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Candidate assessment · October 2026 Edition

Criteria Corp vs CodeSignal

Seven of fourteen models named Criteria Corp first on the direct prompt; zero named CodeSignal. Criteria Corp was named by thirteen of the fourteen models and CodeSignal by eight and Criteria Corp carries 35 labels and CodeSignal 13, so the shares are not directly comparable.

Criteria Corp

accepted challenger

Named in three categories this edition.

CodeSignal

accepted challenger

Named in four categories this edition.

First-choice share19%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate14%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 12; printed, not drawn.
Labels3513A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Criteria Corp reading right to left. Rank and label count are printed, not drawn.TestGorilla was named alongside these two in nine of the fourteen direct answers. TestGorilla vs Criteria Corp · TestGorilla vs CodeSignal · Criteria Corp vs HackerRank

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.
Criteria CorpFirst choices, of fourteen modelsCodeSignal
Direct701 against Criteria Corp
Paraphrase11
Comparative10
Budget-constrained003 against Criteria Corp · 1 against CodeSignal
Scale-constrained10
Negative001 against Criteria Corp
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 Criteria Corp and CodeSignal 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
GPT-6 Luna
Muse Glimmer 30B
Criteria Corp CodeSignal first choice named as an alternative argued againstblank: not namedEach cell is one answer, Criteria Corp on the left and CodeSignal on the right.

The direct prompt

The plain question, one answer per model, grouped by where Criteria Corp and CodeSignal stood in it.

Criteria Corp first, CodeSignal not the choice

7 of 14 modelsCodeSignal was named in the answer but not as the choice, or not at all.
Perplexity SonarCriteria Corp alternatives: HireVue, TestGorilla, The Predictive Index
Grok 4.1 FastCriteria Corp alternatives: HireVue, TestGorilla, The Predictive Index
Mistral SmallCriteria Corp, Evalart alternatives: Adaface
DeepSeek V4 FlashCriteria Corp alternatives: TestGorilla, eSkill
Kimi K2Criteria Corp alternatives: TestGorilla, The Predictive Index, Vervoe, Wonderlic
GPT-6 LunaCriteria Corp alternatives: TestGorilla
Muse Glimmer 30BCriteria Corp alternatives: Evalart

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashTestGorilla alternatives: Criteria Corp, The Predictive Index, Vervoe
Qwen 3.7 FlashTestGorilla alternatives: CodeSignal, HireVue, Humantic AI

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 miniTestGorilla alternatives: Codility, HackerRank, Mercer | Mettl
Llama 4 Maverickno first choice
GLM 4.7 FlashXTestGorilla alternatives: Testlify, Vervoe
MiniMax M2.5Evalart, Toggl Hire

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
CodeSignal leads by two points.
CodeSignal2%#– of 13
Criteria Corp0%#11 of 13
The full small business standing →
Mid-marketThe figures above
The order flips: Criteria Corp leads at mid-market.
Criteria Corp19%#2 of 12
CodeSignal2%#5 of 12
The full mid-market standing →
Enterprise
Criteria Corp leads by thirteen points.
Criteria Corp13%#3 of 11
CodeSignal0%#– of 11
The full enterprise standing →

What the models said about Criteria Corp

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

“Avoid: Criteria Corp, Wonderlic, and HireVue if budget is a concern” Kimi K2 · budget prompt · hard negative
“Avoid platforms like Criteria Corp, Predictive Index, or HireVue” DeepSeek V4 Flash · budget prompt · hard negative
“Cons: User interface can feel dated/clunky ... implementation often requires a dedicated project manager.” Qwen 3.7 Flash · direct prompt · soft negative
“Criteria Corp is a good choice for mid-market companies that want a validated battery embedded in an existing ATS or HRIS stack.” Llama 4 Maverick · paraphrase prompt · first choice
“For a 500-person company: Mid-market platforms (Criteria Corp, Plum, TestGorilla Plus tier) often hit the sweet spot” Kimi K2 · scale prompt · first choice
“For a mid-market B2B company the platform that is most consistently called out as a fit is Criteria Corp.” Muse Glimmer 30B · direct prompt · first choice

What the models said about CodeSignal

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

“The best skills testing tool for hiring for a mid-sized B2B company would be CodeSignal, TestGorilla, or Criteria Corp.” Llama 4 Maverick · 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.