| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Digital employee experience | IT operations and endpoint | 0% | 52 of 219 | 36% | 14 | criticized challenger |
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 1 | 0 | 1 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.1 Fast | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 1 | 1 | 0 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 1 | 1 | 2 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 2 | 0 | 2 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 1 | 1 |
| MiniMax M2.5 | 0 | 1 | 0 | 2 | 3 |
| GPT-6 Luna | 0 | 0 | 0 | 1 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
Verbatim evidence the judge attached to positive labels.
“Stick to well-known, audited DEXs like Uniswap, dYdX, or SushiSwap” Mistral Small · DEX · negative prompt · first choice
“SushiSwap is also commonly listed for dollar-cost averaging and multi-chain support” Perplexity Sonar · DEX · budget prompt · alternative
“For community-driven ecosystem: SushiSwap” MiniMax M2.5 · DEX · comparative prompt · alternative
“SushiSwap for multi-chain flexibility” Mistral Small · DEX · comparative prompt · alternative
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“The 2020 incident was a classic "chef Nomi" exit-scam” MiniMax M2.5 · DEX · negative prompt · hard negative
“Its team documented a RouteProcessor2 exploit in April 2023 involving token approvals... The incident alone doesn’t show that Sushi’s current contracts are compromised.” GPT-6 Luna · DEX · negative prompt · soft negative
“its history of security incidents and codebase complexities make it worth additional scrutiny” DeepSeek V4 Flash · DEX · negative prompt · soft negative
“Various exploits (2023–2024), ranging from ~$1.8M to $11M in losses.” GLM 4.7 FlashX · DEX · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 25 of the 28 answers that named SushiSwap and are not a share of its labels.
No domain is on file for SushiSwap, so its own site is not marked.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read October 5, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when SushiSwap's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as SushiSwap, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for SushiSwap by email, built from the raw record of the edition. It shows: