GTM AI Index
Index Sales Revenue intel › Avoma vs Revenue Grid
Revenue intelligence and forecasting · September 2026 Edition

Avoma vs Revenue Grid

Five of twelve models named Avoma first on the direct prompt; zero named Revenue Grid. Avoma was named by eleven of the twelve models and Revenue Grid by eleven and Avoma carries 31 labels and Revenue Grid 30, so the shares are not directly comparable.

Avoma

accepted challenger

Named in five categories this edition.

Revenue Grid

accepted challenger

Named in one category this edition.

First-choice share20%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#7A position in a field of 13; printed, not drawn.
Labels3130A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Avoma reading right to left. Rank and label count are printed, not drawn.Gong was named alongside these two in nine of the twelve direct answers. Avoma vs Clari Copilot · Avoma vs HubSpot Sales Hub · Avoma vs Gong

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 revenue intelligence and forecasting page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
AvomaFirst choices, of twelve modelsRevenue Grid
Direct50
Paraphrase00
Comparative00
Budget-constrained61
Scale-constrained00
Negative002 against Revenue Grid
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Avoma and Revenue Grid were named in the same answer forty-three times, of the 239 answers naming Avoma and the 70 naming Revenue Grid. In those answers Revenue Grid took the first choice zero times and Avoma fourteen.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Avoma and Revenue Grid stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
Avoma Revenue Grid first choice named as an alternative argued againstblank: not namedEach cell is one answer, Avoma on the left and Revenue Grid on the right.

The direct prompt

The plain question, one answer per model, grouped by where Avoma and Revenue Grid stood in it.

Avoma first, Revenue Grid an alternative

5 of 12 modelsRevenue Grid was named in the answer but not as the choice, or not at all.
Perplexity SonarAvoma alternatives: 6sense, Clari Copilot, Gong, ZoomInfo
Mistral SmallAvoma alternatives: BoostUp, Oliv, Revenue Grid, Salesloft
Kimi K2Avoma alternatives: BoostUp, Revenue Grid, Revenue.io
GLM 4.7 FlashXAvoma alternatives: Clari Copilot, Gong, HubSpot Sales Hub, Revenue Grid, Salesloft
MiniMax M2.5Avoma, Terret alternatives: Clari Copilot, Gong, HubSpot Sales Hub

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Clari Copilot alternatives: BoostUp, Gong, HubSpot Sales Hub, Revenue Grid
Gemini 3.5 FlashGong alternatives: Avoma, Clari Copilot, HubSpot Sales Hub, Oliv, Terret
Grok 4.1 FastGong alternatives: Avoma, Clari Copilot, HubSpot Sales Hub, Revenue Grid
DeepSeek V4 FlashBoostUp alternatives: Avoma, Gong, HubSpot Sales Hub

Neither was named

3 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniClari Copilot alternatives: BoostUp, Gong
Llama 4 MaverickBoostUp/Terret alternatives: HubSpot Sales Hub, InsightSquared, ZoomInfo GTM Workspace
Qwen 3.7 FlashClari Copilot alternatives: Einstein Copilot / EPM, Gong, Zuuna

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Avoma leads by eighteen points.
Avoma20%#3 of 14
Revenue Grid2%#8 of 14
The full small business standing →
Mid-marketThe figures above
Avoma leads by eighteen points.
Avoma20%#1 of 13
Revenue Grid2%#7 of 13
The full mid-market standing →
Enterprise
The order flips: Revenue Grid leads at enterprise.
Revenue Grid2%#5 of 12
Avoma0%#– of 12
The full enterprise standing →

What the models said about Avoma

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.

“If you need forecasting and conversation intelligence together without breaking the budget, Avoma or Revenue Grid are strong choices.” Claude Haiku 4.5 · budget prompt · first choice

What the models said about Revenue Grid

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.

“Single-vendor dependency: Platforms tightly integrated with specific CRMs (e.g., Revenue Grid/Salesforce, Chorus/ZoomInfo)” GLM 4.7 FlashX · negative prompt · soft negative
“Setup Nightmares & Stability Issues” DeepSeek V4 Flash · negative prompt · soft negative
“Revenue Grid is the most budget-friendly option” Claude Haiku 4.5 · budget prompt · first choice
“transparent pricing starting around $30 per user per month, far below the enterprise norm” Claude Haiku 4.5 · direct prompt · alternative
“Choose Revenue Grid if: You're Salesforce-heavy and want lightweight automation.” GLM 4.7 FlashX · comparative prompt · alternative
Also compared

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.