Avoma vs Fathom
Ten of fourteen models named Avoma first on the direct prompt; zero named Fathom. Avoma was named by fourteen of the fourteen models and Fathom by eleven and Avoma carries 52 labels and Fathom 27, so the shares are not directly comparable.
Named in four categories this edition.
Named in six categories 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 conversational intelligence and call recording page.
By framing
AvomaFirst choices, of fourteen modelsFathom
Across every category in the October 2026 Edition, Avoma and Fathom were named in the same answer eighty times, of the 234 answers naming Avoma and the 180 naming Fathom. In those answers Fathom took the first choice twenty times and Avoma twenty.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | | |
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | | | |
| Grok 4.20 | | | | |||
| Mistral Small | | | | | ||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | ||||
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | |
Avoma Fathom first choice named as an alternative argued againstblank: not namedEach cell is one answer, Avoma on the left and Fathom on the right.The direct prompt
Avoma first, Fathom an alternative
10 of 14 modelsFathom was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Avoma41%#1 of 10Fathom6%#5 of 10What the models said about Avoma
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“The headline price covers meeting transcription only, which may not align with broader needs.” Claude Haiku 4.5 · negative prompt · soft negative
“Affordable, good for SMBs transitioning to mid-market.” Mistral Small · scale prompt · soft negative
“The best conversation intelligence and call recording platform for a mid-market B2B company is likely to be Avoma, Grain, or other AI-native conversation intelligence tools.” Llama 4 Maverick · direct prompt · first choice
“Avoma and Insight7 are top choices due to their affordability, ease of use, and strong feature sets tailored to mid-market needs” Mistral Small · direct prompt · first choice
“For most mid-market B2B companies, Avoma offers the best balance of features, pricing transparency, and scalability” Kimi K2 · direct prompt · first choice
What the models said about Fathom
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“not a replacement for CI platforms because it lacks the deal-risk layer, coaching workflow, and forecasting use case” Perplexity Sonar · negative prompt · hard negative
“users report frequent issues with "hallucinated" insights where the AI summarizes a call incorrectly” Qwen 3.7 Flash · negative prompt · soft negative
“Fathom is widely considered the gold standard for teams seeking maximum value with minimal or zero spend.” Gemini 3.5 Flash · budget prompt · first choice
“### 1. Fathom - Best Free Option ... want to start completely free with no usage limits.” 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.