Closed Beta • Concierge pilot running by invite

Make LLMs useful.
Measure what matters.
Optimize what works.

The workbench for prompt quality. Paste a prompt and our concierge analyses it, builds a test plan, and compares the latest models — scored answer by answer, in minutes. Optimise against real outcomes, with the data to prove what ships. Our public model pages add licensed benchmark evidence and transparent task-specific interpretations.

Join the waitlist Explore the benchmarks
54
model families compared
3
published task rankings
6
attributed data sources

Explore all 3 published task rankings across 54 model families →

EQ-Bench 3 by Samuel Paech / EQ-bench · source snapshot fetched 2026-07-16· Creative Writing v3 by Samuel Paech / EQ-bench · source snapshot fetched 2026-07-16· Contains data from the Arena Leaderboard Dataset by Arena, licensed under CC BY 4.0 · source snapshot fetched 2026-07-16· UGI Leaderboard by DontPlanToEnd, via Hugging Face Spaces · source snapshot fetched 2026-07-16· Operational price and performance data sourced from Artificial Analysis· Structured Output Benchmark (SOB) by Interfaze / JigsawStack, Inc. (MIT License) See data sources & scoring methodology.

Without Spring Prompt

Your Prompt
Plan month {{month_index}} for Northstar Skin. Choose channels, allocate budget, target the right audiences, and write creative angles that grow revenue without wrecking CAC.
Feedback
PE
Prompt Engineer 2:34 PM

Hey, can someone review this upgrade email output before I ship it? 👀

CEO
CEO
CEO 2:41 PM

Looks fine? Maybe too broad? Search feels risky. Hard to tell. Let's run it and hope for the best.

😕 🤷
❌ No way to measure ❌ Subjective feedback ❌ Ship and pray

With Spring Prompt

Define Evals ✓
Simulate ✓
Optimize ✓

Watch the magic happen...

Audience Fit Right personas, right channels
Budget Discipline Avoid waste and resets
Creative Specificity Angles that real shoppers trust
Benchmarking prompt Running evals... Complete
Plan month {{month_index}} for Northstar Skin across Meta, Search, Shopping, CRM...
Audience Fit
Budget Discipline
Creative Specificity
Overall Score
5.2/10
Rewriting... Prompt changes Iteration /5
prompt.txt + −
Analyzing & rewriting...
Score Progress
/10
10 0
12345
Evaluations Scoring...
Audience Fit
Budget Discipline
Creative Specificity
Overall
/10
Optimized Prompt +77% improvement
Plan month {{month_index}} for Northstar Skin.
Allocate budget across {{channels}} with explicit guardrails.
Match {{personas}} to channel + creative angle, then explain tradeoffs.
Avoid learning resets unless the prior month clearly failed.
Use prior metrics from {{history_summary}} instead of restarting from zero.
Audience Fit
9.2
Budget Discipline
8.8
Creative Specificity
9.5
Overall Score
9.2/10
✓ Measurable ✓ Auto-optimized ✓ 5 iterations

Stop guessing. Start measuring usefulness.

Spring Prompt gives you the loop: define what good looks like, measure against real scenarios, and improve the prompt against real outcomes.

Define "Useful"

Create evals that match the behavior and outcomes you actually care about

See Benchmark Pack Results

Explore the benchmark packs we maintain to see how models behave with tradeoffs, memory, and feedback

Optimize Prompts

Rewrite prompts against your benchmark instead of tweaking blindly

Read Published Findings

Get clear writeups on model releases and benchmark results that actually matter

Join the Waitlist

Early access launching soon

Writing fit, right now

A broader evidence base, translated into tasks.

This overall standing combines attributed external signals for the two published predictive Writing tasks. Structured Output is published separately as a direct benchmark and does not enter this overall score.

Business Writing GPT 5.6 Sol
Email Writing Claude Opus 4.8
Full leaderboard → Live evals →
# Model Writing fit Output tok/s
#1 GPT 5.5OpenAI 97 66
#2 GPT 5.6 SolOpenAI 95 56
#3 Claude Opus 4.8Anthropic 95 55
#4 Claude Fable 5Anthropic 94 65
#5 GPT 5.4OpenAI 86 150

Tie-aware standing across 2 predictive Writing tasks · Structured Output's direct score is excluded · price and throughput do not affect fit

A Better Loop For LLM Work

The workflow is simple: measure usefulness, compare models, improve prompts, and learn from the results.

Custom Evals

Define the behaviors you care about, from formatting and correctness to planning quality and audience fit.

Model Comparison

Run the same task across multiple models and see who actually performs best under the same constraints.

Data-Driven Insights

Track score, ROI, repeat rate, failure modes, and the shape of model behavior over time.

Simulation Benchmarks

Use benchmark packs like ROASBench to test models inside realistic worlds with state, memory, and consequences.

Test Data Management

Organize the scenarios, examples, and benchmark inputs you need to make comparisons consistent and repeatable.

Optimization Engine

Use eval feedback to rewrite prompts and iterate toward prompts that actually outperform the baseline.

How It Works

One workflow for measuring and improving real usefulness

1

Paste Your Prompt

Drop in a prompt from your real work — the concierge analyses it and builds a test plan — or start from a benchmark pack like ROASBench.

2

Define Evaluations

Set the criteria that define success for your use case, from format to long-horizon judgment.

3

Run Across Models

Compare models inside the same evaluation loop — a per-prompt scoring matrix shows which model wins where, and what it costs.

✓

Optimize What Works

Improve prompts against the benchmark, publish findings, and ship with evidence.

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Public Evals

We are building a public library of benchmark packs so people can see which models are genuinely useful, not just polished in demos.

Live now

ROASBench

A 12-month DTC growth simulation that tests channel strategy, targeting, ad copy, iteration quality, and long-horizon judgment.

Professional Packs

Cold Outreach, Data Analyst, Launch Week, Conversion Doctor, Content Engine, and more are queued up next.

Personal Packs

Fridge Roulette, Dropped In, Life Admin, Learn Anything, and Fitness Architect are coming soon.
Browse Evals

Frequently Asked Questions

Everything you need to know

We test both prompt workflows and richer benchmark packs. That ranges from custom evals on your own tasks to simulation-style benchmarks like ROASBench, where a model has to plan, adapt, and make tradeoffs over time.

Each benchmark starts from a seeded world with rules, personas, budgets, and constraints. The model makes decisions, we simulate what happens next, and that updated state becomes the context for the next round.

We store structured state, not just a blob of chat history. Things like budget remaining, audience size, channel memory, brand momentum, offer fatigue, and prior month results all persist and get summarized into working memory for the next decision.

Most tools stop at side-by-side outputs. Spring Prompt is built around usefulness: create evals, run benchmark packs, compare models, and optimize prompts against measurable outcomes instead of taste alone.

Yes. Some packs are being designed so selected human participants can run through the exact same environment, which gives us a useful human benchmark alongside model results.

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Expert insights on AI prompt engineering, optimization techniques, and best practices.

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