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The fairest way to compare AI assistants is to test the products you can actually use, not to choose a model from a leaderboard. Give each finalist the same representative coding, research, and everyday tasks; then score correctness, source support, editing effort, workflow fit, privacy controls, limits, speed, and total plan cost. A model can be excellent in a benchmark yet awkward in your editor, unavailable in your country, or unsuitable for the data you handle.
Start with the product, not the model name
An AI assistant is a service wrapped around one or more models. The surrounding product determines whether it can read your files, search the current web, connect to cloud accounts, edit a repository, run tools, remember preferences, or work inside your preferred editor. Account plan, geography, workspace policy, administrator settings, and connector permissions can change those capabilities.
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Model lists are still useful for forming a shortlist. GitHub’s official Copilot model guide, for example, groups models by practical roles such as fast help, deep reasoning and debugging, general-purpose coding, and longer-running agentic work. That guide explains model positioning inside Copilot; it is not a neutral ranking of consumer applications. Treat any model description in the same way: as a clue about what to test, not as proof that the whole assistant fits your workflow.
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Write down the tasks you perform repeatedly. A person maintaining a production repository needs different evidence from someone who mainly summarizes reports or plans travel. Include the consequences of a wrong answer, the files or services the assistant must access, and how much review time you can spare.
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Coding tasks to put on the list
- Explain an unfamiliar function and identify assumptions it makes.
- Find the cause of a reproducible bug in a small, safe copy of a project.
- Propose a narrowly scoped change with tests and a clear diff.
- Refactor one component without changing its public behavior.
- Read project documentation and produce a setup or migration checklist.
Research tasks to put on the list
- Answer a current question with a specified date range and geography.
- Compare several primary sources and mark disagreements or missing evidence.
- Extract figures from a long document while preserving units and qualifications.
- Produce a brief with links or citations that a colleague can verify.
Everyday tasks to put on the list
- Draft a message in your normal tone and revise it after one instruction.
- Summarize a meeting or article into actions, owners, and deadlines.
- Plan a trip, project, meal schedule, or study session within stated constraints.
- Organize unstructured notes into a list, table, or calendar-ready format.
Choose two or three tasks from each category. Use real patterns, but remove secrets, personal identifiers, confidential customer information, and proprietary code unless your organization’s policy explicitly permits that data.
Run the same controlled trial for every finalist
A useful comparison changes one variable at a time. Prepare a short prompt, the same source files or pasted excerpts, and the same output requirements. Record the assistant, model or mode selected, plan, date, and any tools enabled. If a feature is unavailable to one product, record that as a workflow result rather than silently giving another product extra help.
- Prepare a test pack. Include a sanitized code sample, a reproducible bug report, two or three authoritative documents, and three everyday prompts. State the desired format, audience, date, geography, and constraints.
- Use a fresh conversation where appropriate. This reduces accidental help from earlier messages. If memory is part of your normal workflow, run a second pass with memory enabled and note the difference.
- Give identical instructions. Ask for assumptions, uncertainty, citations where relevant, and a concise final answer. Do not coach one assistant through steps you withhold from another.
- Verify independently. Run code and tests yourself, open cited sources, check calculations, and compare extracted facts with the originals.
- Measure effort and friction. Note time to a usable result, number of follow-up prompts, manual edits, failed tool calls, context limits, and whether the result can be copied into your existing workflow.
- Repeat high-impact tasks. One lucky answer is not a reliable pattern. Run a task again with a slightly different input or a second issue from the same project.
Use a scorecard that reflects your priorities
Score each task on a simple 1–5 scale and keep comments beside the number. A five means the result met your requirement with little checking; a one means it was unusable or unsafe. Do not average everything into a universal winner unless you first explain whose priorities the weighting represents.
| Dimension | What to record | Questions to ask |
|---|---|---|
| Correctness | Facts, code behavior, calculations, and constraints | Did it solve the stated problem without introducing a new one? |
| Completeness | Required steps, edge cases, citations, and requested format | What did you still have to add? |
| Verification burden | Minutes spent checking and editing | Could you identify and fix errors confidently? |
| Workflow fit | Editor, files, browser, mobile, export, and collaboration support | Does it work where the task already happens? |
| Reliability | Consistency across repeated runs and long inputs | Does quality collapse with context or tool use? |
| Speed and limits | Response time, quotas, context windows, and throttling | Will the plan survive your busiest week? |
| Privacy and administration | Training controls, retention, connectors, and admin policy | Can you use it legally and safely with your data? |
After scoring, apply your own weighting. A production engineer might prioritize test results and repository context; a journalist might prioritize source support and date accuracy; a family user might value accessibility, device coverage, and predictable limits. Keep raw scores visible so a high score in an unimportant category cannot hide a failure in a critical one.
How to compare coding assistance
Test explanation before generation
Paste a function or module and ask for its purpose, inputs, side effects, dependencies, and likely failure cases. Compare whether each assistant accurately follows the code rather than merely producing plausible prose. Ask it to cite exact line references or symbols, then check those references yourself.
Rank #2
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Use a reproducible bug
Provide the smallest safe project that still fails, the command that reproduces the failure, expected behavior, and actual output. A strong result identifies a testable cause, proposes a limited fix, and explains why the change addresses the failure. Run the existing test suite and add a regression test when appropriate. Never merge generated edits without review; an assistant can make a confident change that passes a narrow test while breaking an unmentioned contract.
Measure project-context handling
Ask each tool to make the same small change in a test branch. Record which files it can inspect, whether it understands project conventions, how it handles unstated dependencies, and whether it shows a reviewable diff. An editor extension that can navigate symbols and apply a patch may be more useful than a stronger standalone model that requires repeated copy-and-paste.
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Evaluate agentic behavior separately
Long-running tasks deserve their own trial. Define a stopping condition, permitted commands, and a disposable workspace. Check every file change, dependency installation, network request, and command result. Score recovery from a failed command and the clarity of the final summary, not just whether the agent eventually reaches a result.
How to compare research and fact-finding
Separate quick lookup from deep synthesis
The OpenAI Help Center states: “Use search for quick facts, and use deep research for depth and thoroughness.” Standard search is intended to return current web information and links. Deep-research modes are described as slower processes that read and synthesize many sources into a documented report. The choice is about the job: a single current figure may need a quick source check, while a policy comparison may require many documents and an audit trail.
Access to these modes can depend on plan and country. Connected applications can also depend on workspace settings, permissions, and whether a particular app is supported. Test the account you will actually use instead of assuming that a feature shown on a product page is included in every plan.
Rank #3
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Score claim-to-source support
Give every finalist the same question, cutoff date, geography, and requirement to cite primary sources. Open each important citation. Check that it supports the exact claim, not merely a related topic; that the publication date fits your cutoff; and that the assistant has not merged facts from different jurisdictions or versions. Record uncited assertions and statements that should have been qualified.
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Ask what is unknown, disputed, or likely to change. Then change one fact in the prompt, such as the date range or country, and see whether the answer updates cleanly. A useful assistant distinguishes an established fact from an inference and tells you when a source is unavailable. OpenAI’s reliability guidance warns that confidence is not a guarantee and recommends checking important quotes, data, technical details, and external references.
How to compare everyday usefulness
Everyday quality is more than fluency. Give each assistant a recurring task with constraints, such as drafting a concise message for a specific audience, turning notes into an action list, or planning an activity around budget and time. Test one revision request, such as “make this shorter without losing the deadline” or “keep the same tone but remove jargon.”
Score instruction following, usefulness after editing, speed, accessibility, device fit, and available tools. Check whether formatting survives export to the application where you will use it. Test mobile and desktop if you switch between them. A slightly less polished first draft may win if it reliably remembers your requested structure and needs fewer corrective prompts.
Compare integrations, limits, and administration
Make a capability inventory for each shortlisted plan. Include editor extensions, repository access, file upload types and sizes, browser or search modes, spreadsheet or document connectors, APIs, export options, collaboration controls, and support for your operating systems. Distinguish a feature that exists in the product from one enabled for your account.
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| Check | Why it changes the decision |
|---|---|
| Context and file limits | Large codebases or document sets may require repeated uploads or manual chunking. |
| Usage quotas and throttling | A low advertised price can become impractical during intensive work. |
| Tool permissions | Reading a file, editing it, executing commands, and sending data externally are different risks. |
| Workspace controls | Administrators may restrict connectors, retention, model choices, or sharing. |
| Regional availability | A feature or model may not be offered in your country or on your account. |
| Export and portability | You should be able to retain prompts, outputs, citations, and project notes. |
Price the plan you will actually buy, including taxes, seat minimums, overage rules, and whether a higher tier is required for the feature that matters. A free trial with generous limits may not represent the paid plan’s normal behavior, so record the date and conditions of your trial.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare privacy by account and setting
Do not reduce privacy to a single label such as “private.” Read the controls for the exact personal or organizational account, then verify them in the interface. Compare whether new conversations can be excluded from model training, how long chats are retained, whether deleted data is removed on a schedule, what memory stores, and which connectors can access your content.
OpenAI documents a setting that stops new personal conversations from being used to train its models; those conversations can still appear in chat history. It also says Business, Enterprise, Edu, and Healthcare workspace data is not used for training by default. Anthropic says Incognito chats are not used to improve Claude even when Model Improvement is enabled, while describing other possible handling of chats and feedback. These statements apply to the documented account types and settings; confirm current terms and your administrator’s policy before uploading sensitive material.
- Check training-use controls and whether they apply to old conversations or only new ones.
- Review retention, deletion, memory, and human-review language.
- List every connector and the data it can read, write, or transmit.
- Confirm administrator visibility and audit controls in a work account.
- Use the least sensitive test data possible and remove secrets from prompts.
What benchmarks can—and cannot—tell you
Benchmarks are useful for spotting a technical signal, but they are not a buying verdict. OpenAI reported the following Terminal-Bench 4.0 results in 2026:
| Model | Vendor-reported score | What the number represents |
|---|---|---|
| GPT-6 Astra | 57.9% | Terminal-Bench 4.0 result reported by OpenAI |
| Claude Fable 5.1 | 55.8% | Terminal-Bench 4.0 result reported by OpenAI |
| Gemini 3.8 Flash | 19.1% | Terminal-Bench 4.0 result reported by OpenAI |
OpenAI notes that its evaluations ran in a research environment or through its API and may differ from production ChatGPT because prompts and available tools can differ. The table covers one vendor’s selected models on one coding benchmark. It does not measure citation quality, everyday usefulness, privacy, plan limits, editor integration, or your repository. Do not extrapolate it to all coding tasks, and do not treat it as an independent consumer ranking.
A practical comparison workflow
Phase 1: shortlist
Choose two to four assistants that are available in your region and support your essential editor, devices, and connectors. Eliminate any option that fails a non-negotiable privacy or administration requirement before spending time on quality scoring.
Phase 2: run a one-hour smoke test
- Run one coding explanation and one bug task in a disposable project.
- Run one current research question with primary-source citation requirements.
- Run one everyday drafting or planning task followed by a revision.
- Record response time, follow-up prompts, citations, edits, and tool friction.
The smoke test identifies obvious mismatches. It is not enough for a high-risk purchase or a team rollout.
Phase 3: stress the finalists
Repeat the highest-value tasks with longer context, a second input, and the tools you rely on. Test a failure case: an unavailable source, a malformed file, a failing command, or an ambiguous requirement. Observe whether the assistant asks a useful clarification, states a limitation, or improvises an unsafe answer.
Phase 4: price and govern
Calculate expected monthly usage for the exact plan. Read current limits, retention terms, connector permissions, and cancellation rules. For a team, have the administrator test policy controls and audit visibility before inviting real data.
Common comparison mistakes
- Choosing from a leaderboard alone: a benchmark score cannot reveal editor support, source quality, or account availability.
- Changing the prompt for each product: different instructions make the scores incomparable. Keep a shared baseline, then run a separate optimization pass.
- Counting fluent text as accuracy: verify code, figures, quotations, and links independently.
- Ignoring the application layer: a model may be capable while the product lacks your connector, file type, or device support.
- Testing only easy tasks: include a reproducible failure and an ambiguous request to expose recovery behavior.
- Uploading sensitive data during trials: sanitize examples and confirm policy before using confidential material.
- Assuming features are permanent: model access, limits, privacy terms, and regional availability change. Recheck the vendor’s live documentation before subscribing.
Choose by profile, not by a universal winner
| Your priority | Evidence to emphasize | Decision signal |
|---|---|---|
| Production coding | Tests, safe diffs, repository context, IDE integration, agent recovery | Lowest review and rollback burden on your real project |
| Evidence-heavy research | Recency, primary-source coverage, claim support, uncertainty, exportable citations | Fastest path to a report you can audit |
| Everyday productivity | Instruction following, revision quality, speed, accessibility, device fit | Fewest corrective prompts for recurring tasks |
| Sensitive organizational work | Training controls, retention, connectors, admin policy, auditability | Meets governance requirements before quality is considered |
| Budget-constrained use | Free limits, paid quotas, model access, overage and seat rules | Predictable cost at your actual usage level |
If two assistants perform similarly, prefer the one that fits your existing workflow, exposes clearer controls, and leaves you with less verification and editing. If one wins only on a benchmark but loses your real tasks, the task result is the more relevant evidence.
Recheck volatile details before you pay
Model lineups, plan access, quotas, connectors, privacy language, and regional support can change after you test. Revisit the provider’s current product, help, and privacy pages on the day you subscribe. Save the plan name, account type, enabled model, and relevant policy version in your comparison notes. That record makes it possible to explain a later change instead of mistaking a product update for inconsistent testing.
The decision rule
There is no universal winner established by these comparisons. Select the assistant that delivers the best verified results on your recurring tasks while meeting your privacy, workflow, availability, and cost requirements. Keep a second option documented for outages, quota limits, or a task that benefits from a different tool. Re-run a small version of the test whenever your project, plan, or data policy changes.
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