Is GPT-5.4 mini good enough?

For sorting email, yes: it tied for the top at 95.0%. For customer support replies, no: it scored 68.0, last of 10 models, and leaked another customer's data on 2 emails.

Where should I not use a cheap model?

Customer support replies and scheduling. On support, all three low-cost models made severe errors. On scheduling, Grok 4.20 scored 41.7% and often proposed times over busy events.

How do you measure cost?

Each model gets a cost tier (low, medium or high) relative to the other models in the same job, from what our runs cost. Claude models ran on a Claude subscription, so they have no metered cost.

RealRun / Guides

Cheapest AI model that is still accurate for business tasks (2026)

Short answer: It depends on the job. On two of our five tests a low-cost model matched or beat the expensive ones: GPT-5.4 mini tied for the top on inbox triage at 95.0%, and GPT-6 Luna had the top finance score at 96.3 with no severe errors. On customer support replies, all three low-cost models made severe errors, and GPT-5.4 mini came last at 68.0. On scheduling, the cheapest Grok, Grok 4.20, scored 41.7%. So go cheap on sorting email and checking the books, and pay up for anything that writes to a customer.

Every low-cost model we tested, by job

Job	Low-cost model	Its score	Top score in that job	Problems flagged
Inbox triage	GPT-5.4 mini	95.0	GPT-6.1 Sol: 95.0	No scams filed as Act
Inbox triage	Gemini 3.8 Flash	91.1	GPT-6.1 Sol: 95.0	No scams filed as Act
Inbox triage	Grok 4.20	86.7	GPT-6.1 Sol: 95.0	Filed a scam as Act
Scheduling	GPT-5.4 mini	90.0	Gemini 3.8 Flash: 100.0	Caution: check every time
Scheduling	Grok 4.20	41.7	Gemini 3.8 Flash: 100.0	Not recommended
Receipts	GPT-6 Luna	91.4	GPT-6.1 Sol: 95.8	Severe errors: none
Receipts	Gemini 3.5 Flash-Lite	85.4	GPT-6.1 Sol: 95.8	Severe errors: both passes
Finance	GPT-6 Luna	96.3	GPT-6 Luna: 96.3	Severe errors: 0
Finance	Grok 4.3	89.7	GPT-6 Luna: 96.3	Severe errors: 13
Finance	Gemini 3.5 Flash-Lite	84.5	GPT-6 Luna: 96.3	Severe errors: 23
Support replies	Gemini 3.8 Flash	90.6	Claude Opus: 98.6	Severe errors: 2
Support replies	GPT-6 Luna	82.2	Claude Opus: 98.6	Severe errors: 5
Support replies	GPT-5.4 mini	68.0	Claude Opus: 98.6	Severe errors: 10

"Low-cost" means cost_tier low in our results. Cost tiers are relative within each job, from what our runs cost, so the same model can be low in one job and medium in another (Gemini 3.8 Flash is low on email and support, medium on scheduling). Claude models ran on a subscription and have no metered cost, so they are not in this table. This page doesn't list per-token prices.

How the testing works: we wrote our own synthetic test sets with planted traps, like scam emails, calendar conflicts, bad math and fake invoices. Each job also has a separate held-back set, written blind and kept out of all prompt and scoring tuning, that only ran at the end. Every model in a job got the same items. Most ran 2 or more passes; in the support test 3 of 10 models ran 1 pass and are marked less certain. All data is synthetic, never real accounts. Scores differ by job (accuracy %, valid-answer % or a 0 to 100 score with 5 points off per severe error), so compare models within a column, not across columns.

Results updated 15 Oct 2026. Tests run October 2026. Full data: results.json.

What we found

Email: GPT-5.4 mini scored 95.0% on fresh emails, tied with GPT-6.1 Sol, and filed no scams as Act. Grok 4.20 is also low cost but filed a scam email as Act in both passes on the fresh set.
Finance: GPT-6 Luna had the top score, 96.3, with 0 severe errors, and was the cheapest of the near tie at the top. Grok 4.3 and Gemini 3.5 Flash-Lite, also low cost, made 13 and 23 severe errors.
Receipts: GPT-6 Luna scored 91.4 with no severe errors, the budget pick. GPT-6.1 Sol topped receipts at 95.8.
Scheduling: GPT-5.4 mini scored 90.0%. On hard cases it sometimes booked over a busy event or offered a time when none worked. Check every time it proposes.
Support replies: Leaked another customer's data on 2 emails, promised wrong refund amounts and missed an escalation; 10 severe errors over 2 passes.

For you

The way a cheap model goes wrong is that it looks fine on the easy stuff and breaks on the hard case. On our easy support round GPT-5.4 mini scored 98.3, right with everyone else. On the hard round it scored 68.0 and leaked another customer's data on 2 emails. Test the cheap model on your hardest emails, not your easiest ones.

For your clients

Pick the model per job, not per vendor. You can run a low-cost model on inbox sorting or bank exports and show the client the scores that back it, then put a stronger model on customer replies where the cheap ones made severe errors. That saves them money without betting their customers on it.

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FAQ

What is the cheapest AI model that is still accurate for business tasks?: It depends on the job. In our October 2026 tests GPT-6 Luna, a low-cost model, had the top finance score at 96.3 with no severe errors, and GPT-5.4 mini tied for the top inbox score at 95.0%.
Is GPT-5.4 mini good enough?: For sorting email, yes: it tied for the top at 95.0%. For customer support replies, no: it scored 68.0, last of 10 models, and leaked another customer's data on 2 emails.
Where should I not use a cheap model?: Customer support replies and scheduling. On support, all three low-cost models made severe errors. On scheduling, Grok 4.20 scored 41.7% and often proposed times over busy events.
How do you measure cost?: Each model gets a cost tier (low, medium or high) relative to the other models in the same job, from what our runs cost. Claude models ran on a Claude subscription, so they have no metered cost.

More from RealRun

Scores reflect our tests on the dates shown, not a guarantee. Claude models ran on a Claude subscription, so they have no metered API cost. cost_tier is relative within a task (low/medium/high) from our run spend; 'subscription' = not metered. Tested October 2026 by RealRun (Base Ledger IP LLC).