How much does LLM-as-a-judge cost?
Estimate the cost of using an LLM to evaluate generated responses — model comparison, regression testing, benchmark runs, QA scoring, rubric-based grading, pairwise response comparison, and production output quality review. Enter your evaluation volume, how many candidate responses each evaluation compares, and rubric complexity to get a Low / Likely / High monthly cost estimate. Everything runs locally in your browser — no data is sent to any server.
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Estimated monthly cost
This estimates only the judge model's own inference cost. It does not include the cost of generating the candidate responses being evaluated (priced by whichever calculator matches that workload) or any benchmark/eval-framework hosting or tooling costs.
This is an estimate, not a billing guarantee. Actual costs depend on your provider's exact tokeniser, your real prompts and responses, and your billing tier.
How this estimate works
- Judge rubric/instructions
- The original prompt/context
- One or more candidate responses
Reads the rubric, context, and candidate(s), then produces a verdict.
A score, a structured verdict, or a verdict with a short explanation.
This is deliberately not a chatbot: the candidate response(s) being judged are already- generated text fed in as INPUT, not written fresh as output. This calculator prices the judge's own inference cost only — not the cost of generating the responses being judged.
How much does it cost to evaluate 100,000 AI responses?
At GPT-5.4 Mini's current rates, scoring 100,000 individual responses against a standard rubric (single-response QA scoring, score-only output) comes out to a Likely monthly cost of $56.10 (range $33.30–$85.50). Use the calculator above to model your own volume, candidate count, and model.
Joint vs. independent: how candidates reach the judge
When comparing multiple candidate responses, they can reach the judge two genuinely different ways. Joint: one call sees every candidate together and returns one comparative verdict (the common pattern for pairwise or multi-way comparison — "which response is better"). Independent: one separate call per candidate, each producing its own verdict (the pattern for grading many responses one at a time against a fixed rubric). These have different cost shapes — joint pays the rubric/context overhead once and stacks candidate tokens into one larger call; independent pays that overhead once per call, with only one candidate's tokens each time.
Why more candidates increase judge input
If 2 candidate responses are being compared, both contribute their own tokens to the judge's input — never just one. More candidates, whether joint or independent, means more total tokens the judge has to read.
Why rubric complexity and explanation mode matter
A simple pass/fail rubric costs little to specify. A detailed rubric with scoring anchors and examples adds real input tokens on every call. On the output side, a bare score costs almost nothing; asking the judge to explain its verdict adds meaningfully more output, though still far less than a full written response.
Low / Likely / High explained
A short prompt, short candidate response(s), a minimal rubric, score-only output.
Typical prompt and response lengths, a standard rubric. Plan against this number.
A long prompt, long candidate response(s), a detailed rubric, verdict+explanation output. Budget against this.
Cut your cost
Example monthly LLM evaluation costs
These worked examples use the same default assumptions as the calculator above — pairwise comparison (2 candidates, joint mode), medium-length prompt and responses, a standard rubric, structured-verdict output — with GPT-5.4 Mini. Only the number of evaluations per month changes between rows.
| Usage level | Evaluations / month | Low | Likely | High |
|---|---|---|---|---|
| Light usage | 100 | $0.0555 | $0.0930 | $0.1440 |
| Typical usage | 1,000 | $0.5550 | $0.9300 | $1.44 |
| Heavy usage | 10,000 | $5.55 | $9.30 | $14.40 |
And here's the per-evaluation cost for three common evaluation types — single-response QA scoring, pairwise model comparison, and a large multi-way evaluation run — with GPT-5.4 Mini, each with its own candidate count and output mode:
| Evaluation type | Low | Likely | High |
|---|---|---|---|
| Single-response QA scoring (10K responses) | $0.000333 | $0.000561 | $0.000855 |
| Pairwise model comparison | $0.000555 | $0.000930 | $0.001440 |
| Large multi-way evaluation run | $0.001455 | $0.002550 | $0.004162 |
Caveats
- These estimates use the same reference prompt and response-length token ranges as the site's chatbot calculator, accurate to ±10–15% for plain English text.
- Judge-output token figures (score / structured verdict / verdict+explanation) are this project's own descriptive assumption, not a provider-published figure — no provider publishes a token size for a judge verdict, since it depends on the exact rubric and verdict schema.
- Each provider uses a different tokeniser. Actual token counts may differ ±5–10% between providers for the same content.
- This estimates only the judge model's own inference cost. It does not include the cost of generating the candidate responses being evaluated, or any benchmark/eval-framework hosting or tooling costs.
- Prices shown are manually verified against each provider's official pricing page as of 2026-09-08. AI provider pricing changes frequently — verify at your provider's official pricing page before making budget decisions.
- The long-context pricing tier, where a selected model has one, is resolved against each call's own input size, never a monthly aggregate.
- The context-window warning above appears when a call's combined tokens exceed 80% of the selected model's context window — a safety margin before truncation or failure risk, not a hard limit at 100%.
This is an estimate, not a billing guarantee. Always confirm against your provider's usage dashboard and official pricing before committing a budget.