MGID vs Sharethrough
Zero of fourteen models named MGID first on the direct prompt; zero named Sharethrough. MGID was named by fourteen of the fourteen models and Sharethrough by ten and MGID carries 46 labels and Sharethrough 15, so the shares are not directly comparable.
Named in two categories this edition.
Sharethrough
accepted challengerNamed in one category 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 native and content advertising page.
By framing
MGIDFirst choices, of fourteen modelsSharethroughAcross every category in the October 2026 Edition, MGID and Sharethrough were named in the same answer twenty-seven times, of the 112 answers naming MGID and the 47 naming Sharethrough. In those answers Sharethrough took the first choice three times and MGID six.
Every model, every framing
| Model | DirectSH | ParaphraseSH | ComparativeSH | Budget-constrainedSH | Scale-constrainedSH | NegativeSH |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | SH | | | |||
| Perplexity Sonar | SH | | | |||
| Grok 4.1 Fast | SH | | | SH | SH | |
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | | ||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | SH | | | ||
| Kimi K2 | | SH | | |||
| GLM 4.7 FlashX | SH | SH | | | ||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | | | |
MGIDSH Sharethrough first choice named as an alternative argued againstblank: not namedEach cell is one answer, MGID on the left and Sharethrough on the right.The direct prompt
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
13 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
MGID29%#1 of 8What the models said about MGID
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What I'd Avoid or Use with Caution - MGID – Heavy entertainment/clickbait inventory” DeepSeek V4 Flash · paraphrase prompt · hard negative
“High-volume, low-CPC affiliate-style networks such as MGID and RichAds: ... they can need substantial filtering to avoid invalid traffic and low-quality placements.” Perplexity Sonar · negative prompt · soft negative
“Many advertisers limit MGID, Revcontent and similar performance networks to direct-response use cases with strict creative compliance” Muse Glimmer 30B · negative prompt · soft negative
“For the tightest budget, MGID appears to be the best starting point due to its lower CPCs and accessibility for beginners.” MiniMax M2.5 · budget prompt · first choice
“The Overall Best Choice: MGID ... most accessible and cost-effective native ad network for small businesses” Gemini 3.5 Flash · budget prompt · first choice
“MGID stands out as the best native advertising platform for companies with a limited budget.” Grok 4.1 Fast · budget prompt · first choice
What the models said about Sharethrough
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“prioritize platforms like StackAdapt (omnichannel) or Sharethrough (brand-safe in-feed) per recent analyses” Grok 4.1 Fast · scale prompt · first choice
“Leverage TripleLift or Sharethrough to dynamically transform standard assets into native formats” Gemini 3.5 Flash · comparative prompt · alternative
“If your goal is premium brand perception and engagement, consider Sharethrough or Nativo.” Qwen 3.7 Flash · 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.