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Data driven insights and analysis that help turn your AI spend into AI returns.

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Quality
Which model scores highest on your task?

Consistent across two runs. The top models hold their positions.

support ticket summary · 5 models, 2 runs
Claude Opus 5
91.7
Grok 4.5
88.5
Qwen 3.8 27B
88.2
GPT-4.1 Nano
70.7
GPT-4o Mini
70.5
quality score (0-100) Claude wins · 3.2pts clear of the field
Cost
What does that quality difference cost at scale?

At 100 calls/day, the spread is $27/month. At 1,000 calls/day, it is $270.

cost projection · 100 calls/day
Claude Opus 5
$27.35
Grok 4.5
$6.01
Qwen 3.8 27B
$2.38
GPT-4o Mini
$0.30
monthly cost at 100 calls/day Qwen: 88.2 quality for $2.38/mo
Reliability
Which models clear your bar consistently?

GPT-4o Mini looks like the obvious choice on cost. Twelve runs later, it passes half the time.

pass rate · quality threshold 95 · 12 runs
Kimi K3
12/12
DeepSeek V4 Flash
10/12
GPT-4o Mini
6/12
threshold: quality >= 95 Kimi K3 · never misses
Compliance
A score of 75 is not the answer. It is the starting point.

Per-criterion scoring shows exactly where the failure is. The diagnostic tells you which pipeline layer caused it.

compliance score per criterion · GPT-4o Mini
Availability
0
Fabrication
100
Pricing
100
Superlatives
100
weighted total: 75 (4 criteria, equal weights) Availability clause failed

Want to try the idea before signing up? A set of free AI tools lets you compare models, test context, check consistency, and size up cost on your own prompts with your own keys, no account needed. The paid features below pick up where those leave off.

Agent evaluation: see where a workflow wasted money, time or effort

Pro
Model evaluation is the unit test: it checks one step, a single call to one model. Agent evaluation is the integration test: an agent analysis reads the trace of a whole workflow (the step-by-step record of what your agent did) and checks every step. Both run on one engine. Kiro and Claude Code traces are detected automatically.

Credits per step - one real multi-agent workflow, agent analysis of 4 Nov 2026

MCP server: your AI agents can benchmark and monitor other AI models natively.

Pro
The MCP server exposes over 40 tools. Any MCP-capable client (Claude Desktop, Cursor, VS Code Copilot, Amazon Q) discovers them automatically. An agent can run a comparison, read the results, and make a model decision without leaving its workflow. See the MCP docs.
Agent
> run_comparison models=[gpt-4o, gpt-4o-mini, claude-haiku-4-5] prompt="classify this support ticket"
job_id: cmp_x9y8z7
> poll_job job_id="cmp_x9y8z7"
status: complete
> get_comparison comparison_id="cmp_x9y8z7"
claude-haiku-4-5: quality 91.4, cost $0.00041/call, latency 980ms
verdict: route pipeline to claude-haiku-4-5, highest quality under $0.002 threshold

Presets: save a run configuration once, invoke it by name from anywhere.

Pro
Save the models, scoring, rubric, and context behind a run as a named preset, then run it by name from the dashboard, the API, or a plain-language ask to your agent. “Run our marketing-standards check on this blurb” resolves to the saved configuration and returns a verdict. Configure the standard once; from then on it is a sentence.
Agent
> list_presets
marketing-standards (compliance), standard-comparison (performance), brand-voice (quality)
ask "run our marketing-standards check on this blurb" -> match preset marketing-standards
> score artifact="<the blurb>" rubric_id=marketing-standards.rubric compliance_store_id=marketing-standards.store
50/100 | superlatives: fail tier clarity: fail pricing: pass fabrication: pass

Model comparison: run your prompt against every major model simultaneously.

Starter
Public leaderboards test on their data. This runs on yours. See the full model registry.

Quality score · support ticket task

Prompt A/B testing: measure how prompt changes affect cost and quality across every model.

Starter
A/B test two prompts head-to-head. Cost, latency, and response quality shift across every model in the run simultaneously.

Cost and latency change: Prompt B vs Prompt A

Rubric scoring: define your quality criteria once, score every response automatically.

Pro
Write a rubric. The AI judge reads each criterion independently, shows its reasoning, and runs the same way every time. Quality, latency, and cost in one view with bubble size representing cost per call. No reviewer fatigue across 500 runs. See the Rubric Writing Guide.

Quality vs latency vs cost: same prompt, same rubric

Scoring: judge text you already have, no model run, no cost.

Pro
Paste a finished piece of copy, a drafted answer, or a clause, and score it against a rubric or your compliance documents. The judge reads your words and returns a per-criterion verdict. Paste several versions to stack-rank them against one standard. See the scoring launch post.

Compliance score, same copy, before and after one fix (out of 100)

Pipeline diagnostics: find which layer is causing the failure, not just that something failed.

Pro
The diagnostic reads every layer: prompt, context, system prompt, criteria, parameters, scoring, model. It returns a specific fix. Most teams have been optimising the wrong layer.

Diagnostic findings by pipeline stage · compliance benchmark, 7 runs

Scheduled benchmarks: track reliability over time, not just today's score.

Pro
Win rate over time, not just today’s score. A model that passes once might pass half the time, or drift quietly after a provider update. Scheduled tracking tells you which models are genuinely reliable, which are volatile, and which have changed. See the Benchmark Guide.

Pass rate · quality threshold 95 · 12 scheduled runs (RAG pipeline)

Drift monitoring: catch provider-side regressions before your users do.

Pro
Providers update weights without announcing it. Scheduled benchmarks catch the drop before your users do. Alert thresholds fire the moment a run breaches your quality floor. See the Monitoring Guide.

Quality score · 12 scheduled runs (threshold: 95)

RAG context stores: measure whether improving your knowledge base improves your outputs.

Pro
Upload your knowledge base. The most relevant chunks are injected into every run automatically. A/B test two store versions to measure whether improving your documents improves your outputs. See the RAG Store Guide.

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

Compliance scoring: score outputs against your regulations clause by clause.

Pro+
The alert fired before it reached production. Upload your regulations. Score every output against specific clauses. Get alerted the moment a run drops below your threshold. See the Compliance Store Guide.

Compliance score across benchmark runs (illustrative)

Bring Your Own Endpoint (BYOE): benchmark your own models and pipelines against the field.

Pro
Register any OpenAI-compatible endpoint: fine-tuned model, self-hosted deployment, or full pipeline. Benchmark it against the field. Reproducible, exportable evidence. See the BYOE Guide.

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

Inference parameters: lock temperature and seed to get reproducible, auditable scores.

Pro
At temperature 0.7 with no seed, the same prompt produces a 32-point score range across five runs. At temperature 0.1 with a fixed seed, it produces the same score every time. One is a valid measurement, the other a fuzzy indicator.

Score stability: locked parameters vs defaults (5 runs, same prompt)

REST API: fire comparisons, benchmarks, and evaluations from any script or pipeline.

Starter
Fire comparisons, evaluations, and benchmark runs from any script, pipeline, or CI workflow. Same async pattern as the dashboard: POST to start, poll for the job, retrieve the full result. Per-criterion rubric scores come back in the same response. Developer keys on all plans. See the API docs.

Rubric scores per criterion: support reply task

Orgs: one subscription, per-engineer visibility, no shared credentials.

Starter
Each member’s usage draws from their own plan allowance. One invoice, per-engineer visibility, no shared credentials.

Where to start

Manual comparisons on all plans. Rubric scoring, scoring supplied text, diagnostics, scheduled benchmarks, and BYOE start at Pro. Compliance scoring at Pro Plus. REST API on all plans. MCP server and agent analysis on Pro and above.
Starter
$49/mo
Pro
$129/mo
Pro+
$199/mo
Enterprise
$499/mo
Comparisons50/dayunlimitedunlimitedunlimited
Rubric scoring + judge--✓✓✓
Scoring (judge supplied text)--✓✓✓
Saved presets--✓✓✓
Scheduled benchmarks--daily4x dailyhourly
RAG context stores--1 store5 storesunlimited
Compliance scoring----✓✓
BYOE endpoints--1510
Alert thresholds--✓✓✓
REST API access1 key5 keys10 keys50 keys
MCP server--✓✓✓
Agent analysis (agent evaluation)--✓✓✓

Ready to measure your models?

Pick a plan and run your first comparison in minutes.

See pricing