Four of fourteen models named Chargeblast first on the direct prompt; two named Chargeflow. Chargeblast was named by thirteen of the fourteen models and Chargeflow by thirteen and Chargeblast carries 41 labels and Chargeflow 40, so the shares are not directly comparable.
Named in one category 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 chargeback management page.
Across every category in the November 2026 Edition, Chargeblast and Chargeflow were named in the same answer sixty-six times, of the 107 answers naming Chargeblast and the 173 naming Chargeflow. In those answers Chargeflow took the first choice eighteen times and Chargeblast eleven.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Most mainstream chargeback tools (Chargeblast, Chargeflow, Justt) are built for ecommerce merchants dealing with consumer card disputes. For a mid-market B2B company, those tools won't fit your workflows.” MiniMax M2.5 · direct prompt · soft negative
“Chargeblast is listed as 5.0/5 on Capterra/GetApp with only 6 reviews... Small sample sizes mean ratings can be inflated by early adopters.” Muse Glimmer 30B · negative prompt · soft negative
“Chargeblast users on Trustpilot praised the platform but complained about slow support and chaotic onboarding with billing problems.” DeepSeek V4 Flash · negative prompt · soft negative
“Primary Focus: Prevention via pre-dispute notifications... Best For: Merchants looking to proactively lower their dispute rates” Qwen 3.7 Flash · comparative prompt · first choice
“Chargeblast offers the best balance of advanced features (automation/AI) and price ($29/mo) for growing small businesses.” Qwen 3.7 Flash · budget prompt · first choice
“likely to be one of the top-rated options listed in the search results, such as Chargeblast, ChargebackStop, or Midigator” Llama 4 Maverick · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“here are the chargeback management tools you should be cautious about or avoid: ... Chargeflow has mixed reviews with significant concerns” GLM 4.7 FlashX · negative prompt · hard negative
“This app is low quality AI that malfunctions. DO NOT USE! Don't be stupid and trust Chargeflow with your revenue.” Muse Glimmer 30B · negative prompt · hard negative
“Consider avoiding Chargeflow (due to the Trustpilot breach)” DeepSeek V4 Flash · negative prompt · hard negative
“E-commerce stores built on standard platforms (Shopify, WooCommerce, Stripe) benefit most from plug-and-play AI platforms like Chargeflow.” Gemini 3.5 Flash · comparative prompt · first choice
“Chargeflow is a reasonable low-upfront-cost option: it advertises no subscription for dispute recovery and charges 25% of chargebacks it wins” GPT-6 Luna · budget prompt · first choice
“Chargeflow uses a success-based pricing model, charging 25% of recovered funds with no monthly fee and a 4x ROI guarantee” Claude Haiku 4.5 · budget 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.