Ten of fourteen models named Sage Fixed Assets first on the direct prompt; zero named Fixed Asset Pro. Sage Fixed Assets was named by fourteen of the fourteen models and Fixed Asset Pro by eleven and Sage Fixed Assets carries 53 labels and Fixed Asset Pro 29, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the fixed asset accounting page.
Across every category in the November 2026 Edition, Sage Fixed Assets and Fixed Asset Pro were named in the same answer thirty-three times, of the 129 answers naming Sage Fixed Assets and the 70 naming Fixed Asset Pro. In those answers Fixed Asset Pro took the first choice three times and Sage Fixed Assets eleven.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | FA | |||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | FA | |||||
| DeepSeek V4 Flash | FA | FA | ||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | FA | |||||
| Kimi K2 | FA | FA | ||||
| GLM 4.7 FlashX | FA | |||||
| MiniMax M2.5 | FA | |||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B | FA |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“This tool received a high negative share in evaluations, with users reporting it is often inadequate for complex needs.” Mistral Small · negative prompt · hard negative
“Avoid enterprise products such as Netgain, Sage Fixed Assets, or Asset Panda” Perplexity Sonar · budget prompt · hard negative
“Unlike subscription-heavy enterprise software (like Sage or Bloomberg Tax), Fixed Asset Pro offers flat pricing without high recurring annual fees.” Gemini 3.5 Flash · budget prompt · soft negative
“Start with Sage Fixed Assets if you need a proven, dedicated solution for compliance and depreciation—it's the most frequently recommended "default"” Grok 4.20 · direct prompt · first choice
“Compliance-driven mid-market firms that want turnkey IRS and GAAP support... purpose-built for the entire asset lifecycle” Claude Haiku 4.5 · direct prompt · first choice
“Sage Fixed Assets is the strongest overall pick when rigorous depreciation and tax-rule compliance can't be compromised.” Claude Haiku 4.5 · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“This tool is explicitly flagged as something to avoid if your needs are complex” Mistral Small · negative prompt · hard negative
“Avoid for mid-market: ... overly basic tools like Fixed Asset Pro.” Grok 4.20 · direct prompt · hard negative
“affordable and strong for basic depreciation and asset tracking. Users should be aware of its calculation reliability, interface age, limited custom reporting, and need for manual processes” Muse Glimmer 30B · negative prompt · soft negative
“This is widely considered the best option for small-to-medium businesses and CPAs who want a dedicated system without paying monthly subscription fees.” Qwen 3.7 Flash · budget prompt · first choice
“Tightest cash flow + need dedicated depreciation: Fixed Asset Pro or Canopy $32/mo are the most commonly cited "affordable" dedicated solutions.” Muse Glimmer 30B · budget prompt · first choice
“Start with AssetAccountant's free tier or Fixed Asset Pro (one-time purchase) to avoid ongoing subscription costs.” GLM 4.7 FlashX · budget prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.