GRIN vs Aspire
Zero of twelve models named GRIN first on the direct prompt; zero named Aspire. GRIN was named by eleven of the twelve models and Aspire by eleven and GRIN carries 38 labels and Aspire 31, so the shares are not directly comparable.
Named in one category 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 twelve 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
GRINFirst choices, of twelve modelsAspire
Across every category in the September 2026 Edition, GRIN and Aspire were named in the same answer sixty-nine times, of the 108 answers naming GRIN and the 80 naming Aspire. In those answers Aspire took the first choice three times and GRIN eight.
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 | | |
GRIN Aspire first choice named as an alternative argued againstblank: not namedEach cell is one answer, GRIN on the left and Aspire on the right.The direct prompt
Neither was the first choice, one was named
3 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 of 12 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
What the models said about GRIN
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Do not sign an expensive annual contract with a legacy B2C influencer platform (like GRIN or CreatorIQ).” Gemini 3.5 Flash · direct prompt · hard negative
“What to skip: GRIN, Aspire, CreatorIQ, and Upfluence are excellent but built for B2C/e-commerce” DeepSeek V4 Flash · direct prompt · hard negative
“GRIN | Built for DTC/ecommerce (Shopify-centric)... don't map well to B2B sales cycles” Kimi K2 · paraphrase prompt · hard negative
“GRIN/Aspire focus on end-to-end workflows like gifting and payments (ideal for scaling DTC)... Choose based on your scale (e.g., GRIN for 50+ creators)” Grok 4.1 Fast · comparative prompt · first choice
“The top influencer marketing platforms include GRIN, Upfluence, and CreatorIQ for comprehensive, AI-driven campaign management.” Llama 4 Maverick · comparative prompt · first choice
“the recommended creator and influencer management tool for a mid-sized B2B company is GRIN” Llama 4 Maverick · paraphrase prompt · first choice
What the models said about Aspire
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Platforms to AVOID on a Limited Budget - Aspire, Upfluence, CreatorIQ \u2014 typically $2,000+ per month” DeepSeek V4 Flash · budget prompt · hard negative
“If you value your time, budget, or team's productivity, steer clear.” Qwen 3.7 Flash · negative prompt · hard negative
“Avoid: Enterprise tools like Aspire/Upfluence ($2K+/mo)” Grok 4.1 Fast · budget prompt · hard negative
“All-in-one mid-market/DTC | GRIN, Aspire, Upfluence | ... sweet spot for your size” DeepSeek V4 Flash · scale prompt · first choice
“My Top Recommendation: Aspire (formerly AspireIQ)” Kimi K2 · paraphrase prompt · first choice
“Top Recommendation: Aspire (formerly AspireIQ)” GLM 4.7 FlashX · paraphrase prompt · first choice
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.