An AI prompt checker is a tool that evaluates your prompt for structural quality before you send it to a language model. The best ones score each dimension separately, clarity, specificity, structure, robustness, and point to the specific phrase causing the problem, not just the category.
Across 1,018 prompts in PromptEval's State of Prompt Quality benchmark (CC BY 4.0), the average score is 54 out of 100, and only 10.5% clear the 75-point bar that marks a prompt ready for production. Most fall short on obvious, fixable problems: no output format specified, role instructions mixed with variable inputs, vague constraints that mean different things to different models.
A prompt that scored 18 on the first run reached 79 after two focused revision cycles. Same model, same task, just the structural problems fixed. That gap is what a systematic checker makes possible. This guide compares the six strongest AI prompt checkers available in 2026: what each one measures, whether it checks against each provider's official guide, and which fits your actual workflow.
What separates a useful prompt checker from a one-time tool
Most prompt checkers surface the same feedback: "this prompt is vague" or "add more context." What they rarely do is tell you which sentence is vague, why that vagueness causes model drift, or how to fix it without disrupting the rest of the prompt.
Four criteria determine whether a prompt checker becomes part of your workflow or gets used once:
- Numerical scores per dimension. A single overall grade doesn't tell you where to start. Separate scores for clarity, specificity, structure, and robustness tell you exactly which dimension to target first.
- Specific callouts, not categories. "Your prompt lacks context" is not actionable. "Line 3 uses 'professional tone' without defining what that means for this model" is.
- Production-ready features. If you're shipping prompts in a product, you need version history, iteration tracking, and A/B comparison, not a grade on a single attempt.
- A free tier that actually evaluates. Some tools require a paid account to see full results. Those aren't free checkers, they're demos with a paywall.
For a deeper look at what each dimension measures and the research behind them, this breakdown of prompt evaluation metrics covers the methodology in detail.
Six AI prompt checkers compared, 2026
The table below covers checkers designed specifically for prompt quality assessment. Not full LLM observability platforms like LangSmith or Braintrust, which require SDK setup and target engineering teams managing production traces. Those tools solve a different problem. For a comparison that includes both categories, the full prompt evaluation guide covers the complete spectrum.
| Tool | Score scale | Dimensions | Playground | A/B testing | Free tier |
|---|---|---|---|---|---|
| PromptEval | 0–100 per dimension | 4 named (clarity, specificity, structure, robustness) | ✓ BYOK | ✓ Batch A/B | 3 evals/month |
| Indexly | 1–10 overall | Framework validation (CRISPE / CO-STAR) | No | No | Daily limit |
| Feedough | None | 5 qualitative (ambiguity, context, specificity, logic, clarity) | No | No | Unlimited |
| AI Prompt Checker | 0–100 overall | 7 (includes tone and context) | No | No | Unclear |
| Parix.ai | 0–100 overall | 8 (length, role, format, constraints, examples…) | No | No | Unlimited, in-browser |
| SpacePrompts | 0–10 overall | Not disclosed | No | No | Yes |
One column no checker in this table scores is the one that decides whether your prompt behaves the same on GPT and on Claude: conformance to each provider's own prompting guide. More on that below, since it is where most of these tools quietly agree to skip the hard part.
Tool breakdown
1. PromptEval
PromptEval scores prompts 0–100 across four named dimensions: clarity, specificity, structure, and robustness. Each dimension gets its own score, not a single average, which means a prompt can score 92 on clarity and 40 on robustness at the same time. That split tells you exactly which dimension to fix in the next revision, rather than chasing a composite number that hides the actual problem. You can try it as a free prompt checker in the browser, no signup needed for the first check.
The data from the platform illustrates why dimensional scoring matters: the current top-ranked prompt on PromptEval's leaderboard, a B2B sales agent by gabriel.eng. Scores 87 overall, with clarity at 92 and specificity at 78. That 14-point gap between the two strongest dimensions shows that even well-rated production prompts have specific weak spots that a single overall score would obscure.
Beyond evaluation, PromptEval includes features the other tools on this list don't offer: a Playground for live testing with your own Anthropic or OpenAI API key, Batch A/B Test for comparing two prompt variants across up to 10 test inputs with a radar chart output, a version library with diffs, and a production iteration tool for surgical edits based on observed behavior. A Daily Challenge, a daily prompt engineering puzzle with a public leaderboard, is separate from the evaluation workflow but in the same product.
The free tier covers 3 credits per month, no credit card, plus API lint 10 calls/month (BYOK unlimited). Basic ($9/month) adds the improved prompt output, iterator, and playground with 30 credits/month. Pro ($19/month) removes all limits on evaluation, iteration, and playground use, and adds a slug API for serving the current production-tagged prompt from application code, plus the CI regression gate and GitHub Action. Team ($49/month) adds workspaces with roles, an approval workflow before any version goes to production, audit log, and export JSON/CSV.
What it doesn't do: PromptEval evaluates prompt structure, not runtime behavior. It won't tell you that your prompt returns a hallucinated date on 3% of real user requests. That requires a tracing tool like Langfuse or Helicone running on live traffic. For how structural evaluation fits into a full testing workflow, this guide on testing and iterating AI prompts covers both layers.
Best for: Developers and product teams who need fast structural quality gates before shipping and want batch A/B testing or live playground testing without setting up a CLI or SDK.
2. Indexly
Indexly validates prompts against established frameworks. CRISPE (Context, Role, Instruction, Style, Personality, Example) and CO-STAR. The feedback shows which framework elements are present and which are missing, flags hallucination-prone vague terms, and detects undefined template variables like {{name}}.
The output includes a rewritten "Enhanced Version" with gaps filled in. That's useful for learning prompt structure. The limitation is that it gives an overall 1–10 score, not separate scores per dimension, so you can't track whether a specific revision improved clarity while keeping specificity stable. There's no version history, no A/B testing, and no playground.
The free tier is limited to one check per day. Paid pricing isn't listed publicly.
Best for: Writers or marketers learning CRISPE or CO-STAR who want a framework-based template check. Not for teams iterating on production prompts where progress needs to be measurable.
3. Feedough
Feedough's checker is qualitative. It analyzes five dimensions (ambiguity, context adequacy, specificity, logical flow, clarity) and returns written feedback without assigning a score. The output reads like a brief editorial review: what's missing, what works, what to rewrite.
The free tier is genuinely unlimited, which is uncommon. The trade-off: without a score, you can't track improvement across iterations. Revise the prompt, run it again, and you get another written assessment, not a number that moved from 48 to 71. For teams that need to demonstrate improvement objectively, that's a structural problem.
Best for: Casual users who want written qualitative feedback without signing up. Not suitable for teams that need to measure and compare prompt versions.
4. AI Prompt Checker (aipromptchecker.com)
This tool evaluates across 7 dimensions, including tone and context. Two criteria that neither PromptEval nor Indexly score explicitly. The output is a 0–100 score. Free tier availability wasn't clearly documented at time of testing.
The 7-dimension approach covers more criteria than PromptEval's 4, but the scores aren't broken down per dimension. You see an overall number, which makes it harder to identify which specific dimension dropped after a revision. No playground, A/B testing, version history, or iteration tools.
Best for: Users who specifically need tone and context scored as named criteria. Not suited for production workflow integration.
5. Parix.ai
Parix.ai is the strongest of the new free entrants. It scores a prompt 0–100 across eight weighted criteria (length, role definition, specificity, output format, audience, constraints, examples, clear directive), returns a rewritten higher-scoring version, and runs entirely in your browser, so the prompt never leaves your device. No signup, no daily cap. For a fast privacy-safe gut check, it is genuinely good.
Two limits keep it a checker rather than a workflow. It reports one composite score, not a per-dimension breakdown, so you cannot see that a revision lifted structure while dropping robustness. And its own docs describe the scoring as "universal prompt-quality principles that apply to every major LLM," which is the crux: it does not check your prompt against what OpenAI, Anthropic, or Google actually publish for their models. That is fine for a first pass and misleading if you are shipping the same prompt to two providers.
Best for: Quick, private, no-signup checks where a single score and an auto-rewrite are enough. Not for tracking measurable improvement across versions or for provider-specific tuning.
6. SpacePrompts
SpacePrompts gives a 0–10 score with a short written assessment of what to improve. No signup required, no dimensions disclosed, no configuration. The product is its speed. You paste a prompt and get a number and a few sentences in seconds.
That simplicity works for a one-time sanity check. It won't help you understand which structural dimension dropped, compare two prompt versions, or build a systematic improvement workflow.
Best for: One-time checks on non-production prompts where a quick gut-check score is enough.
The one check almost none of them run: your provider's official guide
Search for a prompt checker today and half the results promise to grade prompts "for ChatGPT, Claude and Gemini." Read the fine print and they grade against generic best practices, then relabel the output per model. A prompt checker that is provider-aware verifies your prompt against the rules each vendor actually publishes, because those rules disagree with each other in ways that change model behavior.
Concrete cases from the current guides: Anthropic recommends wrapping inputs in XML tags for Claude, which OpenAI's guidance does not ask for. OpenAI's GPT-5 guide leans on Markdown sections and explicit reasoning cues. Google's Gemini guidance puts more weight on few-shot examples. A prompt tuned to one of these can score well on a generic checker and still underperform on another provider, and no composite 0–100 tells you which rule you broke.
This is the gap PromptEval's provider compatibility check fills: it lints your prompt against 18 documented rules drawn from the official OpenAI, Anthropic, and Google guides, each rule linked to its source, and shows a per-provider pass/fail. If you only want that specific check, the free Prompt Format Checker runs it in the browser with no signup, the same privacy posture Parix.ai offers, aimed at a harder question. The full breakdown of prompt format across Claude, GPT and Gemini covers why each rule exists.
How to choose based on your situation
Dimensional scoring is a method of measuring prompt quality that assigns separate scores to different aspects, clarity, specificity, structure, and robustness, rather than a single overall rating. It's what lets you see that a prompt improved on structure (from 55 to 78) but regressed on robustness (from 70 to 52) after an edit, which a composite score would hide.
With that in mind, here's the decision:
- Shipping prompts in a product → PromptEval. Version history, batch A/B testing, and an iteration workflow are only available here. The $19/month Pro tier makes sense when a failing prompt in production means user-facing errors you have to debug manually.
- Learning prompt frameworks from scratch → Indexly. CRISPE and CO-STAR feedback teaches the underlying structure. Use it as a learning tool, not a quality gate for production.
- One-off checks, no account → Feedough or SpacePrompts. Feedough gives qualitative written feedback without a signup. SpacePrompts gives a fast score. Neither tracks progress across revisions.
- Need tone and context as explicit scored dimensions → AI Prompt Checker. The only tool on this list that evaluates those as named criteria.
See your prompt's score in under 10 seconds
Several checkers here show a teaser and ask for a card before the real result. PromptEval gives you 3 evaluations free, no credit card: paste a prompt, get a 0–100 score across all 4 dimensions plus the per-provider pass/fail. Want to build the instinct before you run one? The Daily Challenge is a short prompt engineering exercise with a public leaderboard, no signup for today's puzzle.
Frequently Asked Questions
What is an AI prompt checker?
An AI prompt checker is a tool that evaluates your prompt's structural quality before you send it to a language model. It analyzes the text for clarity, specificity, output format definition, and robustness. The structural properties that determine whether a prompt produces consistent, usable results across different inputs and model runs.
Do AI prompt checkers actually improve results?
Yes, when they identify specific structural problems. A prompt that scored 18 on PromptEval reached 79 after two revision cycles targeting the exact callouts. Missing output format, vague role definition, and unspecified quality constraints. Generic feedback without a trackable score doesn't produce measurable improvement because you can't verify whether the revision helped.
What's the difference between a prompt checker and a prompt tester?
A prompt checker evaluates the structure of your prompt before running it against a model. A prompt tester runs your prompt on actual inputs and evaluates the model outputs. Checkers catch structural flaws before you burn API credits. Testers catch behavioral failures that only appear at runtime. Wrong format returned, edge cases missed, hallucinations on specific inputs. Both belong in a complete quality workflow.
Can I use an AI prompt checker without an API key?
Yes. PromptEval, Feedough, Indexly, and SpacePrompts all evaluate prompts through a browser interface without requiring your own API key. PromptEval's Playground feature requires a BYOK key if you want to run the evaluated prompt live on a model, but the structural scoring itself does not.
What prompt dimensions matter most in production?
In production contexts, robustness and specificity cause the most failures. Robustness, how the prompt handles edge cases and unexpected inputs, breaks when a user does something your tests didn't anticipate. Specificity, whether the output format and constraints are precise enough, breaks when the model interprets your instructions differently than intended. Clarity and structure are prerequisites, but robustness and specificity are where production prompts actually fail at scale.
Are there prompt checkers made for ChatGPT, Claude, and Gemini specifically?
Most tools that advertise per-model checking score against generic best practices and relabel the output. Real provider-aware checking verifies the prompt against each vendor's published guide, because the rules differ: Anthropic recommends XML tags for Claude, OpenAI's GPT-5 guide leans on Markdown structure, and Google emphasizes few-shot examples for Gemini. PromptEval's provider compatibility check lints against 18 documented rules pulled from the official OpenAI, Anthropic, and Google guides, each linked to its source, and shows a per-provider pass/fail. The free Prompt Format Checker runs that same check in the browser with no signup.