Interview scheduling
Asked as “interview scheduling software”, and as “recruiting coordination tool”, on behalf of a mid-market B2B company. 64 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
20%
Contested · GoodTime 16%
20Calendly16GoodTime11Lever53others
20% of first choices, contested.
Since September 2026▲+1Since September 2026: 21% → 21%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.Calendly held the lead, +1 point on 21%, inside the 13-point floor.
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
Calendly41%Clear leader▼−23Since September 2026: 62% → 38%, −23 points. Past the 13-point floor: movement. Read over the models both editions asked.Standing →Mid-marketCalendly20%Contested▲+1Since September 2026: 21% → 21%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.This pageEnterprise
GoodTime55%Clear leader▼−5Since September 2026: 56% → 51%, −5 points. Inside the 13-point floor: within noise. Read over the models both editions asked.Standing →
Calendly41%Clear leader▼−23Since September 2026: 62% → 38%, −23 points. Past the 13-point floor: movement. Read over the models both editions asked.Standing →Mid-marketCalendly20%Contested▲+1Since September 2026: 21% → 21%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.This pageEnterpriseGoodTime55%Clear leader▼−5Since September 2026: 56% → 51%, −5 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
01Calendly20%33%55criticized challenger▲+1Since September 2026: 21% → 21%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.21% → 21%
Calendly20%33%55criticized challenger▲+1Since September 2026: 21% → 21%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked.21% → 21%02GoodTime16%18%49accepted challenger▲+7Since September 2026: 9% → 16%, +7 points. Inside the 13-point floor: within noise. Read over the models both editions asked.9% → 16%
GoodTime16%18%49accepted challenger▲+7Since September 2026: 9% → 16%, +7 points. Inside the 13-point floor: within noise. Read over the models both editions asked.9% → 16%03
Lever11%9%34accepted challenger▲+3Since September 2026: 8% → 11%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked.8% → 11%
Lever11%9%34accepted challenger▲+3Since September 2026: 8% → 11%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked.8% → 11%04
ModernLoop9%8%26accepted challenger▼−6Since September 2026: 15% → 9%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 9%
ModernLoop9%8%26accepted challenger▼−6Since September 2026: 15% → 9%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 9%05
Greenhouse8%14%42accepted challenger▼−6Since September 2026: 13% → 7%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.13% → 7%
Greenhouse8%14%42accepted challenger▼−6Since September 2026: 13% → 7%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.13% → 7%06
Cal.com8%0%21accepted challenger▲+3Since September 2026: 4% → 7%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked.4% → 7%
Cal.com8%0%21accepted challenger▲+3Since September 2026: 4% → 7%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked.4% → 7%07
Koalendar6%0%10accepted challenger▼−6Since September 2026: 11% → 5%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.11% → 5%
Koalendar6%0%10accepted challenger▼−6Since September 2026: 11% → 5%, −6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.11% → 5%08
Ashby2%0%18accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 2%
Ashby2%0%18accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 2%09
Doodle2%38%13criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 2%
Doodle2%38%13criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 2%Show the three products at 0%, ordered by negative rate
12
HireVue0%40%10criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
HireVue0%40%10criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%11
YouCanBookMe0%21%14accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 0%
YouCanBookMe0%21%14accepted challenger▼−2Since September 2026: 2% → 0%, −2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 0%10
Paradox0%10%20accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
Paradox0%10%20accepted 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 twenty-eight head-to-head pages: the top eight products, each against each
Calendly vs GreenhouseCalendly vs Cal.comCalendly vs KoalendarCalendly vs AshbyGoodTime vs LeverGoodTime vs ModernLoopGoodTime vs GreenhouseGoodTime vs Cal.comGoodTime vs KoalendarGoodTime vs AshbyLever vs ModernLoopLever vs GreenhouseLever vs Cal.comLever vs KoalendarLever vs AshbyModernLoop vs GreenhouseModernLoop vs Cal.comModernLoop vs KoalendarModernLoop vs AshbyGreenhouse vs Cal.comGreenhouse vs KoalendarGreenhouse vs AshbyCal.com vs KoalendarCal.com vs AshbyKoalendar vs Ashby
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 →
010203040506070809101112Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Calendly20%
Calendly20%02GoodTime16%
GoodTime16%03Lever11%
Lever11%04ModernLoop9%
ModernLoop9%05Greenhouse8%
Greenhouse8%06Cal.com8%
Cal.com8%07Koalendar6%
Koalendar6%08Ashby2%
Ashby2%09Doodle2%
Doodle2%10Paradox0%
Paradox0%11YouCanBookMe0%
YouCanBookMe0%12HireVue0%
HireVue0%What they warned about
One of fourteen models held their first choice under the paraphrase. 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, GPT-6 Luna 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.
Calendly33%
18 of 55 labels negative · 10 of 14 models · 1 hard negative
“Avoid using general-purpose tools (like Doodle or basic free tiers of Calendly) for serious hiring workflows” DeepSeek V4 Flash, negative prompt
Doodle38%
5 of 13 labels negative · 5 of 14 models · 2 hard negative
“Why to avoid them: They are designed for small groups and ad-hoc meetings. They lack the necessary integration with Applicant Tracking Systems (ATS)” GLM 4.7 FlashX, negative prompt
GoodTime18%
9 of 49 labels negative · 5 of 14 models · 1 hard negative
“What to avoid if on a tight budget: Enterprise-level tools like **GoodTime** or **ModernLoop**” DeepSeek V4 Flash, budget prompt
Greenhouse14%
6 of 42 labels negative · 5 of 14 models · 1 hard negative
“Avoid full ATS suites like Lever/Greenhouse unless free tiers suffice, as they're pricier for scale.” Grok 4.1 Fast, budget 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
80 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. 1028 links across 217 sites, every framing counted. Ranked by the number of answers carrying the site or page. 4 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 · Lever49 answers · 67 citations · 13 models
vendor site · Cal.com37 answers · 47 citations · 11 models
34 answers · 34 citations · 12 models
vendor site · Sapia.ai28 answers · 31 citations · 10 models
vendor site · Metaview26 answers · 31 citations · 11 models
26 answers · 30 citations · 10 models
vendor site · G226 answers · 27 citations · 10 models
vendor site · GoodTime24 answers · 29 citations · 13 models
vendor site · Koalendar23 answers · 28 citations · 10 models
vendor site · FirstHR21 answers · 21 citations · 9 models
vendor site · Intervue20 answers · 22 citations · 8 models
vendor site · Pin19 answers · 29 citations · 10 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.
41 answers · 12 models
33 answers · 10 models
32 answers · 12 models
26 answers · 10 models
23 answers · 10 models
23 answers · 10 models
23 answers · 9 models
21 answers · 9 models
21 answers · 10 models
19 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 interview scheduling software”, “interview scheduling software”, “interview scheduling tools”. 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 |
|---|---|---|---|---|---|---|---|---|
| Calendly calendly.com | 20% | 240 | 368,000 | 2,824 | 292,858 | $34 | 5 | |
| GoodTime goodtime.io | 16% | 149 | 135,000 | 614 | 2,435 | $1,627 | 7 | 4 |
| Lever lever.co | 11% | 275 | 60,500 | 24,663 | 189,552 | $657 | 7 | |
| ModernLoop modernloop.com | 9% | 87 | 880 | 7 | 410 | |||
| Greenhouse greenhouse.com | 8% | 419 | 301,000 | 52,273 | 62,551 | $5,545 | 74 | |
| Cal.com cal.com | 8% | 82 | 2,900 | 100 | 439,943 | 3 | ||
| Koalendar koalendar.com | 6% | 31 | 2,400 | 32 | 1,332,699 | 9 | ||
| candidate.fyi candidate.fyi | 3% | 35 | 210 | 0 | 494 | 10 | 1 | |
| Workable workable.com | 3% | 308 | 14,800 | 520 | 698,499 | $14,562 | 77 | |
| Ashby ashbyhq.com | 2% | 160 | 49,500 | 3,112 | 216,820 | 16 |
Google's top ten for the three searches hold 13 sites; 3 of them are among the sites the models cited here (goodtime.io, peoplemanagingpeople.com, selectsoftwarereviews.com). The first result for “best interview scheduling software” is candidate.fyi.
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 interview scheduling software for a mid-market B2B company?”Paraphrase“Which recruiting coordination tool would you recommend to a mid-sized B2B company?”Comparative“What are the top interview scheduling tools and how do they differ?”Budget-constrained“What is the best interview scheduling software for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating an interview scheduling software. What should we look at?”Negative“Which interview scheduling tools should I avoid or be cautious about?”
Claude Haiku 4.5ModernLoopLeverChangedGoodTimeCalendly, Koalendarno first choiceagainst: Acuity Scheduling, Calendly, Greenhouse, HireVue, TimeTap, YouCanBookMe
Three alternatives
Ashby, GoodTime, GreenhouseFour alternatives
JazzHR, Manatal, Recruitee, WorkableSeven alternatives
Calendly, Greenhouse, Homebase, Joveo, Koalendar, VidCruiter, monday AI WorkspaceThree alternatives
Setmore, Sign In Scheduling, YouCanBookMeGPT-5.4 miniAshbyAvatureChangedno first choiceCalendly, Doodleno first choicenothing named
Three alternatives
Calendly Sales, GoodTime, PreludeSix alternatives
Calendly, GoodTime, HireVue, ModernLoop, Paradox, PreludeTwo alternatives
GoodTime, WorkableGemini 3.5 FlashModernLoopagainst: Calendly, Greenhouse, Levercandidate.fyiChangedagainst: Calendly, GoodTimeGoodTime, ModernLoopCal.comagainst: GoodTime, ModernLoopGoodTime, ModernLoop, candidate.fyiagainst: Calendlyagainst: Calendly, GoodTime, Google Calendar Appointment Schedules, Paradox, YouCanBookMe
Three alternatives
Ashby, Calendly’s Prelude, GoodTimeThree alternatives
Ashby, Gem Scheduling, ModernLoopFour alternatives
Calendly, Paradox, YouCanBookMe, candidate.fyiSix alternatives
Calendly, Google Calendar "Appointment Schedules", Koalendar, Microsoft Bookings, TidyCal, YouCanBookMeFive alternatives
Arrange, Ashby, Cronofy, Greenhouse, ParadoxPerplexity SonarGoodTimeLeverChangedno first choiceagainst: PicktimeCal.com, Calendlyno first choicenothing named
Three alternatives
Calendly Sales, Greenhouse, ModernLoopTwo alternatives
Gem, PinSix alternatives
Calendly, GoodTime, Greenhouse, ModernLoop, Paradox, VidCruiterTwo alternatives
Doodle, YouCanBookMeGrok 4.1 FastCalendlyWorkableChangedCal.com, CalendlyKoalendaragainst: Greenhouse, LeverGoodTimeagainst: Calendly, SavvyCal, YouCanBookMeagainst: Calendly, Doodle, Picktime, Vyte
Three alternatives
Chili Piper, Greenhouse, LeverTwo alternatives
Greenhouse, LeverSeven alternatives
Acuity Scheduling, GoodTime, ModernLoop, Paradox, Prelude, SavvyCal, YouCanBookMeFour alternatives
Calendly, Microsoft Bookings, Setmore, YouCanBookMeTwo alternatives
Lever, VidCruiterMistral SmallGreenhouse, LeverRecruitee, WorkableChangedCalendlyGoogle Calendar, Microsoft Bookingsno first choiceagainst: Doodle, HireVue, VidCruiter
One alternative
Cal.comThree alternatives
Acuity Scheduling, Harver, Sapia.aiTwo alternatives
Calendly, SetmoreDeepSeek V4 FlashCal.com, Calendlyagainst: GoodTimeGreenhouseChangedCal.comCal.com, Setmoreagainst: GoodTime, ModernLoopGoodTimeagainst: Calendlyagainst: Calendly, Doodle, GoodTime, Yodel.io
Two alternatives
Greenhouse, LeverThree alternatives
GoodTime, Jobvite, LeverSix alternatives
Calendly, GoodTime, Greenhouse, HireVue, ModernLoop, ParadoxThree alternatives
Calendly, Doodle, KoalendarFour alternatives
Cronofy, ModernLoop, Paradox, WhenAvailableLlama 4 MaverickLeverno first choiceChangedno first choiceCalendlyno first choicenothing named
Four alternatives
Cal.com, Greenhouse, Joveo, Workforce NowOne alternative
Cal.comQwen 3.7 FlashGoodTimeagainst: Calendly, GreenhouseCalendlyChangedagainst: Workday RecruitingGoodTime, ModernLoopagainst: Greenhouse, LeverCalendlyGem, GoodTimeagainst: Calendly, Doodleagainst: Calendly
Two alternatives
Ashby, LeverThree alternatives
Greenhouse, Jobvite, LeverTwo alternatives
Calendly, ParadoxThree alternatives
Google Calendar "Appointment Schedules", YouCanBookMe, Zoho RecruitTwo alternatives
Greenhouse, LeverKimi K2ModernLoopagainst: PreludeGreenhouseChangedagainst: Workday RecruitingGoodTime, ModernLoopCalendlyGoodTimeagainst: Calendly, GoodTime, Greenhouse, HireVue
Five alternatives
Ashby, Calendly, GoodTime, Greenhouse, LeverOne alternative
LeverFour alternatives
Calendly, Greenhouse, Lever, ParadoxThree alternatives
Breezy HR, GoodTime, YouCanBookMeThree alternatives
Calendly, Lever, ModernLoopGLM 4.7 FlashXCalendlyagainst: Acuity SchedulingGreenhouseChangedGoodTimeCalendly, KoalendarGoodTimeagainst: Calendly Salesagainst: Calendly, Doodle, GoodTime, Paradox
Three alternatives
GoodTime, Greenhouse, LeverThree alternatives
Cal.com, GoodTime, LeverThirteen alternatives
Ashby, Calendly, Gem, HireVue, Homebase, Jobma, Joveo, Lever, Microsoft Bookings, ModernLoop, Paradox, Setmore, candidate.fyiThree alternatives
FluentBooking, Homebase, YouCanBookMeThree alternatives
Gem Scheduling, Lever, candidate.fyiMiniMax M2.5LeverLeverHeldCal.com, Calendlyagainst: Google Calendar appointment slotsBizBot, CalendlyGoodTime, ModernLoopagainst: Calendlyagainst: Calendly, GoodTime, Recruiterflow
Three alternatives
Cal.com, Chili Piper, GreenhouseThree alternatives
Greenhouse, Workable, Zoho RecruitTwo alternatives
Acuity Scheduling, Cal.comGPT-6 LunaModernLoopGoodTimeChangedGoodTime, ModernLoopagainst: PreludeCalendlyno first choiceagainst: Calendly
Two alternatives
GoodTime, GreenhouseThree alternatives
Ashby, Greenhouse, ParadoxFive alternatives
Ashby, Calendly, Cronofy, Greenhouse, ParadoxOne alternative
Google CalendarMuse Glimmer 30BGreenhouse, JobviteLeverChangedGoodTimeCal.com, Koalendarno first choiceagainst: Calendly, HireVue
Two alternatives
GoodTime, candidate.fyiThree alternatives
Greenhouse, Jobvite, RecruiteeFive alternatives
Calendly, Greenhouse, ModernLoop, Paradox, VidCruiterOne alternative
CalendlyBold 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 10:33 | yes | 9 | 8 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:27 | yes | 2 | 7 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:26 | yes | 19 | 22 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 09:11 | yes | 18 | 4 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:19 | yes | 22 | 9 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:48 | yes | 5 | 3 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:57 | yes | 23 | 24 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:18 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:46 | yes | 12 | 62 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:54 | yes | 12 | 22 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:57 | yes | 21 | 38 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:59 | yes | 10 | 33 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:46 | yes | 3 | 13 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:21 | yes | 12 | 23 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:14 | yes | 18 | 9 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:47 | yes | 2 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:18 | yes | 14 | 62 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 10:41 | yes | 20 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:13 | yes | 20 | 11 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:54 | yes | 6 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:53 | yes | 23 | 18 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:10 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:29 | yes | 5 | 31 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:38 | yes | 10 | 31 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:19 | yes | 19 | 285 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:09 | yes | 17 | 31 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:01 | yes | 3 | 10 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:26 | yes | 15 | 25 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:17 | yes | 9 | 8 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:22 | yes | 6 | 8 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 07:53 | yes | 18 | 24 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 09:27 | yes | 27 | 6 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:34 | yes | 19 | 9 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:54 | yes | 8 | 8 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 08:43 | yes | 17 | 35 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:37 | yes | 5 | 1 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:35 | yes | 14 | 30 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:59 | yes | 21 | 37 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:49 | yes | 10 | 31 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:37 | yes | 20 | 87 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 10:12 | yes | 8 | 21 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:38 | yes | 20 | 36 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:10 | yes | 9 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:05 | yes | 4 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:17 | yes | 14 | 61 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 09:01 | yes | 20 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 07:29 | yes | 15 | 8 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:08 | yes | 5 | 4 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:04 | yes | 22 | 32 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:40 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:57 | yes | 5 | 24 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:57 | yes | 10 | 21 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:51 | yes | 7 | 77 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:51 | yes | 5 | 11 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:34 | yes | 3 | 20 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:05 | yes | 12 | 17 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:37 | no | 0 | 6 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:56 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:11 | yes | 13 | 25 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 11:27 | yes | 19 | 6 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:32 | yes | 17 | 9 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:29 | no | 0 | 6 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:30 | yes | 14 | 33 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:33 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:37 | no | 0 | 32 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:05 | yes | 21 | 29 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:28 | yes | 11 | 37 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:18 | yes | 5 | 14 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 07:56 | yes | 3 | 28 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:38 | yes | 14 | 25 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:16 | yes | 16 | 9 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:30 | yes | 5 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:03 | yes | 21 | 20 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 08:48 | yes | 20 | 3 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:53 | yes | 23 | 11 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:56 | yes | 5 | 4 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:48 | yes | 23 | 32 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:56 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:23 | yes | 5 | 51 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:39 | yes | 24 | 25 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:53 | yes | 24 | 141 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:53 | yes | 19 | 30 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:55 | yes | 3 | 14 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:38 | yes | 25 | 32 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
Acuity read as Acuity Scheduling
Workday read as Workday Recruiting
Unresolved, counted raw
BizBot
Calendly Talent Cloud
Calendly’s Prelude
G2.com
Google Calendar "Appointment Schedules"
Google Calendar Appointment Schedules
Hireflow
Incruiter.com
Lever built-in scheduling
Vyte
Workforce Now
Xobni
Yodel.io
Zoom Scheduler
Discontinued, still offered
No shut-down product was recommended here.