Find out which model your task should actually be running on.
Most teams overpay by defaulting to a flagship model. Send us your task and we'll benchmark it across all relevant models (up to 30+), then send back the optimal pick for cost, speed, or accuracy.
No call required · Async-only · From $299 for one task across all relevant models.
Picking the wrong model is silent and expensive.
Three traps cost teams a lot of money before anyone notices.
Defaulting to a flagship
On well-defined tasks, smaller or older models often match accuracy at 5 to 20 times lower cost.
Spec sheets and "AI judges" lie
Tokenizers vary, chain-of-thought tokens get billed, and using AI to judge AI is circular. The only honest cost is real dollars per run.
Rankings shift constantly
New flagships ship every quarter. Yesterday's winner can become next month's worst-priced option overnight.
The math is brutal. In a good way.
Switching to the right model usually cuts your bill by an order of magnitude on the same task.
Numbers from a real audit (~15x cheaper post-audit). See the case study.
Three steps. No call needed. 48 hours.
You define the task once. We design the test, run the benchmark, and send a report you can act on.
Send your task
Describe the LLM task and share the prompt plus 5-20 test cases (input + expected output).
What "test cases" means
One fixed prompt that runs on every input. Each test case is one input plus the correct expected output. We score deterministically against those expected outputs. No prompt yet? We'll design one.
We benchmark all relevant models
Real API calls in parallel across providers. We capture accuracy, real cost per run, latency, and stability.
How we score
Deterministically, with 18 modes: exact match, regex, JSON schema, numeric tolerance, set overlap, contains-all, contains-any, word overlap, and more. We pick the modes that fit your output shape. No "LLM-as-a-judge", no subjective taste calls.
You get the report
A synthesized PDF: primary model, fallbacks, cost-at-volume, latency notes, and re-test triggers.
When the audit works, and when it doesn't.
If you can describe what a correct output looks like, the audit can probably help.
Best-fit tasks
- Any task with a clear prompt and a clear expected output
- Image classification & vision tasks
- Document extraction & structured parsing
- Ticket / email routing & intent classification
- Content moderation & policy checks
- RAG answer grading against ground truth
- OCR cleanup, compliance & legal redlines
Poor-fit tasks
- "Which AI is best, generally?" (no defined task)
- Chatbot taste tests & preference comparisons
- Broad coding ability ("is it a good coder?")
- Creative writing style or voice matching
- Long multi-turn assistant memory
- Image generation (DALL-E, Midjourney, etc.)
- Anything you can't define a correct output for
A report you can act on.
A 10-minute read your engineers, founders, and ops leads can all act on, not a raw data dump.
One audit. Or a recurring relationship.
Start with a single audit. Add a retainer later if your model selection needs to keep pace with releases.
Entry audit
One task. All relevant models. Done in 48h.
- One task across all relevant models
- Edge-case planning and dataset design
- Real API calls, not spec-sheet lookups
- Synthesized PDF (primary, fallbacks, cost-at-volume, re-test triggers)
- 48-hour turnaround once intake is complete
- One round of clarifying questions
Monitoring retainer
Stay on the optimal model as the market moves.
- Monthly re-runs of your audited task
- Fresh report when models or pricing change
- Provider-launch alerts for your task
- Direct line for ad-hoc model questions
- Priority on new audits
Final price depends on number of models tested, volume of sample inputs, and edge-case complexity. We confirm before any work starts.
Multiple tasks? Email us for custom engagements.
The same platform we use, for hands-on teams.
If you have an engineer with the time, the OpenMark platform is what we use internally.
A guided agent for quick setup, plus a manual mode for precise control over prompts, datasets, and scoring logic.
- Starter model access
- 2x parallel benchmark workers
- 5 tests per task · 3 active tasks
- 60-day workspace history
- All 100+ models unlocked
- 4x parallel benchmark workers
- 10 tests per task · 30 active tasks
- Advanced AI drafting agent
- Unlimited workspace history
- All 100+ models unlocked
- 12x parallel benchmark workers
- 20 tests per task · 100 active tasks
- Reasoning-tier AI drafting agent
- 5 GB attachment storage
Credit packs from $5 (333 credits). Buying any pack unlocks full model access permanently. Yearly plans save ~17%.
Once you know the right model per step, OpenClaw routes to it automatically.
Open-source companion. Drop in benchmark results from OpenMark and the router serves the optimal model per task at runtime, with deterministic fallbacks when a provider is rate-limited or down.
View on GitHubCommon questions before you ask.
What does the audit actually deliver?
A synthesized PDF with: task summary, dataset and edge-case coverage, models tested, recommended primary model, fallback options, cost projections at your stated volume, latency notes, caveats, and re-test triggers. Deliberately not a raw data dump.
How long does it take?
48 hours from the moment you provide a usable task definition, sample inputs, and pass/fail criteria. If your intake is incomplete, we'll come back with one round of clarifying questions before the clock starts.
Do I need to share production data?
No. Anonymized or synthetic samples that mirror your real edge cases are fine. We just need them to be representative.
Why not just look up $/M tokens and pick the cheapest?
Tokenizers vary across providers, so the same input becomes a different number of tokens depending on the model. A model that looks 20% cheaper per token can quietly cost more once you account for that. Some models also output more chain-of-thought tokens than others, which you pay for. The only honest cost number is the actual dollar cost per run on your prompt, captured from real API calls.
Why not run this on the platform myself?
You can. The platform exists at openmark.ai/ui and starts at $29/month. The audit exists for teams who want the outcome without learning test design and edge-case planning.
Still unsure if your task is a good fit? Email us at support@openmark.ai.
Send your task. Get a model recommendation in 48h.
Whether the task is in production, in design, or just an idea, send what you have. We'll reply within one business day.