HackerEarth vs DevSkiller
One of fourteen models named HackerEarth first on the direct prompt; one named DevSkiller. HackerEarth was named by eleven of the fourteen models and DevSkiller by nine and HackerEarth carries 15 labels and DevSkiller 10, so the shares are not directly comparable.
Named in three categories this edition.
Named in two categories this edition.
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
HackerEarthFirst choices, of fourteen modelsDevSkiller
Across every category in the October 2026 Edition, HackerEarth and DevSkiller were named in the same answer seven times, of the 44 answers naming HackerEarth and the 20 naming DevSkiller. In those answers DevSkiller took the first choice zero times and HackerEarth one.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | |
HackerEarth DevSkiller first choice named as an alternative argued againstblank: not namedEach cell is one answer, HackerEarth on the left and DevSkiller on the right.The direct prompt
HackerEarth first, DevSkiller not the choice
1 of 14 modelsDevSkiller was named in the answer but not as the choice, or not at all.DevSkiller first, HackerEarth not the choice
1 of 14 modelsHackerEarth was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.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
HackerEarth2%#– of 8DevSkiller0%#– of 8HackerEarth7%#7 of 9DevSkiller2%#8 of 9HackerEarth2%#5 of 8DevSkiller0%#– of 8What the models said about HackerEarth
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Similar to HackerRank in being low-cost, but with less-modern technology.” Llama 4 Maverick · negative prompt · soft negative
“Weaker auto-cheat detection per tests.” Grok 4.1 Fast · comparative prompt · soft negative
“For mid-market B2B companies specifically, HackerEarth and TestGorilla stand out for their balance of features, pricing accessibility, and ATS integration compatibility” Claude Haiku 4.5 · direct prompt · first choice
“The best coding assessment platform for a company with a limited budget is HackerEarth, which offers coding tests and live technical interviews on a modest budget.” Llama 4 Maverick · budget prompt · first choice
“HackerEarth or TestGorilla would be the best starting points due to their affordability” MiniMax M2.5 · 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
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.