Recognition and rewards
Asked as “employee recognition platform”, and as “employee rewards program software”, on behalf of a mid-market B2B company. 51 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
29%
Contested · Nectar 22%
29Bonusly22Nectar12Awardco37others
29% of first choices, contested.
Since September 2026↗new leaderNew leader since September 2026: Nectar (26%) replaces Bonusly (22% then, 23% now), 2 points clear, inside the 13-point floor.Nectar leads at 26%, replacing Bonusly, which led at 22% and stands at 23% now: 2 points clear, inside the floor, so the swap reads as unsettled.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.
Small business
Bonusly60%Clear leader▼−1Since September 2026: 60% → 59%, −1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketBonusly29%Contested↗new leaderNew leader since September 2026: Nectar (26%) replaces Bonusly (22% then, 23% now), 2 points clear, inside the 13-point floor.This pageEnterprise
Achievers39%Contested=heldSince September 2026: 38% → 38%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.Standing →
Bonusly60%Clear leader▼−1Since September 2026: 60% → 59%, −1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketBonusly29%Contested↗new leaderNew leader since September 2026: Nectar (26%) replaces Bonusly (22% then, 23% now), 2 points clear, inside the 13-point floor.This pageEnterpriseAchievers39%Contested=heldSince September 2026: 38% → 38%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.Standing →The standing
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. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01Bonusly29%7%57accepted challenger▲+1Since September 2026: 22% → 23%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.22% → 23%
Bonusly29%7%57accepted challenger▲+1Since September 2026: 22% → 23%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.22% → 23%02
Nectar22%4%45accepted challenger▲+4Since September 2026: 22% → 26%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.22% → 26%
Nectar22%4%45accepted challenger▲+4Since September 2026: 22% → 26%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.22% → 26%03
Awardco12%8%36accepted challenger▲+7Since September 2026: 7% → 14%, +7 points. Inside the 13-point floor: within noise. Read over the models both editions asked.7% → 14%
Awardco12%8%36accepted challenger▲+7Since September 2026: 7% → 14%, +7 points. Inside the 13-point floor: within noise. Read over the models both editions asked.7% → 14%04Achievers8%18%33accepted challenger▲+4Since September 2026: 5% → 9%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 9%
Achievers8%18%33accepted challenger▲+4Since September 2026: 5% → 9%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 9%05
Guusto6%8%13accepted challenger=heldSince September 2026: 5% → 5%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 5%
Guusto6%8%13accepted challenger=heldSince September 2026: 5% → 5%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 5%06
Motivosity2%3%30accepted challenger▼−5Since September 2026: 7% → 2%, −5 points. Inside the 13-point floor: within noise. Read over the models both editions asked.7% → 2%
Motivosity2%3%30accepted challenger▼−5Since September 2026: 7% → 2%, −5 points. Inside the 13-point floor: within noise. Read over the models both editions asked.7% → 2%Show the two products at 0%, ordered by negative rate
08
Workhuman0%9%23accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 0%
Workhuman0%9%23accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 0%07
Kudos0%4%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
Kudos0%4%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 13 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
All fifteen head-to-head pages: the top six products, each against each
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
0102030405060708Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Bonusly29%
Bonusly29%02Nectar22%
Nectar22%03Awardco12%
Awardco12%04Achievers8%
Achievers8%05Guusto6%
Guusto6%06Motivosity2%
Motivosity2%07Kudos0%
Kudos0%08Workhuman0%
Workhuman0%What they warned about
Two of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Mistral Small, DeepSeek V4 Flash, Llama 4 Maverick, Qwen 3.7 Flash, Kimi K2, GLM 4.7 FlashX, MiniMax M2.5 and Muse Glimmer 30B changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Achievers18%
6 of 33 labels negative · 4 of 14 models · 2 hard negative
“avoid or be cautious with Bonusly (SMB versions), Awardco, Achievers, and generic Slack/Teams integrations” Mistral Small, negative prompt
Bonusly7%
4 of 57 labels negative · 4 of 14 models · 1 hard negative
“Some AI models explicitly recommend avoiding SMB-focused "peer-shout-out" tools like Bonusly for large organizations” Mistral Small, negative prompt
Awardco8%
3 of 36 labels negative · 3 of 14 models · 3 hard negative
“Criticized for limited reward catalog flexibility (exclusively tied to Amazon Business), potential reward delivery delays, no free trial, and opaque pricing” Mistral Small, negative prompt
Clearview AI100%
2 of 2 labels negative · 2 of 14 models · 2 hard negative
“Avoid or be very cautious** with platforms that have been publicly tied to **mass‑scraping... (e.g., Clearview AI, Face++, SenseTime, Yitu, Kairos)” MiniMax M2.5, negative prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the fourteen in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
71 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 901 links across 173 sites, every framing counted. Ranked by the number of answers carrying the site or page. 18 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.
vendor site · Achievers40 answers · 63 citations · 14 models
vendor site · Tremendous39 answers · 40 citations · 11 models
vendor site · G237 answers · 66 citations · 13 models
vendor site · Workhuman37 answers · 39 citations · 13 models
vendor site · Awardco31 answers · 37 citations · 12 models
28 answers · 35 citations · 10 models
vendor site · Engagedly25 answers · 25 citations · 9 models
vendor site · Motivosity22 answers · 24 citations · 11 models
21 answers · 23 citations · 9 models
vendor site · ThriveSparrow20 answers · 21 citations · 8 models
vendor site · Kudos19 answers · 19 citations · 10 models
vendor site · Bonusly18 answers · 20 citations · 7 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
39 answers · 11 models
30 answers · 10 models
25 answers · 9 models
23 answers · 10 models
23 answers · 9 models
21 answers · 9 models
20 answers · 8 models
18 answers · 8 models
18 answers · 9 models
16 answers · 10 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best employee recognition platform”, “employee recognition platform”, “recognition platforms”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days | Best Google position |
|---|---|---|---|---|---|---|---|---|
| Bonusly bonusly.com | 29% | 176 | 14,800 | 75 | 44,595 | 21 | 2 | |
| Nectar nectarhr.com | 22% | 137 | 246,000 | 20,852 | 14,388 | 43 | 9 | |
| Awardco awardco.com | 12% | 124 | 27,100 | 303 | 32,927 | 14 | ||
| Matter matterapp.com | 10% | 46 | 90,500 | 151,595 | 21,353 | 1 | ||
| Achievers achievers.com | 8% | 107 | 6,600 | 69,832 | 295,924 | 5 | 4 | |
| Guusto guusto.com | 6% | 53 | 1,900 | 25 | 8,527 | |||
| Reward Gateway rewardgateway.com | 4% | 37 | 720 | 34 | 41,014 | 30 | ||
| Motivosity motivosity.com | 2% | 87 | 8,100 | 58 | 12,155 | $27 | 30 | |
| Perkbox perkbox.com | 2% | 10 | ||||||
| Recognize recognizeapp.com | 2% | 9 | 74,000 | 110,244 | 35,069 | 8 | 6 |
Google's top ten for the three searches hold 18 sites; 7 of them are among the sites the models cited here (achievers.com, bonusly.com, kudos.com, peoplemanagingpeople.com, selectsoftwarereviews.com, thrivesparrow.com, tremendous.com). The first result for “best employee recognition platform” is selectsoftwarereviews.com.
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.ShowHide
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ShowHideModelDirect“What is the best employee recognition platform for a mid-market B2B company?”Paraphrase“Which employee rewards program software would you recommend to a mid-sized B2B company?”Comparative“What are the top recognition platforms and how do they differ?”Budget-constrained“What is the best employee recognition platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating an employee recognition platform. What should we look at?”Negative“Which recognition platforms should I avoid or be cautious about?”
Claude Haiku 4.5AchieversAwardcoChangedno first choiceMatterno first choicenothing named
Five alternatives
Awardco, Bonusly, Motivosity, Nectar, WorkhumanThree alternatives
Bonusly, Motivosity, NectarFive alternatives
Assembly, Connecteam, Guusto, Kudos, NectarGPT-5.4 miniAchieversMotivosityChangedAchievers, BonuslyRecognizeno first choicenothing named
Six alternatives
Awardco, Bonusly, Motivosity, Nectar, Recognize, WorkhumanTwo alternatives
Awardco, WorkhumanThree alternatives
Kudos, Nectar, WorkhumanThree alternatives
Bonusly, Guusto, NectarGemini 3.5 FlashAwardcoNectarChangedagainst: O.C. Tanner Culture CloudAwardco, BonuslyGuustoagainst: Bonusly, Nectarno first choiceagainst: Guustonothing named
Four alternatives
Bonusly, Motivosity, Nectar, TeamflectThree alternatives
Awardco, Bonusly, TeamflectFive alternatives
Achievers, Kudos, Motivosity, Nectar, TeamflectFour alternatives
Connecteam, Kudoboard, Matter, Tap My BackThree alternatives
Awardco, Bonusly, MotivosityPerplexity SonarNectarAwardcoChangedno first choiceBonuslyno first choicenothing named
Four alternatives
Awardco, Bonusly, Kudos, Vantage CircleTwo alternatives
Achievers, MotivosityOne alternative
QarrotGrok 4.1 FastAwardcoagainst: AchieversAwardcoHeldno first choiceBonuslyagainst: Bucketlist Rewards, Nectar, Reward GatewayBonuslyagainst: Achievers, Bonusly, Workday Peakon
Two alternatives
Bonusly, NectarFour alternatives
Achievers, Bonusly, Motivosity, NectarThree alternatives
Assembly, Guusto, KudosTwo alternatives
Achievers, AwardcoMistral SmallNectarPerkbox, Reward GatewayChangedno first choiceBonusly, Matterno first choiceagainst: Achievers, Awardco, Bonusly, Generic Slack/Teams integrations
Two alternatives
Achievers, Reward GatewayThree alternatives
Connecteam, Empuls, QarrotOne alternative
CarooDeepSeek V4 FlashBonusly, NectarNectarChangedBonusly, MatterMatteragainst: Achievers, Awardco, Workhumanno first choiceagainst: AWS Rekognition, Azure Face API, Clearview AI, Meta's "Name Tag"
Four alternatives
Achievers, Kudos, Motivosity, WorkhumanFour alternatives
Achievers, Awardco, Bonusly, PerkboxSeven alternatives
Achievers, Awardco, Kudoboard, Kudos, Motivosity, Nectar, WorkhumanOne alternative
BonuslyLlama 4 Maverickno first choiceMatterChangedno first choiceNectarno first choicenothing named
Two alternatives
Awardco, TerryberryTwo alternatives
Kudos, XoxodayQwen 3.7 FlashNectarAchieversChangedBonusly, WorkhumanBonuslyno first choiceagainst: ESLinsider, ITTT, TTA/The TEFL Academy, Udemy Business
Two alternatives
Achievers, BonuslyTwo alternatives
Bonusly, Reward GatewayThree alternatives
Culture Amp, Kudos, Vantage CircleSix alternatives
Kudos, Microsoft Teams, Motivote, Slack, ThriveSponge, ZappyAppKimi K2AwardcoBonusly, NectarChangedBonusly, NectarBonuslyno first choiceagainst: Achievers, Workhumanagainst: Achievers, Bonusly, Kudos
Three alternatives
Bonusly, Kudos, NectarThree alternatives
Awardco, Guusto, MotivosityThree alternatives
Assembly, Awardco, WorkTangoThree alternatives
Guusto, Motivosity, NectarGLM 4.7 FlashXBRAVONectarChangedno first choiceMatterno first choiceagainst: Awardco
Four alternatives
Achievers, Nectar, Recnice, WorkhumanFour alternatives
Assembly, Awardco, Bonusly, WorkTangoTwo alternatives
Assembly, BonuslyMiniMax M2.5BonuslyAchievers, NectarChangedno first choiceBonusly, Guusto, Nectarno first choiceagainst: Amazon Rekognition, Clearview AI, Face++, Google Cloud Vision API, IBM Watson Visual Recognition, Kairos, Microsoft Azure Face, OpenCV DNN module, SenseTime, Yitu Technology
Four alternatives
Kudos, Nectar, Workhuman, Xoxoday EmpulsTwo alternatives
Qarrot, Reward GatewayGPT-6 LunaBonuslyBonuslyHeldno first choiceBonuslyagainst: Motivosityno first choicenothing named
Two alternatives
Awardco, MotivosityTwo alternatives
Awardco, MotivosityMuse Glimmer 30BBonuslyBonusly, Reward GatewayChangedno first choiceGuusto, HeyTaco Classicno first choiceagainst: AI Global Media / Acquisition International, Globee Awards, Stevie Awards, The Women Leaders Magazine, WCBRB Worldwide Certified Business Review Board, cioreview.com, logisticstechoutlook.com, medtechbusinessreview.com, theciotimes.com
Six alternatives
Achievers, HR Cloud's Workmates, Kudos, Motivosity, Nectar, WorkhumanThree alternatives
Kudos, Motivosity, NectarTen alternatives
Achievers, Awardco, Bonusly, Guusto, Kudos, Matter, Motivosity, Nectar, O.C. Tanner Culture Cloud, WorkhumanOne alternative
BonuslyBold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Eighty-four rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:36 | yes | 9 | 8 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:30 | yes | 4 | 6 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:45 | yes | 15 | 28 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 10:49 | yes | 18 | 2 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:36 | yes | 21 | 14 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:47 | yes | 8 | 5 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:16 | yes | 22 | 20 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:55 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:44 | yes | 12 | 43 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:02 | yes | 9 | 21 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:03 | yes | 13 | 68 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:00 | yes | 14 | 27 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:29 | yes | 3 | 17 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:10 | yes | 13 | 20 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:30 | yes | 16 | 8 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:05 | yes | 4 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:52 | yes | 10 | 18 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 07:47 | yes | 19 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:18 | yes | 21 | 8 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:58 | yes | 7 | 5 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:41 | yes | 23 | 27 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:25 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:09 | yes | 10 | 33 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:44 | yes | 13 | 22 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:17 | yes | 19 | 131 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:01 | yes | 11 | 29 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:54 | yes | 3 | 14 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:33 | yes | 21 | 29 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:13 | no | 0 | 2 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:10 | yes | 5 | 21 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:09 | yes | 11 | 23 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 11:44 | yes | 19 | 4 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:02 | yes | 22 | 19 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:22 | yes | 10 | 9 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:38 | yes | 17 | 48 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:18 | yes | 5 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:53 | no | 0 | 31 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:25 | yes | 10 | 25 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:33 | yes | 25 | 81 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:27 | no | 0 | 14 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:48 | yes | 6 | 19 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:07 | yes | 13 | 32 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:47 | yes | 9 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:42 | yes | 3 | 4 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:41 | yes | 16 | 24 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 11:45 | yes | 20 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:33 | yes | 23 | 7 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 12:01 | yes | 7 | 7 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:19 | yes | 19 | 18 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:08 | yes | 5 | 3 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:25 | no | 0 | 30 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:39 | yes | 15 | 18 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:07 | yes | 12 | 17 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:37 | yes | 5 | 14 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 07:52 | yes | 2 | 12 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:44 | yes | 21 | 30 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:03 | no | 0 | 7 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:01 | no | 0 | 6 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 07:59 | no | 0 | 15 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 10:29 | yes | 18 | 4 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:49 | yes | 13 | 11 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:02 | no | 0 | 7 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:17 | yes | 18 | 49 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:26 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:43 | no | 0 | 28 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:10 | yes | 9 | 42 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:04 | yes | 15 | 46 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:04 | no | 0 | 8 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 10:53 | yes | 2 | 14 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:33 | yes | 15 | 24 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:29 | no | 0 | 2 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:20 | yes | 4 | 4 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:18 | yes | 15 | 51 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 07:37 | yes | 19 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:40 | yes | 24 | 7 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:48 | yes | 5 | 5 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:13 | yes | 10 | 31 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:33 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:32 | yes | 10 | 35 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:56 | yes | 23 | 23 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:11 | yes | 20 | 31 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:53 | no | 0 | 43 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:08 | no | 0 | 2 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:05 | yes | 23 | 37 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
Bucketlist read as Bucketlist Rewards
O.C. Tanner read as O.C. Tanner Culture Cloud
Unresolved, counted raw
AI Global Media / Acquisition International
AWS Rekognition
Amazon Rekognition
Apple/Google (on‑device)
Apple’s Secure Enclave
Azure Face API
BioID
Bonus (now part of HR tech suites)
CELTA
Coqui
CultureBot
ESLinsider
EngageWith (by Firstup)
Face++ (Megvii)
FaceTec
Generic Slack/Teams integrations
Glints
Globee Awards
Google Cloud Vision API
HR Cloud Workmates
HR Cloud's Workmates
HeyTaco Classic
IBM Watson Visual Recognition
ITTT
Kairos
Kaldi
MTCNN
Meta's "Name Tag"
Microsoft Azure Face
MobileNet
Motivote
Mozilla DeepSpeech
Octanner
OpenCV DNN module
PyTorch Mobile
SenseTime
Stevie Awards
TTA/The TEFL Academy
TensorFlow Lite
The Women Leaders Magazine
ThriveSponge
WCBRB Worldwide Certified Business Review Board
Windows Hello
Yitu Technology
ZappyApp
cioreview.com
dlib
logisticstechoutlook.com
medtechbusinessreview.com
theciotimes.com
Discontinued, still offered
No shut-down product was recommended here.