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Index Talent acquisition AI recruiting assistants › Workable vs Ashby
AI recruiting assistants · September 2026 Edition

Workable vs Ashby

One of twelve models named Workable first on the direct prompt; two named Ashby. Workable was named by eleven of the twelve models and Ashby by ten and Workable carries 24 labels and Ashby 14, so the shares are not directly comparable.

Workable

accepted challenger

Named in nine categories this edition.

Ashby

accepted challenger

Named in seven categories this edition.

First-choice share17%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%21%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 13; printed, not drawn.
Labels2414A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Workable reading right to left. Rank and label count are printed, not drawn.GoPerfect vs Workable · GoPerfect vs Ashby · Workable vs Manatal

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 AI recruiting assistants page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
WorkableFirst choices, of twelve modelsAshby
Direct122 against Ashby
Paraphrase61
Comparative10
Budget-constrained002 against Workable
Scale-constrained00
Negative001 against Ashby
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, Workable and Ashby were named in the same answer sixty-one times, of the 288 answers naming Workable and the 136 naming Ashby. In those answers Ashby took the first choice three times and Workable ten.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Workable and Ashby 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
Workable Ashby first choice named as an alternative argued againstblank: not namedEach cell is one answer, Workable on the left and Ashby on the right.

The direct prompt

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

Workable first, Ashby an alternative

1 of 12 modelsAshby was named in the answer but not as the choice, or not at all.
Grok 4.1 FastWorkable alternatives: Ashby, hireEZ

Ashby first, Workable not the choice

2 of 12 modelsWorkable was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashAshby alternatives: Findem, Humanly, Juicebox
Mistral SmallAshby alternatives: Greenhouse, TheHireHub.AI, hireEZ

Neither was the first choice, one was named

7 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5GoPerfect alternatives: Juicebox, Workable
Perplexity SonarGoPerfect alternatives: Autonomy Recruit, Workable
DeepSeek V4 FlashGreenhouse alternatives: Ashby, Gem, SmartRecruiters, Workable, hireEZ
Qwen 3.7 FlashGem alternatives: Ashby, Autonomy Recruit, Greenhouse, TheHireHub.AI
Kimi K2Greenhouse alternatives: GoPerfect, Lever, Workable
GLM 4.7 FlashXTheHireHub.AI alternatives: Ashby, Autonomy Recruit, Greenhouse, Workable
MiniMax M2.5TheHireHub.AI alternatives: Ashby, Lever, Workable

Neither was named

2 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniSeekOut alternatives: Paradox
Llama 4 MaverickAutonomy Recruit alternatives: Humanly, Lever, TheHireHub.AI

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
Workable leads by twelve points.
Workable12%#3 of 9
Ashby0%#– of 9
The full small business standing →
Mid-marketThe figures above
Workable leads by ten points.
Workable17%#2 of 13
Ashby7%#4 of 13
The full mid-market standing →
Enterprise
Workable leads by ten points.
Workable10%#3 of 10
Ashby0%#– of 10
The full enterprise standing →

What the models said about Workable

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

“The Best All-in-One Solution: Workable ... Go with Workable. It offers the best balance of price, ease of use, and AI capability” Qwen 3.7 Flash · paraphrase prompt · first choice
“I'd recommend starting with either Pin (for simplicity and cost-effectiveness) or Workable (for comprehensive features)” Claude Haiku 4.5 · paraphrase prompt · first choice

What the models said about Ashby

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

“Potentially Greenhouse, Lever (by Employ), Ashby (FCRA investigations for AI screening/interviews)” Grok 4.1 Fast · negative prompt · soft negative
“Has a learning curve; avoid if your team wants simplicity” Kimi K2 · direct prompt · soft negative
“less "AI recruiter" and more ATS/workflow platforms” Perplexity Sonar · direct prompt · soft negative
“Ideal for startups and mid-market companies needing a unified system for ATS, CRM, scheduling, and analytics.” Mistral Small · direct prompt · first choice
“I'd recommend Ashby if you need a full ATS replacement, or Spark Hire if you already have an ATS” Kimi K2 · 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.