Know what changes
before you change models.

Pisama compares replacements against your workflow’s requirements. See what changes in quality, cost and response time before you decide to switch.

See the real model comparison

For one read-only text workflow: answers, classifications or structured decisions.

See the trade-offs.
Then make the call.

Actual model outputs from the public Banking77 benchmark. The report keeps improvements and regressions visible.

Banking77

Banking intent classification

13 October 2026

500 primary cases · 30 separate challenge cases

Default preference among 8 eligible

GPT-6 Luna

43 incumbent errors corrected.
12 new errors introduced.

The lead is narrow. Different priorities can change the preferred model.

Correctness: 50%
Consistency: 20%
Cost: 20%
p95 latency: 10%

4 of 12 tested configurations. 8 eligible for ranking; 4 unranked.
Configuration	Consistency	Errors / 500	Cost / 1k cases	Latency p50 / p95
o4-miniCurrent model	Reference	126	$1.7612	2.07 / 5.87s
GPT-6 LunaDefault preference	84.8%	95	$0.0842	2.52 / 4.08s
GLM-5.3-FlashLowest fully measured cost	84.4%	111	$0.0282	1.32 / 3.03s
Gemini 3.8 FlashFewest primary errors	84.0%	82	$1.0833	2.09 / 4.38s

Consistency measures agreement with the incumbent, including shared mistakes. Errors use the benchmark’s reference labels. Costs are USD per 1,000 cases, including recorded attempts. Latency sums HTTP attempts; collection windows differ, so timings are descriptive.

Public research example. Your assessment covers the scoped model candidates agreed for your workflow. See assessment scope.

The decision, with the evidence behind it.

From the tested configurations to the cases that changed.

Decision report

A recommendation with quality, cost and response-time trade-offs. See what meets your agreed limits and what remains unresolved.

Case evidence

Matching outputs, improvements and regressions, traced to individual cases. Review the differences against your business rules.

Configuration handoff

The exact model, API route and settings, proposed fixes, and a canary and fallback plan for your rollout.

Assisted Migration Assessment

Start with one workflow and the evidence you already have.

Scope, pricing, review effort and inference estimates are agreed before work begins.

Bring your own provider keys. Inference is billed directly by your providers, with no Pisama token markup.

One read-only workflow: Text answers, classifications or structured decisions. Each request can include up to three model calls and one read-only tool. We replay recorded inputs and tool responses.
Up to three candidate configurations: Three combinations of model, API route or settings across development. We start with the recommended direct swap and investigate failures within that allowance.
One final assessment: One configuration is locked before testing on cases held back from development. Your agreed criteria stay fixed. The report records whether it passes, fails or needs more evidence.

Your expertise, where it matters.

Pisama prepares the work. Your team provides domain judgement and decides whether to deploy.

Pisama handles

Evidence preparation and proposed criteria
Model comparisons and failure investigation
Focused review questions and the final handoff

Your team contributes

Approved logs, policies and provider access
Criteria approval and answers to domain questions
The rollout decision and deployment

We agree expert review and engineering effort separately before work begins, including an audit sample and any gaps that need additional review.

What are you considering changing?

Tell us your current model, workflow and decision deadline. We’ll explain what can be assessed and propose a scope.

Discuss your migration

Free readiness guide

Prepare your migration

Start with one workflow and the evidence you already have. Use these five steps to locate existing materials, note gaps and agree the support a useful comparison needs.

This guide helps you prepare. It does not test models or recommend a replacement.

See the assisted assessment

Find your configuration and deadline
Locate the existing configuration or request logs that identify the provider, exact model ID, API route, reasoning level, prompt version and generation settings. Confirm any retirement date in the provider’s official notice, then note your decision deadline.
If missing. Ask the engineering contact where the configuration is stored. Note missing settings and keep an unconfirmed retirement date marked as unresolved.
Locate evidence you are allowed to use
Find existing log exports, tests, typical requests, known failures and trusted outcomes for one workflow, including the context each model receives. Check permission to use this evidence. Agree approved access and handling before sharing sensitive records; keep them out of an initial enquiry.
If missing. Note which records are unavailable or need approval. Any use of anonymized or synthetic evidence, and its limits, can be agreed during scoping.
Agree the decision and evidence needed
Bring policies, output requirements, tests and examples of trusted decisions. Pisama prepares comparison criteria and checks whether the available independent cases can support your agreed limits. Your reviewer resolves ambiguous outcomes before testing. Your domain rules remain the reference when the current model is wrong.
If missing. Record unclear outcomes and gaps in coverage. If the available evidence supports only a diagnostic, agree that scope explicitly before paying for model execution.
Agree access and check replay scope
Arrange the agreed access to existing configuration, logs and approved tool responses needed for replay. Confirm the current model is still callable and identify any documented model-call order and tool dependencies. The assisted scope covers one text workflow with structured decisions, up to three model calls and one read-only tool.
If missing. List unavailable models or unreplayable dependencies before agreeing scope. Saved outputs can help, but may not support a fresh baseline comparison.
Agree contacts, review effort and inference
Name an engineering contact and a qualified domain reviewer. Agree examples to calibrate the review, which disagreements need expert judgement and an audit sample of other results. Pisama prepares the evidence and focused questions. Agree separate estimates for domain review, engineering involvement and provider inference before work begins.
If missing. Note gaps in contact availability and any limits on time or spend. Agree the review plan, access needs and inference estimate during scoping.

Before we start.

What happens after I request a sample?

We follow up with a sample report and help applying it to your workflow. The public Banking77 report is available immediately, without an email address. A sample request does not start a paid assessment.

What if the evidence cannot support a decision?

We check replay feasibility and case coverage before execution. If your evidence supports only a diagnostic, we explain the gaps and agree that scope first. A pass is never guaranteed.

You receive the findings and next steps whether the result is pass, fail or inconclusive. Changes after final confirmation require a new agreed assessment.

Which workflows need a different scope?

Live tool execution, write actions and end-to-end autonomous agents need separate validation. Fine-tuned incumbents can be assessed, but model training is outside the initial scope. The current model must still be callable for a fresh baseline.

How are inference costs and data handled?

You pay your providers directly for inference using your own accounts. BYOK does not mean execution is customer-hosted or that data stays in your environment. Managed inference billing is available by agreement.

Your team supplies approved cases. We agree access and data handling before testing, including any limits from anonymized or synthetic evidence. Do not include sensitive records in the initial enquiry.

Can we start on our own?

Yes. The free readiness guide helps you find your existing inputs and identify gaps. If you have the evaluation setup and capacity to investigate results, you may be able to run the comparison yourself.

Know what changesbefore you change models.