Recruiting CRM and sourcing for enterprise buyers
Asked as “candidate sourcing tool”, and as “recruiting CRM”, on behalf of an enterprise B2B company. 51 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
18%
Contested · Gem 14%
18SeekOut14Gem12Beamery57others
18% of first choices, contested.
Since September 2026↗new leaderNew leader since September 2026: SeekOut (19%) replaces Beamery (20% then, 9% now), 9 points clear, inside the 13-point floor.SeekOut leads at 19%, replacing Beamery, which led at 20% and stands at 9% now: 9 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
Manatal25%Contested↗new leaderNew leader since September 2026: Manatal (22%) replaces Pin (23% then, 18% now), 4 points clear, inside the 13-point floor.Standing →Mid-market
Gem15%Contested↗new leaderNew leader since September 2026: Gem (15%) replaces Lever (20% then, 11% now), 4 points clear, inside the 13-point floor.Standing →Enterprise
SeekOut18%Contested↗new leaderNew leader since September 2026: SeekOut (19%) replaces Beamery (20% then, 9% now), 9 points clear, inside the 13-point floor.This page
Manatal25%Contested↗new leaderNew leader since September 2026: Manatal (22%) replaces Pin (23% then, 18% now), 4 points clear, inside the 13-point floor.Standing →Mid-marketGem15%Contested↗new leaderNew leader since September 2026: Gem (15%) replaces Lever (20% then, 11% now), 4 points clear, inside the 13-point floor.Standing →EnterpriseSeekOut18%Contested↗new leaderNew leader since September 2026: SeekOut (19%) replaces Beamery (20% then, 9% now), 9 points clear, inside the 13-point floor.This pageThe standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01SeekOut18%26%31criticized challenger▲+4Since September 2026: 15% → 19%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 19%
SeekOut18%26%31criticized challenger▲+4Since September 2026: 15% → 19%, +4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 19%02Gem14%17%29accepted challenger▲+6Since September 2026: 10% → 16%, +6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.10% → 16%
Gem14%17%29accepted challenger▲+6Since September 2026: 10% → 16%, +6 points. Inside the 13-point floor: within noise. Read over the models both editions asked.10% → 16%03
Eightfold AI12%14%22accepted challenger▲+9Since September 2026: 5% → 14%, +9 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 14%
Eightfold AI12%14%22accepted challenger▲+9Since September 2026: 5% → 14%, +9 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 14%04
Beamery12%29%21criticized challenger▼−11Since September 2026: 20% → 9%, −11 points. Inside the 13-point floor: within noise. Read over the models both editions asked.20% → 9%
Beamery12%29%21criticized challenger▼−11Since September 2026: 20% → 9%, −11 points. Inside the 13-point floor: within noise. Read over the models both editions asked.20% → 9%05
iCIMS10%32%19criticized challenger▲+9Since September 2026: 2% → 12%, +9 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 12%
iCIMS10%32%19criticized challenger▲+9Since September 2026: 2% → 12%, +9 points. Inside the 13-point floor: within noise. Read over the models both editions asked.2% → 12%06
LinkedIn Recruiter6%52%29criticized challenger▼−13Since September 2026: 15% → 2%, −13 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 2%
LinkedIn Recruiter6%52%29criticized challenger▼−13Since September 2026: 15% → 2%, −13 points. Inside the 13-point floor: within noise. Read over the models both editions asked.15% → 2%07
hireEZ4%20%25accepted challenger=heldSince September 2026: 5% → 5%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 5%
hireEZ4%20%25accepted challenger=heldSince September 2026: 5% → 5%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.5% → 5%08
Greenhouse4%9%11accepted challenger▲+5Since September 2026: 0% → 5%, +5 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 5%
Greenhouse4%9%11accepted challenger▲+5Since September 2026: 0% → 5%, +5 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 5%Show the four products at 0%, ordered by negative rate
12
SAP Ariba0%43%14criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
SAP Ariba0%43%14criticized 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
Coupa0%36%11criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
Coupa0%36%11criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%10
Lever0%10%10accepted challenger▼−8Since September 2026: 8% → 0%, −8 points. Inside the 13-point floor: within noise. Read over the models both editions asked.8% → 0%
Lever0%10%10accepted challenger▼−8Since September 2026: 8% → 0%, −8 points. Inside the 13-point floor: within noise. Read over the models both editions asked.8% → 0%09
Jaggaer0%0%11accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.0% → 0%
Jaggaer0%0%11accepted 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.
Head to head
All twenty-eight head-to-head pages: the top eight products, each against each
Gem vs Zoho RecruitGem vs PinGem vs LinkedIn RecruiterGem vs GreenhouseLever vs ManatalLever vs hireEZLever vs Zoho RecruitLever vs PinLever vs LinkedIn RecruiterLever vs GreenhouseManatal vs hireEZManatal vs Zoho RecruitManatal vs PinManatal vs LinkedIn RecruiterManatal vs GreenhousehireEZ vs Zoho RecruithireEZ vs PinhireEZ vs LinkedIn RecruiterhireEZ vs GreenhouseZoho Recruit vs PinZoho Recruit vs LinkedIn RecruiterZoho Recruit vs GreenhousePin vs LinkedIn RecruiterPin vs GreenhouseLinkedIn Recruiter vs Greenhouse
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% negative30%
Key
01SeekOut18%
SeekOut18%02Gem14%
Gem14%03Eightfold AI12%
Eightfold AI12%04Beamery12%
Beamery12%05iCIMS10%
iCIMS10%06LinkedIn Recruiter6%
LinkedIn Recruiter6%07hireEZ4%
hireEZ4%08Greenhouse4%
Greenhouse4%09Jaggaer0%
Jaggaer0%10Lever0%
Lever0%11Coupa0%
Coupa0%12SAP Ariba0%
SAP Ariba0%What they warned about
Zero 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, MiniMax M2.5, 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.
LinkedIn Recruiter52%
15 of 29 labels negative · 10 of 14 models · 6 hard negative
“you want to avoid per-seat / per-InMail / credit models. LinkedIn Recruiter and most per-user sourcing stacks get very expensive and unpredictable” Muse Glimmer 30B, budget prompt
SeekOut26%
8 of 31 labels negative · 8 of 14 models · 4 hard negative
“Why to Avoid for Large-Scale Predictability: Most enterprise platforms like SeekOut (typically $10,000+ annually per recruiter)” Claude Haiku 4.5, budget prompt
SAP Ariba43%
6 of 14 labels negative · 6 of 14 models · 1 hard negative
“frequently cited as costly for the value they deliver and are sometimes considered outdated for current enterprise needs” Mistral Small, negative prompt
hireEZ20%
5 of 25 labels negative · 5 of 14 models · 3 hard negative
“**Poor.** Strongly optimized for dedicated sourcing teams rather than casual company-wide users.” Gemini 3.5 Flash, 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
79 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. 1136 links across 349 sites, every framing counted. Ranked by the number of answers carrying the site or page. 3 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 · Pin39 answers · 96 citations · 12 models
vendor site · Lever34 answers · 42 citations · 11 models
vendor site · Leonar26 answers · 31 citations · 11 models
vendor site · Intervue25 answers · 25 citations · 11 models
24 answers · 26 citations · 12 models
vendor site · Metaview23 answers · 28 citations · 12 models
vendor site · Guideflow20 answers · 22 citations · 11 models
19 answers · 24 citations · 10 models
vendor site · Noon18 answers · 30 citations · 9 models
vendor site · Gartner13 answers · 21 citations · 6 models
vendor site · Gem13 answers · 16 citations · 7 models
vendor site · Greenhouse12 answers · 17 citations · 8 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.
26 answers · 10 models
22 answers · 11 models
22 answers · 10 models
22 answers · 11 models
21 answers · 10 models
17 answers · 11 models
11 answers · 11 models
11 answers · 9 models
11 answers · 8 models
9 answers · 5 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 candidate sourcing tool”, “candidate sourcing tool”, “sourcing 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 |
|---|---|---|---|---|---|---|---|---|
| SeekOut seekout.com | 18% | 205 | 6,600 | 52 | 5,796 | 1 | ||
| Gem gem.com | 14% | 209 | 246,000 | 225,116 | 16,291 | 10 | ||
| Eightfold eightfold.ai Eightfold AI 12% | 12% | 540 | 4,400 | 187 | 53,360 company | 17 | ||
| Beamery beamery.com | 12% | 293 | 1,000 | 21 | 12,866 | 0 | ||
| iCIMS icims.com | 10% | 147 | 27,100 | 667 | 1,165,712 | $5,674 | 26 | |
| LinkedIn parent site LinkedIn Recruiter 6% | 6% | 111 | 11,100,000 | 77,031 | ||||
| hireEZ hireez.com | 4% | 185 | 2,400 | 43 | 3,058 | 35 | ||
| Greenhouse greenhouse.com | 4% | 419 | 301,000 | 52,273 | 62,551 | $5,545 | 74 | |
| Bullhorn bullhorn.com | 4% | 201 | 49,500 | 1,451 | 53,403 | $226 | 17 | |
| Pin pin.com | 4% | 199 | 368,000 | 243,385 | 24,375 | 28 | 2 |
Google's top ten for the three searches hold 18 sites; 4 of them are among the sites the models cited here (metaview.ai, peoplemanagingpeople.com, pin.com, selectsoftwarereviews.com). The first result for “best candidate sourcing tool” is hiretruffle.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 candidate sourcing tool for an enterprise B2B company?”Paraphrase“Which recruiting CRM would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade sourcing tools and how do they differ?”Budget-constrained“What is the best candidate sourcing tool for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a candidate sourcing tool. What should we look at?”Negative“Which sourcing tools should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5no first choiceiCIMSChangedCoupaManatalagainst: Gem, SeekOutno first choiceagainst: Coupa, SAP Ariba
Two alternatives
Beamery, BullhornFive alternatives
GEP SMART, Ivalua, Jaggaer, SAP Ariba, ZycusTwo alternatives
Arya, PinGPT-5.4 miniSeekOutiCIMSChangedno first choiceSeekOutagainst: LinkedIn Recruiterno first choicenothing named
Four alternatives
Bullhorn, Eightfold AI, Greenhouse, hireEZOne alternative
SmartRecruitersOne alternative
hireEZGemini 3.5 FlashSeekOutBeamery, Eightfold AIChangedGemEightfold AIagainst: LinkedIn Recruiter, SeekOut, hireEZno first choicenothing named
Two alternatives
Gem, hireEZThree alternatives
Avature, Gem, PhenomThree alternatives
Beamery, SeekOut, hireEZTwo alternatives
Findem, PhenomPerplexity SonarGemiCIMSChangedSAP AribaGemagainst: LinkedIn Recruiter, Workday Skills Cloudno first choiceagainst: Coupa, SAP Ariba
Four alternatives
Lever, LinkedIn Recruiter, SeekOut, hireEZOne alternative
AvatureFour alternatives
Ivalua, Jaggaer, ORO Labs, ZycusThree alternatives
Beamery, Phenom, iCIMSGrok 4.1 FastLinkedIn RecruiterGreenhouseChangedagainst: Bullhornno first choiceagainst: Beamery, Eightfold AIBullhornagainst: Eightfold AI, Greenhouse, Handshake, Indeed, JazzHR, Lever, LinkedIn Recruiter, Oracle Taleo, Pin, SeekOut, SmartRecruiters, Workday Recruiting, hireEZ, iCIMSno first choiceagainst: Beamery, Coupa, GEP SMART, Google for Jobs, LinkedIn Recruiter, Precoro, Procurify, SAP Ariba, SAP SuccessFactors Recruiting, iCIMS
Five alternatives
Beamery, Eightfold AI, Gem, SeekOut, hireEZFour alternatives
Beamery, Lever, SmartRecruiters, iCIMSTwo alternatives
PeopleForce, WorkableMistral SmallEightfold AI, hireEZBeameryChangedno first choiceGem, SeekOutno first choiceagainst: SAP Ariba, Workday P2P/T&E
Two alternatives
Phenom, SmartRecruitersTwo alternatives
Greenhouse, Recruit CRMTwo alternatives
Eightfold AI, LinkedIn RecruiterDeepSeek V4 FlashGemagainst: Loxo, hireEZiCIMSChangedLinkedIn Recruiter, SeekOutGem, Manatalagainst: LinkedIn Recruiter, SeekOutno first choicenothing named
Four alternatives
Eightfold AI, Greenhouse, Lever, SeekOutThree alternatives
Avature, Beamery, SmartRecruitersThree alternatives
Gem, Juicebox, hireEZLlama 4 MaverickPhenomBullhornChangedno first choiceEightfold AIno first choiceagainst: SAP Ariba, SeekOut
Two alternatives
Intervue, hireEZThree alternatives
Greenhouse, Vincere, iCIMSFive alternatives
AmazingHiring, Beamery, Gem, SeekOut, hireEZQwen 3.7 FlashSeekOut, hireEZBeameryChangedagainst: Gemno first choiceagainst: GEP SMARTNoon.ai, Pinagainst: Beamery, Gem, LinkedIn Recruiter, SeekOut, iCIMSno first choiceagainst: Excel, LinkedIn Recruiter
One alternative
GemOne alternative
iCIMSFive alternatives
Coupa, Ivalua, Jaggaer, Oracle Procurement Cloud, SAP AribaOne alternative
Breezy HRKimi K2Gemagainst: LinkedIn RecruiterGreenhouseChangedagainst: Oracle Fusion Cloud HCM, SAP SuccessFactors Recruiting, Workday Recruitingno first choiceEightfold AIagainst: Entelo, Gem, LinkedIn Recruiter, SeekOut, hireEZno first choiceagainst: Coupa, Fairmarkit, Precoro, Procurify, SAP Ariba
Three alternatives
Eightfold AI, SeekOut, hireEZTwo alternatives
Lever, iCIMSFive alternatives
Coupa, GEP SMART, Ivalua, Jaggaer, SAP AribaThree alternatives
Phenom, SmartRecruiters, TeamtailorGLM 4.7 FlashXSeekOutBeameryChangedhireEZGem, SeekOutagainst: Beamery, HireVue, LinkedIn Recruiter, SAP SuccessFactors Recruiting, iCIMSno first choicenothing named
Four alternatives
Eightfold AI, Greenhouse, hireEZ, iCIMSTwo alternatives
Avature, iCIMSSix alternatives
Eightfold AI, Findem, Gem, HeroHunt.ai, Loxo, SeekOutTwo alternatives
Eightfold AI, Workday RecruitingMiniMax M2.5Eightfold AI, SeekOutiCIMSChangedno first choicePin Business Planagainst: Beamery, Eightfold AI, LinkedIn Recruiter, iCIMSno first choiceagainst: Precoro, Procurify, Tradogram
Four alternatives
Gem, Greenhouse, Lever, SmartRecruitersTwo alternatives
Greenhouse, LeverOne alternative
NoonGPT-6 LunaLinkedIn RecruiterBeameryChangedno first choiceLinkedIn Recruiteragainst: Scout, SeekOutno first choicenothing named
Two alternatives
Gem, SeekOutTwo alternatives
Avature, PhenomEight alternatives
Coupa, GEP SMART, Ivalua, Jaggaer, Keelvar, Oracle Fusion Cloud Procurement, SAP Ariba, ZycusMuse Glimmer 30BEntelo, SeekOutBeameryChangedLinkedIn Recruiteragainst: Beamery100Hires, Pinagainst: LinkedIn Recruiter, iCIMSno first choiceagainst: Apollo.io, Gem, LinkedIn Recruiter, hireEZ
One alternative
Eightfold AIThree alternatives
Avature, Workday Recruiting, iCIMSThree alternatives
Eightfold AI, SeekOut, hireEZTwo alternatives
Beamery, Eightfold AIBold 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 07:46 | no | 0 | 4 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:39 | yes | 4 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 07:49 | yes | 11 | 30 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 10:22 | yes | 20 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:03 | yes | 24 | 9 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:19 | yes | 12 | 6 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:06 | yes | 25 | 40 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:25 | yes | 6 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:33 | yes | 10 | 23 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:44 | yes | 15 | 22 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:53 | yes | 23 | 31 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:17 | yes | 11 | 21 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:14 | yes | 3 | 14 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:25 | yes | 14 | 13 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:27 | yes | 9 | 8 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:48 | yes | 2 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:02 | yes | 14 | 26 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 10:51 | yes | 20 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:02 | yes | 21 | 8 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:17 | yes | 5 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:24 | yes | 22 | 23 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:58 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:49 | yes | 9 | 19 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:52 | yes | 17 | 18 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:45 | yes | 14 | 71 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 07:44 | yes | 19 | 20 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:38 | yes | 4 | 13 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:49 | yes | 24 | 21 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:04 | yes | 9 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:07 | yes | 1 | 6 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:08 | yes | 16 | 28 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 12:02 | yes | 17 | 5 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 07:36 | yes | 24 | 9 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 07:36 | yes | 16 | 9 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:25 | yes | 24 | 55 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:49 | yes | 5 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:42 | yes | 15 | 59 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:26 | yes | 20 | 32 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:40 | yes | 23 | 33 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:19 | yes | 9 | 17 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:22 | yes | 10 | 48 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:30 | yes | 15 | 37 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:06 | yes | 17 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:58 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:19 | yes | 21 | 74 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 10:38 | yes | 20 | 4 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:15 | yes | 22 | 12 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:41 | yes | 6 | 6 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:22 | yes | 19 | 25 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 07:46 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:04 | yes | 15 | 192 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:20 | yes | 25 | 25 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:21 | yes | 18 | 73 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:22 | yes | 18 | 49 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:24 | yes | 2 | 21 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:05 | yes | 16 | 36 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:14 | yes | 18 | 15 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:56 | yes | 2 | 9 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:23 | no | 0 | 16 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 09:46 | yes | 20 | 7 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:43 | no | 0 | 6 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:42 | no | 0 | 8 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:51 | yes | 25 | 53 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:34 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:56 | no | 0 | 32 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:11 | yes | 25 | 49 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:42 | yes | 14 | 39 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:28 | yes | 9 | 29 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:03 | yes | 5 | 47 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:53 | yes | 14 | 26 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:37 | yes | 27 | 13 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:51 | yes | 5 | 7 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:17 | yes | 12 | 59 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 11:06 | yes | 20 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:34 | yes | 21 | 9 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:48 | yes | 14 | 5 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:57 | yes | 25 | 32 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 07:43 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:51 | yes | 8 | 69 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:27 | yes | 25 | 36 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:37 | yes | 25 | 84 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:58 | yes | 14 | 24 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:23 | yes | 4 | 15 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:49 | yes | 20 | 28 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
Eightfold read as Eightfold AI
GEP read as GEP SMART
SAP SuccessFactors read as SAP SuccessFactors Recruiting
Workday read as Workday Recruiting
iCIMS Talent Cloud read as iCIMS
Unresolved, counted raw
Basware
Coupa Strategic Sourcing
Leoforce
Metaview Sourcing
Noon.ai
ORO Labs
Olive
Oracle Procurement Cloud
Phenom Talent Cloud
Pin Business Plan
Raindrop
SAP Ariba Sourcing
Scout
Scout RFP (Workday Strategic Sourcing)
Workday P2P/T&E
Workday Strategic Sourcing
Workday's native modules
Discontinued, still offered
No shut-down product was recommended here.