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Candidate assessment · November 2026 Edition

Codility vs Harver

Two of fourteen models named Codility first on the direct prompt; one named Harver. Codility was named by thirteen of the fourteen models and Harver by six and Codility carries 34 labels and Harver 11, so the shares are not directly comparable.

Codility

accepted challenger

Named in four categories this edition.

Harver

accepted challenger

Named in three categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%9%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#7A position in a field of 12; printed, not drawn.
Labels3411A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Codility reading right to left. Rank and label count are printed, not drawn.TestGorilla was named alongside these two in twelve of the fourteen direct answers. TestGorilla vs Codility · TestGorilla vs Harver · Criteria Corp vs Codility

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 candidate assessment page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
CodilityFirst choices, of fourteen modelsHarver
Direct21
Paraphrase001 against Codility
Comparative30
Budget-constrained00
Scale-constrained00
Negative002 against Codility · 1 against Harver
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 Codility and Harver 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
Codility Harver first choice named as an alternative argued againstblank: not namedEach cell is one answer, Codility on the left and Harver on the right.

The direct prompt

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

Codility first, Harver not the choice

2 of 14 modelsHarver was named in the answer but not as the choice, or not at all.
Mistral SmallCodility, HackerRank alternatives: TestGorilla, Thomas International
Muse Glimmer 30BCodility, Thomas alternatives: HackerRank, HireVue, Weirdly

Harver first, Codility not the choice

1 of 14 modelsCodility was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Criteria Corp, Harver, Plum alternatives: JobCannon, SHL, TestGorilla, Vervoe, Wonderlic

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Perplexity SonarCriteria Corp alternatives: Codility, TestGorilla, Vervoe
Grok 4.20Criteria Corp alternatives: Harver, TestGorilla, Thomas International
DeepSeek V4 FlashTestGorilla alternatives: Canditech, Codility, Criteria Corp, HackerRank
Llama 4 MaverickThomas International alternatives: Codility, EmployTest, HackerRank, Weirdly
Kimi K2TestGorilla alternatives: Codility, Criteria Corp, HackerRank, HireVue, Vervoe
MiniMax M2.5Prevue HR, TestGorilla alternatives: Codility, Criteria Corp, HackerRank, HireVue

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniCriteria Corp alternatives: HighMatch, TestGorilla, Testlify, The Predictive Index
Gemini 3.5 FlashTestGorilla alternatives: Criteria Corp, The Predictive Index, Vervoe
Qwen 3.7 FlashTestGorilla alternatives: HackerRank, PI Online, Spark Hire, Vervoe
GLM 4.7 FlashXTestGorilla alternatives: Criteria Corp, HireVue, Thomas International
GPT-6 LunaCriteria Corp alternatives: TestGorilla

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
Codility leads by two points.
Codility2%#5 of 11
Harver0%#– of 11
The full small business standing →
Mid-marketThe figures above
Codility leads by two points.
Codility4%#3 of 12
Harver2%#7 of 12
The full mid-market standing →
Enterprise
Codility leads by four points.
Codility4%#4 of 12
Harver0%#11 of 12
The full enterprise standing →

What the models said about Codility

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

“these are often viewed unfavorably compared to competitors like HackerRank or HackerEarth” Qwen 3.7 Flash · negative prompt · hard negative
“positioned as best for repeatable, defensible technical hiring decisions... Codility scored highest on published criteria” Muse Glimmer 30B · comparative prompt · first choice

What the models said about Harver

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

“heavy reliance on unvalidated or "black-box" AI/personality assessments (e.g., certain features in Harver, Paradox, or generic AI tools)” Grok 4.20 · negative prompt · soft negative
“Criteria Corp, Harver, and Plum bundle personality, cognitive, and behavioral assessments in one platform” Claude Haiku 4.5 · direct prompt · first choice
“include Criteria Corp, TestGorilla, Harver, and HireVue” DeepSeek V4 Flash · scale prompt · alternative
“Solid for high-volume or frontline hiring with good customer support” Grok 4.20 · direct 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.