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Recognition and rewards · November 2026 Edition

Assembly vs Nectar

One of fourteen models named Assembly first on the direct prompt; one named Nectar. Assembly was named by thirteen of the fourteen models and Nectar by fourteen and Assembly carries 31 labels and Nectar 41, so the shares are not directly comparable.

Assembly

accepted challenger

Named in two categories this edition.

Nectar

accepted challenger

Named in four categories this edition.

First-choice share8%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#4A position in a field of 10; printed, not drawn.
Labels3141A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Assembly reading right to left. Rank and label count are printed, not drawn.Bonusly was named alongside these two in thirteen of the fourteen direct answers. Bonusly vs Assembly · Bonusly vs Nectar · Matter vs Assembly

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 recognition and rewards page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
AssemblyFirst choices, of fourteen modelsNectar
Direct11
Paraphrase02
Comparative01
Budget-constrained30
Scale-constrained00
Negative00
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.

Across every category in the November 2026 Edition, Assembly and Nectar were named in the same answer fifty-five times, of the 98 answers naming Assembly and the 118 naming Nectar. In those answers Nectar took the first choice two times and Assembly six.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Assembly and Nectar 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
Assembly Nectar first choice named as an alternative argued againstblank: not namedEach cell is one answer, Assembly on the left and Nectar on the right.

The direct prompt

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

Assembly first, Nectar an alternative

1 of 14 modelsNectar was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXAssembly, Bonusly alternatives: Awardco, Motivosity, Nectar

Nectar first, Assembly not the choice

1 of 14 modelsAssembly was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Nectar alternatives: Achievers, Bonusly, Bucketlist Rewards, Motivosity

Neither was the first choice, one was named

8 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashBonusly alternatives: Awardco, Kudos, Nectar, WorkTango
Perplexity SonarBonusly alternatives: Awardco, Nectar
Grok 4.20Awardco alternatives: BRAVO, Bonusly, Motivosity, Nectar
Mistral SmallBonusly alternatives: Assembly, Awardco, Bucketlist Rewards, Motivosity
DeepSeek V4 FlashBonusly alternatives: Assembly, Awardco, Nectar
Kimi K2Bonusly alternatives: Assembly, Awardco, Motivosity
GPT-6 LunaBonusly alternatives: Awardco, Motivosity, Nectar
Muse Glimmer 30BBonusly alternatives: Assembly, Awardco, Inspirus, Nectar

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniAwardco alternatives: Achievers, Bonusly, Workhuman
Llama 4 Maverickno first choice
Qwen 3.7 FlashBonusly alternatives: Culture Amp, Kudos, Lattice
MiniMax M2.5Awardco, Bonusly alternatives: Inspirus, Vantage Circle

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
Nectar leads by four points.
Nectar7%#4 of 13
Assembly4%#6 of 13
The full small business standing →
Mid-marketThe figures above
The order flips: Assembly leads at mid-market.
Assembly8%#3 of 10
Nectar6%#4 of 10
The full mid-market standing →
Enterprise
Assembly leads by two points.
Assembly2%#7 of 10
Nectar0%#10 of 10
The full enterprise standing →

What the models said about Assembly

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

“For most mid-market companies: Bonusly or Assembly - excellent balance of features, pricing, and ease of use” GLM 4.7 FlashX · direct prompt · first choice
“Bonusly or Assembly offer the best balance of features, transparency, and scalability.” Kimi K2 · budget prompt · first choice
“For a company with a limited budget, Assembly is the best overall choice” Perplexity Sonar · budget prompt · first choice

What the models said about Nectar

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

“Nectar is best for mid-market companies (50-2,000) wanting clear peer recognition with visibility into collaboration patterns” Claude Haiku 4.5 · direct prompt · first choice
“Choose Bonusly or Nectar for their seamless Slack/Teams integrations and low barrier to entry.” Gemini 3.5 Flash · comparative prompt · first choice
“I'd usually recommend Nectar as the best starting point” GPT-5.4 mini · paraphrase prompt · first choice
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.