Upfluence vs Modash
One of fourteen models named Upfluence first on the direct prompt; zero named Modash. Upfluence was named by fourteen of the fourteen models and Modash by twelve and Upfluence carries 44 labels and Modash 25, so the shares are not directly comparable.
Named in three categories this edition.
Named 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 influencer platforms page.
By framing
UpfluenceFirst choices, of fourteen modelsModash
Across every category in the November 2026 Edition, Upfluence and Modash were named in the same answer forty-one times, of the 119 answers naming Upfluence and the 66 naming Modash. In those answers Modash took the first choice six times and Upfluence two.
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 4.20 | | | | | | |
| 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 | | | | |
Upfluence Modash first choice named as an alternative argued againstblank: not namedEach cell is one answer, Upfluence on the left and Modash on the right.The direct prompt
Upfluence first, Modash not the choice
1 of 14 modelsModash was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 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
Upfluence7%#3 of 15Modash4%#7 of 15Upfluence2%#5 of 13Modash2%#10 of 13What the models said about Upfluence
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 to Avoid for Mid-Market B2B ... more B2C-oriented; up to 5-6x the cost” DeepSeek V4 Flash · direct prompt · hard negative
“Avoid Upfluence at all costs – this company is effectively a scam.” Muse Glimmer 30B · negative prompt · hard negative
“Enterprise platforms like Upfluence, Grin, CreatorIQ, Traackr, or Aspire ... often start at hundreds per month—better for bigger budgets.” Grok 4.20 · budget prompt · soft negative
“KALO IQ and Upfluence are the best choices due to their affordability, ease of use, and strong B2B features” Mistral Small · direct prompt · first choice
“For Serious Campaigns: AspireIQ, Upfluence, or Influencity (known for robust databases and analytics).” MiniMax M2.5 · negative prompt · first choice
“I recommend Upfluence as the top creator and influencer management tool.” Grok 4.20 · paraphrase prompt · first choice
What the models said about Modash
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Mid-market pricing → Influencity, Modash, Favikon (generally $150–$500/mo)” GLM 4.7 FlashX · budget prompt · soft negative
“For a mid-sized B2B company, Modash is the strongest, most sensible default choice.” DeepSeek V4 Flash · paraphrase prompt · first choice
“best overall pick is usually Modash” GPT-5.4 mini · budget prompt · first choice
“Choose tools (like HypeAuditor, Modash, or Phyllo-integrated tools) that analyze audience demographics and fake-follower percentages.” Gemini 3.5 Flash · negative 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.