Attention and Gong both record sales conversations, score them with AI against criteria you set, and use what they capture to flag deals and drive next steps. Gong is the older and broader platform, with forecasting and enablement products built around its recordings. Attention pitches itself as the more configurable, agent-first option, and its own comparison page argues for switching.
This page compares them on what their own pages say, read on 31 October 2026, and then gives five tests to run on either before you trust its scores. Our AI call scoring guide covers the method in full.
The short answer
Choose Gong if you want call scoring inside a platform that also runs your forecast, account views and enablement programs. Choose Attention if you want AI agents built in plain language, CRM fields filled from calls, and scoring and deal-health criteria you configure yourself (its "only Attention" claims are its own; test them). Whichever you pick, test the AI scores against your own expert markers before you put them in front of reps, because both vendors can change the model under you.
What Gong does
Gong's AI Call Reviewer is "an agent that helps your team give structured feedback on sales and customer conversations using scorecards." It can suggest answers for a manager to accept or edit, or "score entire calls automatically using your predefined criteria" (Gong help center). It is available on the Enable Essentials and Enable plans.
Gong's documentation also says it "rolled out updates to the AI model for AI Call Reviewer (Scorecards) on January 27, 2026," and that "changes in call review trends may reflect improved scoring accuracy" (Gong help center). That note is unusually candid, and it matters: when the model changes, score trends can move with no change in rep behavior.
Around scoring, Gong sells the wider Revenue AI OS: agents "for pipeline review, coaching, deal risk, and more," and Gong Enable for "scalable coaching, self-practice, and rep readiness programs" (Gong platform). Gong's pricing page lists no prices; licenses are per user with a platform fee (Gong pricing).
What Attention does
Attention's home page leads with agents: "Every meeting, email, and sales interaction captured. The next move on every deal, drafted and done. You approve. Attention executes" (Attention). Admins describe a play in plain language and Attention builds the agent, with "200+ integrations."
For scoring, Attention says it will "score your reps with any criteria," whether "existing frameworks (MEDDIC, BANT), your own sales playbook, or ad-hoc grading for product launches," and compare rep performance "by role or call type" (Attention scorecards). Its comparison page claims five features Gong lacks, including customizable deal scoring ("only Attention will let you choose how your deals are scored"), call notes mapped to CRM fields, and cross-call analysis (Attention vs Gong). Those "only" claims are Attention's; Gong's own pages describe agents and AI scorecards of its own, so test the specific workflow you need in both.
Side by side
| Dimension | Gong | Attention |
|---|---|---|
| Core | Revenue AI OS: recording, forecasting, enablement | Recording plus AI agents that draft and execute next steps |
| AI call scoring | AI Call Reviewer: suggested answers or fully automatic scoring against your scorecards | AI scorecards against any criteria, including MEDDIC, BANT or your playbook |
| Scoring model changes | Documented: model updated 27 Jan 2026, trends may shift | Not described on the pages we read |
| Deal health | Forecast and deal boards with risk detection | Deal scoring with criteria you choose |
| What it coaches | Call delivery and skills, through scorecards and Enable | Call delivery against your criteria, with recommendations after every call |
| Where it works | Recorded calls and emails, rolled up to deals and accounts | Recorded calls and emails, rolled up to deals |
| Manager view | Scorecards, boards, forecasts | Rep performance by role or call type; deal scores |
| Published pricing | No | Not shown on the pages we read |
Five tests to run on either before you buy
Every AI call scorer looks plausible in a demo. Our research program ran more than 5,000 tests and more than 250,000 scoring decisions across 15 AI models, against blind human marking by trained assessors at PeerLab, to find out what makes one trustworthy (field guide). Five tests came out of it that any buyer can run in a pilot.
- Rank agreement with your experts
- Re-run stability
- Expert-to-expert baseline
- Model-change drift
- Quote checking
Rank agreement with your experts. Have two of your best managers mark a set of your calls blind. Ask the tool to score the same calls and compare the order, best to worst. Our final setup reached 0.89 rank agreement with the experts' order. Judge on ranking: across 34 configurations, average error and ranking correlated at only 0.02, and a scorer that never read the transcript landed an average error (0.41) almost identical to the best real model's (0.43).
Re-run stability. Score the same calls twice. In our final setup 1.7% of checklist-model scores changed on a re-run. One of the newest general-purpose models on its own changed 15% to 17% of scores between identical runs. If a rep can get a different score for the same call, they will stop trusting all of them.
Expert-to-expert baseline. Your two managers will disagree with each other; trained experts in our study disagreed on 18% of scores. Do not hold a tool to a standard your own people do not meet.
Model-change drift. Ask the vendor how they check scores when the underlying model changes, and what notice you get. In our research every newer model generation marked reps harsher than the human assessors, and the gap grew. Gong's own note about its January 2026 update shows this is a live question for any vendor.
Quote checking. If the tool writes feedback with quotes from the call, check that every quote is in the transcript. In our tests, cheap evidence extraction produced quotes of which a fifth were not in the call.
The can AI score a sales call post walks through the research behind each test, and if you want to see the method run on one of your own calls, talk to us.
Which one when
- You want scoring, forecasting and enablement in one platform. Gong.
- You want admins to build agents and scoring logic without a RevOps queue. Attention.
- You run a named method and want calls scored against it. Both say they support custom criteria; run test 1 with your method's experts marking.
- You care about scores staying comparable over time. Ask both vendors for test 4 in writing.
- You already have Gong and are considering a switch. Attention's comparison page makes the case; pilot both on the same marked calls and compare on tests 1 and 2.
- You are weighing Attention as the leaner option. Whether leaner is better depends on what you would stop using. If your managers run the forecast from Gong boards each week, a switch moves that work somewhere else, and that cost belongs in the comparison. If nobody opens the boards and the team mostly uses recordings and scorecards, you are paying for a platform you do not use. Attention's comparison page also lists customer results from switching; treat those as the vendor's figures and ask for the references behind them.
Running both, or switching
Not every team has to choose on day one. Ask Attention whether it can work alongside your Gong setup during a trial, so you can compare its scoring next to Gong on the same calls before deciding anything.
If you do switch, plan three things. Rebuild your scorecards in the new tool from your written criteria, and check them against expert marking before reps see a score. Expect scores to move: a new tool runs a different model and prompt, so a drop in week one may come from the scorer and say nothing about the reps. Run both tools on the same calls for a few weeks and re-baseline. And decide what happens to your Gong history, because trends that start again from zero are hard to coach from.
A worked example: a 40-seat team picks one
Larkfield, a fictional payroll software company, has 40 sellers, Gong on renewal in three months, and a sales leader named Robin who wants calls scored against the team's discovery method. Here is how Robin runs the decision in five weeks.
- Week 1: mark the calls. Two senior managers, Sam and Jordan, each mark the same 30 discovery calls blind against the team's scorecard. Where they disagree, they talk it through and write down why. That set becomes the answer key, and their disagreement rate is Larkfield's own baseline for test 3.
- Week 2: load both tools. Both vendors build the same scorecard from the written criteria. Robin sends both the same 30 calls and asks for scores without showing the answer key.
- Week 3: run tests 1 and 2. Robin lines up each tool's order against the managers' order, then has both score the 30 calls again.
- Week 4: run test 5, then check the CRM. Sam opens five calls in each tool and checks the quotes. Jordan checks what lands in the CRM and how much of it needs correcting.
- Week 5: decide. The tool whose order sat closer to Sam's and Jordan's, and whose scores held on the re-run, wins. If the two are close on both, board usage breaks the tie: Robin lists what managers opened in Gong over the last quarter, and forecast boards in weekly use weigh toward renewing Gong.
Questions to ask in either demo
- How do we write our own criteria, and who can edit them after launch?
- What does the rep see after a call, and can a manager hold scores back during a pilot?
- How are quotes in the feedback tied to the transcript?
- What happens to our recordings and scorecards if we leave?
- What does the price include, and what is charged per seat or per platform?
Common mistakes when comparing the two
The first is judging on the demo call. Every vendor picks a call its scorer handles well, and a team's own calls are messier.
The second is comparing feature lists without checking usage. A forecasting module counts for nothing if managers keep running the forecast in a spreadsheet.
The third is switching mid-quarter with no overlap. Reps notice when their numbers drop the week the tool changes, and one wrong score in front of a good rep can cost the whole program its credibility.
What a score leaves for the coach
A call score measures how the rep handled one conversation. It does not tell the rep what the deal is missing or what to say to the buyer next, and that is where most deals are won or lost. A 2 out of 5 on gaining commitment tells the manager where to look; the coaching is deciding which commitment this deal needs and from whom, then planning the conversation that gets it.
Replicate Labs scores calls too, and the research above is how we test our own scorer. The difference is what follows. The expert checks each deal's buyer evidence against the team's method, the rep sees what is missing, and one click starts a call with Caty to prepare the next buyer conversation. The rep talks it through; typing is the fallback. For how coaching fits around a score, see our AI sales coaching guide, and the sales call scorecard for building the scorecard itself. Talk to us.
FAQ
What is the difference between Attention and Gong?
Gong is a broad revenue platform that records calls and adds forecasting, account boards, AI call scoring and enablement on top. Attention records calls and emails and leads with AI agents that draft and execute next steps, CRM updates from calls, and scoring criteria you configure. Both offer AI call scoring.
Does Gong score calls with AI?
Yes, Gong scores calls with AI. Gong's AI Call Reviewer can suggest answers to scorecard questions for a manager to review, or score entire calls automatically against your criteria. It is available on Gong's Enable plans, and Gong updated the underlying AI model on 27 January 2026.
How do you know if an AI call scorer is accurate?
Have your best managers mark a set of calls blind, then compare the tool's ranking of those calls with theirs. Score the same calls twice to check stability. Judge on ranking, because average error can look fine for a scorer that did not read the transcript. Also ask how the vendor checks scores after a model change.
Why did our AI call scores change when reps did not?
The most likely cause is a model change at the vendor. In our research, every newer model generation marked reps harsher than human assessors. Gong's documentation notes that review trends may shift after its January 2026 model update. Ask your vendor for change dates and re-baseline scores against expert marking.
Is Attention a good Gong alternative for call scoring?
Attention can be a good Gong alternative if configurable scoring and agents matter more to you than having forecasting and enablement in one platform. Run both tools on the same expert-marked calls, compare rank agreement and re-run stability, and decide on the results.