Every prompt change
deserves proof
Run prompt-model combinations against real datasets, score with 60+ evaluation metrics, and rank results with weighted scoring. Deploy the config that actually wins - not the one that looked good on three examples.
| # | Configuration | Accuracy weight: 8 | Fluency weight: 6 | Coherence weight: 5 | Latency weight: 3 | Cost weight: 4 | Weighted Score |
|---|---|---|---|---|---|---|---|
| Claude 3.5 Sonnet WINNER temp: 0.7 · top_p: 0.95 · opener_v3 | 92.4% | 89.1% | 91.7% | 1.8s | $0.0038 | 8.74 | |
| 2 | GPT-4o temp: 0.7 · top_p: 0.95 · opener_v3 | 88.6% | 91.3% | 87.2% | 0.9s | $0.0052 | 8.31 |
| 3 | Gemini 1.5 Pro temp: 0.7 · top_p: 0.95 · opener_v3 | 85.2% | 86.7% | 84.9% | 1.2s | $0.0019 | 7.68 |
| 4 | GPT-4o-mini temp: 0.5 · top_p: 0.9 · opener_v2 | 71.8% | 83.4% | 69.1% | 0.4s | $0.0004 | 6.12 |
| 5 | Claude 3.5 Haiku temp: 0.7 · top_p: 0.95 · opener_v3 | 79.3% | 85.8% | 80.4% | 0.6s | $0.0008 | 7.24 |
Not just comparison -
ranked, weighted decisions
Stop testing prompts on cherry-picked examples. Run experiments against versioned datasets with hundreds of real inputs and expected outputs. Every experiment is reproducible - same dataset version, same prompt template, same model config.
Run your first experimentScore every experiment run on accuracy, fluency, coherence, factual correctness, context adherence, prompt perplexity, and more. Use built-in templates, LLM-as-a-judge evaluators, or define custom metrics that match your business logic.
Explore evaluation metricsRank prompt-model combinations across evaluation scores, response time, token usage, and cost - all in one view. Assign custom weights (0–10) to each metric so the ranking reflects what matters to your use case, not just a single number.
See how ranking worksRun experiments across LLMs, text-to-speech, speech-to-text, image generation, and custom models. Same structured workflow, same evaluation framework - whether you're testing a chatbot, a voice agent, or an image pipeline.
See supported model types Every decision backed
by your data
Find the best model for your task
Run the same prompt template across GPT-4o, Claude, Gemini, Llama, and more. Compare accuracy, latency, and cost in one ranked view - pick the winner backed by data, not a blog post.
A/B test prompt variations
Create a base column with your current prompt output, then run new prompt variants against the same dataset. See exactly which rows improved, which regressed, and by how much.
Optimize cost vs quality tradeoff
Weight cost high and find the cheapest model that still passes your quality bar. Weight accuracy high and find the best performer regardless of cost. The ranking adjusts to your priorities.
Regression-test every PR
Run experiments in CI/CD on every code change. If a prompt edit drops accuracy below your threshold, the PR is blocked. No more shipping regressions you catch after users complain.
Validate voice and image pipelines
Not just LLMs. Run experiments on text-to-speech, speech-to-text, and image generation models with the same structured workflow and scoring framework.
Benchmark new model releases
GPT-5 just dropped? Run your existing experiment suite against it in minutes. See how it stacks up on your actual data - not generic benchmarks.
From hypothesis to
production in three steps
Pick your dataset and configs
Select a versioned dataset, choose the prompt templates and model configurations you want to compare, and attach evaluation metrics - accuracy, fluency, cost, or your own custom scorers.
Run, score, rank
Every prompt-model combination runs against your full dataset. 60+ evaluation metrics score each response automatically. Results are ranked with weighted scoring so you see the winner instantly.
Ship the winner
Choose the top-ranked configuration and promote it to production. Set up CI/CD gates so future changes are automatically tested against your experiment baseline - regressions get blocked, improvements ship.
Powering teams from
prototype to production
From ambitious startups to global enterprises, teams trust Future AGI to ship AI agents confidently.