moving against you?" This isn't about predicting the future precisely. It's about avoiding building something that's already fading. If the trend is making your problem less urgent over the next two years, that's a clear signal to reconsider from the start. That said, if your goal is to move fast and capture the market before big tech ships something similar, this question can reasonably be set aside. A real example: a simple idea completely flipped In the gstack documentation, Garry Tan walks through a practical example. You open /office-hours and say: "I want to build an app that summarizes my daily work calendar." Claude doesn't agree and start executing. Instead, it pushes back: what you just described isn't a calendar summary app — it's actually a full personal AI chief of staff. These are entirely different in scope, technical complexity, and user expectations. From that single opening description, /office-hours helps you see: 5 features you were describing without realizing it 4 assumptions that need to be validated before building 3 different implementation directions with varying levels of complexity 1 recommendation: launch the smallest piece first, treat the rest as a long-term roadmap All of this happens before you write a single line of code. The output is saved as a document that subsequent steps in the workflow automatically pick up and continue from. These 6 questions work even without gstack The 6 questions from /office-hours don't require Claude Code or a gstack installation. They're a way of thinking — the same framework YC partners use to evaluate startups — and you can apply them right now with any AI tool you already have. The difference when using them through gstack is that Claude won't let you give vague answers. It pushes for specifics and won't move forward until your response is grounded enough to be useful. That's why /office-hours tends to be the most uncomfortable command in the entire toolkit — not because it's difficult to use, but because it asks exactly what you've been avoiding. Try it today: Before starting your next project, paste these 6 questions into Claude, Gemini, or ChatGPT along with your idea. Ask it to go through each question one at a time and not let you skip any. The results are often more surprising than you'd expect — even for ideas you've already thought through carefully. gstack currently has over 117k stars on GitHub and is still growing. For me, the most valuable part isn't the technical commands like /review or /ship — it's /office-hours, because it's the only command in the entire toolkit that forces you to stop and think before doing anything else.

A useful report usually consumes time in three places: finding sources, organizing evidence, and checking whether conclusions are actually supported. Hermes Agent fits this work because web search, browser automation, files, memory, delegation, and skills can live inside one workflow. Once configured well, a manual process can become a reusable routine for weekly reporting, content audits, and keyword research.The practical method shared here is simple: research first, save evidence, draft last, and run SEO as a separate review stage. This is slower than asking an agent to write immediately, but the output is easier to verify and less likely to blur facts with assumptions.Why Hermes Agent fits reporting and SEOAccording to the official Hermes Agent documentation, the system includes more than 60 tools covering web search, browser automation, terminal, files, memory, cron, and delegation. Hermes also has a learning loop that can create skills from experience and improve them during use. That matters when a report or SEO checklist must run repeatedly.If you are new to the platform, start with our introduction to Hermes Agent and its self learning system. This guide focuses on execution: turning a business question into a sourced report and turning a URL into an actionable SEO review.The rule used in this workflow: Hermes may search, extract, organize, and recommend. A human still approves sources, conclusions, primary search intent, and every change that can affect a live website.Four tool groups to enableWeb and search: discover sources and extract evidence.Browser: inspect rendered pages and interact when extraction is insufficient.Files: save briefs, source tables, drafts, and final reports.Memory and skills: retain brand rules, report formats, and proven procedures.Use hermes tools to inspect and configure toolsets. For web research and file writing, enable only the relevant groups instead of granting broad terminal access. A smaller permission scope reduces accidental actions and produces cleaner logs.Set up Hermes Agent for the first runInstall and choose a modelFollow the official quickstart, then run hermes setup to select a provider, model, and tools. Hermes supports Nous Portal, OpenRouter, OpenAI, and custom endpoints, so the reporting workflow does not depend on one model provider.Use a model with sufficient context for source review, but do not spend flagship model pricing on every stage. Discovery, title normalization, and table formatting can use a cheaper model. Contradiction analysis and final synthesis deserve the stronger option.Create a clear workspaceGive each project its own folder with brief.md, sources.md, notes.md, report.md, and seo-audit.md. This structure tells Hermes which file defines the request, which stores evidence, and which is the final output. It also makes later comparisons easier.In a competitor report, I create one folder per month, list five sites in the brief, and require every conclusion to point to a row in sources.md. If a claim lacks evidence, Hermes must mark it as unverified instead of filling the gap.Use Hermes Agent to create a sourced reportStep 1: Write a brief with a completion testA useful brief answers five questions: who will read the report, which decision it supports, what time range matters, which sources are acceptable, and what sections the output must contain. A broad request such as researching the AI market encourages unnecessary scope.The prompt I use is: “Read brief.md. Find no more than 12 official or reputable sources published within the last 90 days. Save the URL, access date, main evidence, and the passage supporting that evidence in sources.md. Do not write the report until the source table is complete. Create a conflict section when sources disagree.”Step 2: Research before writingHermes provides web search and extraction, while browser automation is better for rendered pages and interactions. The Web Search and Extract documentation recommends browser navigation when summarization is not enough. Search broadly first, then open only important sources in the browser.The best output at this stage is not polished prose. It is a clean source table containing title, publisher, URL, date, evidence, and confidence. Missing dates and authors should remain missing rather than being guessed.Step 3: Synthesize around the business questionAfter sources.md is approved, ask Hermes to write report.md and separate facts, interpretation, and action. Readers can then see which statements are supported and which are analytical judgments.During one content report, Hermes found that traffic declined when several pages lost internal links. Instead of claiming a search penalty, the report preserved two hypotheses: weaker internal architecture and lower search demand. The first action was restoring links and observing the result, not rewriting the entire site.A useful verification prompt: ask Hermes to add a section called “What this report does not prove.” It is often the most valuable part of the document.Use Hermes Agent for practical SEOStart with search intentProvide the URL, audience, and conversion goal, then ask Hermes to identify the primary intent, supporting questions, related entities, and page type visible in search results. A keyword list alone often creates content that repeats phrases without satisfying the reader.A practical prompt is: “Inspect this URL with the browser. Identify one primary intent and three supporting intents. Compare the title, headings, introduction, topic coverage, internal links, and schema with five relevant results. Do not recommend keyword density. Every recommendation must name the location to change and the benefit for the reader.”Run an on page checklistHermes can review titles, descriptions, canonicals, hreflang, headings, alt text, internal links, structured data, and duplicate content. Browser automation is useful because it sees the rendered page. Keep audit permissions separate from editing permissions so a review cannot silently modify production.Does the title describe the intent without duplication?Does the introduction answer the main question early?Do H2 and H3 headings create a clear reading path?Do internal links lead to deeper, relevant explanations?Does schema match content visible to users?Are image URLs valid and alt descriptions useful?Create a content brief before draftingDo not ask Hermes to write the SEO article immediately after research. Require a content brief with persona, intent, angle, H2 and H3 structure, evidence, internal links, and unresolved questions. Approve the brief before drafting.For this Hermes Agent guide, the brief includes more than the phrase “Hermes Agent SEO.” It requires an explanation of toolsets, real prompts, browser permission warnings, a link to the foundational article, and a checklist for measuring the result.Turn the workflow into a reusable skillHermes is distinguished by its skill system. The quickstart describes skills as instruction documents loaded when a task matches, while the learning loop can suggest preserving procedures after complex work. Once a reporting workflow succeeds two or three times, convert it into a skill instead of maintaining a long prompt.What a reporting skill should containTrigger conditions and accepted inputs.Required folder and file names.Source selection rules and conflict handling.Report format, source table, and unverified section.A review checklist before delivery or publication.For an SEO skill, add rules that prevent production edits, forbid unsupported schema, and protect canonical URLs. These safeguards are more important than a keyword list because they stop plausible but harmful changes.Schedule reports with cronHermes supports scheduled automation and delivery to messaging platforms. A weekly report can run on Monday morning, collect new evidence, compare it with the previous week, and send a summary. Automate reading and synthesis, but keep publication and SEO changes behind approval.Protect sensitive data: sanitize customer information, limit file and browser access, and use command approval or isolation when Hermes can access a terminal.Mistakes I encounteredThe objective is too broad“Analyze the entire website” produces a long output with little direction. “Find three evidence backed causes for an impressions decline in tool articles” produces a smaller report that supports action. Scope should connect to a decision.Writing begins before source reviewPolished prose can hide weak sources. Separating sources.md from report.md makes errors visible. Review the evidence table before synthesis.SEO is reduced to one scoreA single score does not explain what to change. Hermes should return an issue, evidence, priority, effort, and a validation method. Recommendations that cannot be measured should rank lower.The workflow never becomes a skillIf the same prompt needs correction every week, the learning loop is being wasted. Record mistakes, update the checklist, and improve the skill after every run.How should you start?Choose one small report, no more than five sources, and one clear decision. Create a brief, ask Hermes for a source table, approve the evidence, and only then permit report writing. Use the same evidence for a content brief or SEO audit, but do not let the agent publish automatically.Once the process is stable, save it as a skill and add a final review checklist. Hermes Agent is most valuable not because it writes one report faster, but because it turns a good method into a procedure that can be repeated, inspected, and improved over time.

Learning more makes you better, a principle long assumed to apply only to humans, turns out to hold true for Hermes Agent too, an open-source AI agent from Nous Research. Every time you work with it, Hermes Agent doesn't forget, it remembers, understands you more deeply, and gets better with each session, thanks to a memory system that can recall everything about you even after the machine has been off for a week. What Is Hermes Agent? Hermes Agent is an open-source AI agent developed and released under the MIT license by Nous Research, the lab behind the Hermes, Nomos, and Psyche model lines. Unlike Antigravity or Codex, which depend on an IDE environment, or ordinary chatbots that ultimately remain a thin wrapper calling a single API, Hermes Agent is built to run continuously on a user's own infrastructure, from a cheap VPS to a GPU cluster or serverless infrastructure, and it operates in a way fairly similar to OpenClaw. The core difference in Hermes Agent lies in how it manages long-term memory and converts experience into real skills. Instead of merely storing raw information or passively remembering preferences the way AI like Gemini or Claude do, Hermes runs a closed "learning loop," meaning that after every work session, it actively distills the process into new tools it can use the next time. This system is run by a background "Curator" agent that automatically scores, prunes, and merges accumulated knowledge, combined with FTS5 search technology that retrieves old memories roughly 4,500 times faster without spending any tokens. As a result, Hermes doesn't just respond and forget, it genuinely becomes a collaborator that grows more knowledgeable and capable over time. Four Features That Set Hermes Agent Apart Nous Research doesn't call Hermes Agent a chatbot or a copilot, it positions it as an agent with a built-in learning loop. The four feature groups below explain why that label isn't just marketing. Memory That Persists Across Sessions The biggest weakness of most AI today is that memory only stores raw chat text rather than how work actually gets done. Hermes Agent addresses this through three combined mechanisms: Fast retrieval: Uses FTS5 full-text search to pull up old memories roughly 4,500 times faster than conventional search, without spending extra tokens the way Gemini or Cowork do. User understanding: Integrates Honcho's dialectical user-modeling approach, helping the agent understand preferences, habits, and personal context in depth across thousands of sessions. Continuity: The agent picks up work exactly where you left off, even if that was a project from weeks earlier. Self-Generating and Self-Improving Skills This is the feature that makes Hermes Agent behave like a collaborator that accumulates experience, rather than just a tool that answers on request: Learning from real use: After completing complex tasks, Hermes Agent distills the process into new skills and stores them in a library to be reused automatically next time. Open agentskills.io standard: These skills follow an open standard, so they can be packaged, shared, and reused across different AI systems without being rewritten from scratch. The Curator mechanism: A background administrative agent periodically scores, prunes, and merges duplicate skills, which keeps the skill library from bloating and becoming disorganized over time. Present on More Than 23 Messaging Platforms Hermes Agent isn't confined to a computer, it integrates directly into the messaging channels people already use on their phones every day: Multiple channels, one brain: You can command Hermes Agent through Telegram, Discord, Slack, WhatsApp, Signal, email, or SMS. Context retained: Whether you message via Telegram in the morning or switch to Discord at night, the agent keeps a single thread of memory, never fragmented by channel. Multimodal interaction: Supports sending voice messages, images, and video, along with the ability to analyze multimodal content. Flexible Runtime Infrastructure Hermes Agent supports six backend types for executing commands: local machine, Docker, SSH, Daytona, Singularity, and Modal. With Daytona and Modal, the environment can hibernate when idle and cost almost nothing while waiting, waking up only when there's work to process. This is why Nous Research describes Hermes Agent as an always-on agent that doesn't require users to keep a server running 24/7 at high cost year-round. Hermes Agent can be installed with a single curl command, supporting Linux, macOS, and Windows via WSL2, or, as of June 5, 2026 with version v0.16.0 "The Surface Release," through an official Native Desktop app for Windows, macOS, and Linux with a fully polished GUI, making it accessible to everyday users without needing a terminal. Built-In Toolset and Limitations to Know 40-Plus Built-In Tools, From Web Search to Schedule Automation Hermes Agent ships with more than 40 built-in tools, including web search, browser actions, file handling, and Python script execution via RPC to run sub-tasks without consuming the main agent's context window. A natural-language scheduling system lets you set recurring tasks like daily reports or data backups, then leaves the agent to run them without being reminded. For tasks that need full isolation, Hermes Agent also supports sub-agents with their own conversation, terminal, and scripts, allowing multiple jobs to run in parallel without diluting the main memory. Challenges and Security Considerations Despite rapid updates, Hermes Agent still has a few points users should keep in mind before deploying it: Stability of the self-learning mechanism: The ability to self-improve skills boosts success rates, with a Tencent Cloud report recording gains of up to 52% along with token savings of up to 61%. However, since this is a self-evolving mechanism, real-world effectiveness still depends on the underlying model chosen and still requires human oversight rather than full trust. Risk from high-level permissions, with security responsibility falling on the user: Hermes Agent can intervene deeply in a system (excessive agency), so connecting it directly to multiple messaging platforms requires users to manage their own API keys and set up guardrails. Unlike closed AI services, Hermes Agent hands full control over to the user, which means the user also bears greater responsibility for configuring access permissions to avoid information leaks. Why Is Hermes Agent Growing So Fast? Hermes Agent's growth could be attributed to Nous Research's marketing, but in our view it comes down to three main factors. A Frictionless Migration Path From OpenClaw Recognizing OpenClaw's large user base, Nous Research built a migration tool that lets users carry over their persona, API keys, the entire skill set, and memory to Hermes Agent with a single command, without losing old data and, of course, without having to reconfigure anything from scratch. If you're currently using OpenClaw and want to try Hermes Agent without losing your old data, look for the hermes claw migrate migration tool built into Hermes Agent before considering a fresh install. Betting on a Closed Learning Loop Instead of a Feature Race While many other agents compete on the number of tools they offer, Hermes Agent positions itself as a self-evolving entity, one that distills experience into new skills and retains long-term memory to understand users more deeply over time. This approach creates lasting value, and the community has already put it to use for projects such as automating large-scale content production with high consistency across many sessions. A Role as a Training Data Engine Beyond serving as a personal assistant, Hermes Agent also functions as a capable research tool. It can generate thousands of parallel tool-calling trajectories and compress them into training data for other AI models. By turning the agent's real-world experience into training data, Hermes becomes a platform that developers building the next generation of autonomous AI can't easily do without. How Is Hermes Agent Different From an Agent Harness? People new to the space often confuse Hermes Agent with the concept of an agent harness, which is the framework that decides how a model calls tools, handles the reasoning loop, and coordinates execution steps internally. If a harness is the engine and chassis that determine how a car drives, then Hermes Agent is like a car that already has that engine installed, plus seats, a navigation system, and the driver's own trip memory. In other words, a harness is the technical architecture layer underneath, while Hermes Agent is a complete end-user product that already packages memory, a skill system, communication channels, and a choice of runtime infrastructure. A developer can build their own harness to control every small detail, but most users don't need to go that deep, they just need an agent that runs right away and gets smarter through use. For a closer look at this underlying architecture layer, read more at What Is Agent Harness? The Framework That Makes AI Work Efficiently, which explains in detail how this type of framework operates. Is Hermes Agent Worth Trying Right Now? Being fully open source, collecting no user data, and supporting complete self-hosting, Hermes Agent is one of the few agents today that lets users keep full control over their own data while still getting a continuous assistant experience with real memory, not the simulated memory that only exists within a single chat. After v0.16.0, the biggest technical barrier for users unfamiliar with terminals has largely been removed, as the native desktop app for Windows, macOS, and Linux has fully replaced the pure CLI approach used before. What's left to judge about Hermes Agent isn't whether it runs, but what it learns after a few real weeks of use. The fastest way to find out is to install the desktop app or run the CLI on a cheap VPS, connect it to a familiar messaging channel like Telegram, then watch what skills the agent forms on its own from how you use it every day. That's also the groundwork for comparing Hermes Agent with other options on the market, from Agent Harness to OpenClaw and Claude Cowork, in the next part of this series.

Sol, Terra, and Luna make GPT-5.6 look more like a product family than a single model. The naming also signals what OpenAI is trying to change: users no longer have to choose only between an expensive flagship and a much smaller model. Instead, they get three capability tiers designed for different workloads. The important caveat is that GPT-5.6 is currently in limited preview, and OpenAI says it is not available in ChatGPT during this preview period.On the other side, Anthropic positions Claude Fable 5 as a frontier model for reasoning, software engineering, scientific research, and long horizon agentic work. The useful question is therefore not simply which model is smarter. It is which product architecture helps a team complete real work with predictable quality, latency, and cost.What GPT-5.6 actually isAccording to OpenAI's preview announcement, GPT-5.6 consists of Sol, Terra, and Luna. Sol is the flagship and most capable option, Terra is a strong lower cost model, and Luna is the fastest and most cost efficient member of the family.The important change is how OpenAI divides demand into three tiers. A research team might use Sol for a difficult reasoning problem, a product team might run most daily work on Terra, and a high volume system might use Luna for thousands of short requests. This looks more like an infrastructure strategy than the launch of a single new chatbot.Availability matters: OpenAI says GPT-5.6 is not available in ChatGPT during the preview. An experience in an API, developer tool, or partner platform should not be treated as the final ChatGPT experience.Sol is designed for difficult, extended workSol is positioned as the strongest GPT-5.6 model for deep reasoning, complex coding, and long multi step tasks. A software team might ask it to understand a repository, identify the cause of a bug, propose a minimal patch, and write regression tests. Sol's value is not answering a short question quickly. It is maintaining the objective while working through a longer chain of decisions.OpenAI also highlights stronger cyber capability as reasoning increases. That can be useful for authorized security testing and vulnerability analysis, but it also makes access controls, logging, sandboxing, and human approval more important.Terra aims for the practical middleTerra targets the broadest category of work: document analysis, content production, application development, research synthesis, and operational support. If Sol is the specialist called for the hardest problem, Terra is the strong team member expected to work throughout the day without making every request unnecessarily expensive.A marketing team could use Terra to read market reports, extract insights, build an outline, and draft several content variants. A development team could use it for code review, test generation, and tickets with a clear scope. This tier could become the default if its real world quality remains consistent.Luna prioritizes speed and scaleLuna is designed for low latency and lower cost. Classification, conversation summaries, field extraction, drafting, and ticket routing do not always require the strongest model. In these cases, response time and total operating cost matter more than maximum reasoning capability.Fast does not mean suitable for everything. If a task requires source verification, a long plan, or a code change with a large blast radius, a team should move it to Terra or Sol instead of forcing Luna beyond its intended role.Claude Fable 5 takes a different routeAnthropic presents Claude Fable 5 as a frontier model for reasoning, software engineering, vision, scientific research, and long horizon agentic work. Instead of emphasizing three product tiers in one generation, Anthropic's message focuses on the capability of a powerful model working inside the Claude ecosystem.This difference changes deployment decisions. With GPT-5.6, an engineering team might build a router that sends each request to Sol, Terra, or Luna. With Fable 5, the focus may be on optimizing prompts, tools, context, and reasoning budgets around one primary model. Neither approach is universally better because the answer depends on workload and operational maturity.A fair comparison: Do not run one prompt and declare a winner. Build a test set covering short tasks, long reasoning, coding, extraction, and recovery from errors. Measure accuracy, latency, the number of human corrections, and the total cost of a completed task.Coding and agentic work depend on the surrounding toolsBoth GPT-5.6 Sol and Claude Fable 5 target complex software work, but the practical experience depends heavily on the system around the model. The ability to read a repository, execute commands, observe results, and correct mistakes can matter as much as a benchmark score. For OpenAI workflows, the Codex page is a useful starting point for understanding how a model participates in coding work.Fable 5 may be attractive to teams already invested in Claude and long running agentic workflows. Read our Claude Fable 5 coverage for more context on Anthropic's positioning and the types of work it targets.What early forum experience tells usEarly discussions on Reddit and developer communities focus on how different Sol, Terra, and Luna feel in real work. Some users describe Sol as the better fit for multi step tasks, Terra as the practical option for routine work, and Luna as the interesting choice for speed. These observations match OpenAI's positioning, but they do not establish a precise quality gap.Forum reports are useful because they reveal the questions real users care about. However, they are self selected evidence. People may use different prompts, access levels, integrations, and preview versions. A result from a developer platform does not guarantee the same result when a model eventually appears in ChatGPT.Early positivesThe three tiers make it easier to understand which model belongs to which workload.Luna creates a clear expectation of low latency for high volume systems.Terra could become a default if it delivers stable quality at a practical cost.Sol is expected to be stronger for coding, long reasoning, and tasks with several verification steps.Open questionsHow large the practical quality gap between Sol and Terra will be on common workloads.The total cost after retries, corrections, and human review are included.How Luna behaves with long prompts and many constraints.Whether performance remains stable as GPT-5.6 expands beyond preview access.Forum reports are not benchmarks: Community experience should help you choose test cases, not make a production purchasing decision by itself.Comparing GPT-5.6 and Fable 5 by workloadWriting and document analysisTerra appears positioned for most document work because it balances capability and cost. Fable 5 may be attractive when documents are long, questions are complex, and the model must maintain an argument across a large context. A useful evaluation should score citation accuracy, structural consistency, and how much editing is required before publication.Software development and debuggingSol and Fable 5 are both candidates for difficult coding tasks. A representative test should include reading existing code, identifying the root cause, producing a minimal fix, writing tests, and explaining risk. Asking a model to create an isolated function from scratch does not reflect how well it works in a real repository.High volume processingLuna has the clearest positioning advantage when speed and cost dominate. At thousands of extraction or classification requests per day, a small difference in price and latency can have a large effect. Fable 5 may be unnecessarily expensive for a workload that only needs short, structured outputs.Research and long reasoningSol and Fable 5 should be compared with tasks that have verifiable outcomes rather than open questions that merely sound impressive. Give both models the same research material and ask them to identify assumptions, detect contradictions, propose an experiment, and explain what evidence is missing. The better model is the one that helps users discover errors faster, not the one that writes the longest answer.Should you choose Sol, Terra, Luna, or Fable 5?If you want maximum capability inside the OpenAI ecosystem, Sol is the first model to test. If you need a strong model for regular use, Terra has the more practical position. If your workload contains many short and repetitive tasks, Luna could reduce operating cost. Fable 5 remains relevant for teams invested in Claude or focused on long reasoning and agentic work.Because GPT-5.6 is still in preview, replacing an entire production workload would be premature. Run the models in parallel on real but sanitized data, record failures, and use the same criteria for every candidate.A test plan you can use nowSelect 20 tasks that represent real work, including easy and difficult cases.Run each task on Sol, Terra, Luna, and Fable 5 when access allows.Score accuracy, response time, total cost, and required human correction.Track severe failures separately instead of relying only on averages.Choose a model for each workload category rather than forcing one model to do everything.Is GPT-5.6 worth switching to now?The most important change in GPT-5.6 may not be Sol's raw capability. It is OpenAI's decision to turn one model generation into three operational tiers. That could help organizations control cost, but only if they can classify workloads and route requests intelligently.The practical next step is to build a small benchmark from your own data. If Sol wins difficult tasks, Terra is good enough for routine work, and Luna handles high volume requests reliably, the three tier architecture has real value. If Fable 5 remains more consistent on long reasoning, a multi model strategy may still be better than committing to one provider.