Stop guessing which model fits your task. Measure it.

Your prompt, your data, scored against every major model simultaneously. Quality, cost, and latency in one run.

Quality

Which model actually followed the instruction?

Same task, same constraint. Qwen cut output from 98 to 53 tokens. Grok went the other way. Instruction-following varies as much as quality scores, and it shows up directly in your bill.

output tokens · 'summarise in exactly 2 sentences' · live run

Qwen 3.8 27B

98->53

Claude S 4.5

90->65

GPT-4.1 Nano

52->47

Grok 4.5

49->54

output tokens vague->specific Qwen: -46% tokens on constraint

Cost

A 53x cost spread on the exact same task.

At 100 calls/day, Grok 4.5 costs $4.45/month. GPT-4.1 Nano costs $0.08. Both ran the same support ticket prompt. At 1,000 calls/day that gap is $43/month.

monthly cost · 100 calls/day · live run data

Grok 4.5

$4.45

Claude S 4.5

$3.79

Qwen 3.8 27B

$0.87

DeepSeek V4

$0.39

GPT-4.1 Nano

$0.08

monthly cost at 100 calls/day 53x spread · same prompt

Prompt A/B

One constraint. Qwen cut costs 37%. Grok got more expensive.

Adding 'exactly two sentences' to a support ticket summary prompt changed the cost profile across every model differently. The same prompt change has a different effect on every model.

cost delta · vague vs constrained prompt · live run

Qwen 3.8 27B

cost -37%

Claude S 4.5

cost -20%

GPT-4.1 Nano

cost -1% (flat)

DeepSeek V4

cost flat (reasoning model)

Grok 4.5

cost +4.5%

same task · one constraint added · different cost reaction from every model

prompt A/B on compare Starter plan

Qwen cut output 46% when given a constraint. Grok produced more.

Starter

Public leaderboards test on their data. This runs on yours. Instruction-following, cost, and latency all shift depending on what you ask. The model that tops a published benchmark may not be the right model for your specific task. See the full model registry. Current per-provider rates are on the AI API pricing pages.

Output tokens · vague vs specific prompt

Your fine-tune beat GPT-4.1 by 4 points at 84% lower cost.

Pro

Register any OpenAI-compatible endpoint and compare it against the field. Fine-tuned model, self-hosted deployment, or full RAG pipeline. Same prompt, same scoring. You own the workflow. LLM Prover owns the measurement. See the BYOE Guide.

Quality score · fine-tune vs frontier models (domain benchmark)

One constraint. Qwen cut costs 37%. Grok got more expensive.

Starter

Run two prompt variants head-to-head. Adding ’exactly two sentences’ to a support ticket prompt cut Qwen’s cost 37% and left Grok 4.5% more expensive. The same prompt change has a different cost impact on every model. The comparison shows you all of them at once.

Cost delta · vague vs specific prompt

Your system prompt is a per-call tax. Measure whether it's worth it.

Starter

A system prompt added to every call is a permanent cost on every LLM interaction. Most were written once and never measured. On Claude, switching from vague to precise inputs produced a 5.5x cost reduction and scores went from 0 to 100. See the full data in the system prompt audit.

Claude Opus 5 · quality score by input precision

6 of 7 findings pointed to the pipeline. Not the model.

Pro

Every run includes a pipeline diagnostic. It reads all seven layers (prompt, context, system prompt, parameters, criteria, scoring, model) and returns a specific finding with a specific fix. Most teams have been optimising the wrong layer. See the Scoring Guide.

Diagnostic findings by pipeline stage · compliance benchmark, 7 runs

Does your knowledge base actually improve outputs? Now you can measure it.

Pro

Upload your documents as a context store. Every comparison run injects the most relevant chunks automatically. A/B test two store versions to measure whether improving your content improves your outputs. See the RAG Store Guide.

Quality score · store A/B test (same model, same prompt)

Where to start

Manual comparisons on all plans. The 53x cost spread and prompt A/B data above came from a Starter plan run. Context stores, system prompt testing, pipeline diagnostics, and BYOE start at Pro.

	Starter $49/mo	Pro $129/mo	Pro+ $199/mo	Enterprise $499/mo
Comparisons per day	25	100	250	500
Models per comparison	5	8	10	20
Inline context	✓	✓	✓	✓
Context stores (vector search)	--	✓	✓	✓
System prompts	1	10	20	unlimited
BYOE endpoints	--	1	5	10
History	90 days	365 days	365 days	unlimited

Run your first comparison in minutes.

Your prompt, every major model, quality and cost side by side.