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Index › HR › Performance and talent management › Goals and OKRs › Tability vs Lattice
Goals and OKRs · November 2026 Edition

Tability vs Lattice

Seven of fourteen models named Tability first on the direct prompt; zero named Lattice. Tability was named by thirteen of the fourteen models and Lattice by fourteen and Tability carries 44 labels and Lattice 43, so the shares are not directly comparable.

Tability

accepted challenger

Named in one category this edition.

Lattice

accepted challenger

Named in fifteen categories this edition.

First-choice share29%10%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate2%14%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 17; printed, not drawn.
Labels4443A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Tability reading right to left. Rank and label count are printed, not drawn.Perdoo was named alongside these two in eight of the fourteen direct answers. Tability vs Profit.co · Tability vs Perdoo · Tability vs Leapsome

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 goals and okrs page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
TabilityFirst choices, of fourteen modelsLattice
Direct70
Paraphrase55
Comparative801 against Lattice
Budget-constrained201 against Lattice
Scale-constrained10
Negative101 against Tability · 4 against Lattice
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Tability and Lattice stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Tability Lattice first choice named as an alternative argued againstblank: not namedEach cell is one answer, Tability on the left and Lattice on the right.

The direct prompt

The plain question, one answer per model, grouped by where Tability and Lattice stood in it.

Tability first, Lattice an alternative

7 of 14 modelsLattice was named in the answer but not as the choice, or not at all.
Grok 4.20Tability alternatives: 15Five, Lattice, Leapsome
Mistral SmallTability alternatives: 15Five, Leapsome
DeepSeek V4 FlashTability alternatives: 15Five, Leapsome, Perdoo, Profit.co
Kimi K2Tability alternatives: 15Five, Lattice, Perdoo, Profit.co
GLM 4.7 FlashXTability alternatives: 15Five, Perdoo, Profit.co, Weekdone
MiniMax M2.5Leapsome, Tability alternatives: 15Five, Betterworks, Lattice
Muse Glimmer 30BTability alternatives: 15Five, Betterworks, Leapsome, Perdoo, Profit.co

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Mooncamp alternatives: FlowyTeam, Perdoo, Profit.co, Tability
GPT-5.4 miniBetterworks alternatives: Lattice, Leapsome, Perdoo, WorkBoard
Gemini 3.5 FlashProfit.co alternatives: Lattice, Leapsome, Microsoft Viva Goals, Quantive
Perplexity SonarCascade Strategy alternatives: Perdoo, Tability
GPT-6 LunaPerdoo alternatives: Betterworks, Lattice, Profit.co, WorkBoard

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 Maverickno first choice
Qwen 3.7 FlashMooncamp alternatives: 15Five, Betterworks, Leapsome

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
Tability leads by seventeen points.
Tability18%#1 of 20
Lattice2%#13 of 20
The full small business standing →
Mid-marketThe figures above
Tability leads by nineteen points.
Tability29%#1 of 17
Lattice10%#4 of 17
The full mid-market standing →
Enterprise
Tability leads by ten points.
Tability12%#4 of 11
Lattice2%#7 of 11
The full enterprise standing →

What the models said about Tability

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

“Overwhelming UI for new users” DeepSeek V4 Flash · negative prompt · soft negative
“Tability: AI goal drafting, automated check-ins, Slack nudges, AI-suggested progress ... AI needs: Tability leads in AI-assisted workflow” MiniMax M2.5 · comparative prompt · first choice
“Consistently ranked as a top overall and enterprise solution, Tability is praised for its structured approach, strong benchmark scores” Mistral Small · comparative prompt · first choice
“Tability ranks first among tested platforms, as it's the only platform where dashboards, governance and AI didn't need assembling.” Claude Haiku 4.5 · comparative prompt · first choice

What the models said about Lattice

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

“Avoid enterprise tools: Skip pricier ones like Lattice, 15Five, or Quantive” Grok 4.20 · budget prompt · hard negative
Also compared
Tability vs Profit.coTability vs PerdooTability vs LeapsomeTability vs OKRs ToolTability vs MooncampTability vs TeamflectProfit.co vs LatticePerdoo vs LatticeLattice vs LeapsomeLattice vs OKRs ToolLattice vs MooncampLattice vs TeamflectAll twenty-eight head-to-head pages and the full goals and okrs standing

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.