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Technical hiring assessments · October 2026 Edition

Coderbyte vs DevSkiller

One of fourteen models named Coderbyte first on the direct prompt; one named DevSkiller. Coderbyte was named by twelve of the fourteen models and DevSkiller by nine and Coderbyte carries 22 labels and DevSkiller 10, so the shares are not directly comparable.

Coderbyte

accepted challenger

Named in two categories this edition.

DevSkiller

accepted challenger

Named in two categories this edition.

First-choice share10%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#8A position in a field of 9; printed, not drawn.
Labels2210A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Coderbyte reading right to left. Rank and label count are printed, not drawn.HackerRank was named alongside these two in ten of the fourteen direct answers. HackerRank vs Coderbyte · HackerRank vs DevSkiller · CodeSignal vs Coderbyte

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 technical hiring assessments page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
CoderbyteFirst choices, of fourteen modelsDevSkiller
Direct11
Paraphrase00
Comparative00
Budget-constrained501 against Coderbyte
Scale-constrained00
Negative101 against Coderbyte
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 Coderbyte and DevSkiller 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
Coderbyte DevSkiller first choice named as an alternative argued againstblank: not namedEach cell is one answer, Coderbyte on the left and DevSkiller on the right.

The direct prompt

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

Coderbyte first, DevSkiller not the choice

1 of 14 modelsDevSkiller was named in the answer but not as the choice, or not at all.
Kimi K2Coderbyte alternatives: HackerRank, TestGorilla

DevSkiller first, Coderbyte not the choice

1 of 14 modelsCoderbyte was named in the answer but not as the choice, or not at all.
Muse Glimmer 30BCodeSignal, DevSkiller, HackerRank alternatives: Codility

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Perplexity SonarCodeSignal alternatives: Coderbyte, DevSkiller
Grok 4.1 FastHackerRank alternatives: CodeSignal, CoderPad, Coderbyte, Codility
Llama 4 MaverickGoodfit alternatives: CodeSignal, Coderbyte, HackerRank
GLM 4.7 FlashXCodility, TestGorilla alternatives: CodeSignal, Coderbyte, HackerRank

Neither was named

8 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5HackerEarth, TestGorilla alternatives: CodeSignal
GPT-5.4 miniHackerRank alternatives: CodeSignal, Codility
Gemini 3.5 FlashCoderPad, Woven alternatives: CodeSignal
Mistral SmallGoodfit alternatives: HackerRank
DeepSeek V4 FlashCodility alternatives: CodeSignal, HackerRank
Qwen 3.7 FlashCodeSignal alternatives: CoderPad, Codility, TestGorilla, Voomer
MiniMax M2.5Codility alternatives: AssessHub, CoderPad, HackerRank
GPT-6 LunaCodeSignal Hire Grow alternatives: CoderPad, HackerRank

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
Coderbyte leads by twenty-two points.
Coderbyte22%#1 of 8
DevSkiller0%#– of 8
The full small business standing →
Mid-marketThe figures above
Coderbyte leads by nine points.
Coderbyte10%#4 of 9
DevSkiller2%#8 of 9
The full mid-market standing →
Enterprise
Coderbyte leads by eighteen points.
Coderbyte18%#2 of 8
DevSkiller0%#– of 8
The full enterprise standing →

What the models said about Coderbyte

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

“Frequently called out as one of the worst by candidates due to bugs ... "riddled with bugs"” Grok 4.1 Fast · negative prompt · hard negative
“Some platforms (like Coderbyte) have cheap base prices but charge add-ons for proctoring” DeepSeek V4 Flash · budget prompt · soft negative
“Best for Regular Hiring: Coderbyte ... unlimited plan offers the best value.” Kimi K2 · budget prompt · first choice
“For a small company watching cash flow, I'd start with Coderbyte's monthly plan.” GPT-6 Luna · budget prompt · first choice
“Coderbyte stands out as one of the best coding assessment platforms” Grok 4.1 Fast · budget prompt · first choice

What the models said about DevSkiller

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

“CodeSignal / DevSkiller are commonly recommended for 50-500 hires mid-market teams” Muse Glimmer 30B · direct prompt · first choice
“CodeSignal or DevSkiller is explicitly listed for *mid-market (50–500 hires)*” Perplexity Sonar · direct prompt · alternative
“Uses the RealLifeTesting" methodology... realistic code scenarios” GLM 4.7 FlashX · comparative 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.