Procure-to-pay
Asked as “procure-to-pay platform”, and as “purchasing software”, on behalf of a mid-market B2B company. 59 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
42%
Clear leader
42Procurify19Precoro12Ramp27others
42% of first choices, clear leader.
Since September 2026▼−3Since September 2026: 45% → 42%, −3 points. Inside the 10-point floor: within noise. Read over the models both editions asked.Procurify held the lead, −3 points on 45%, inside the 10-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
Precoro28%Contested↗new leaderNew leader since September 2026: Precoro (28%) replaces Ramp (27% then, 17% now), 11 points clear, past the 10-point floor.Standing →Mid-market
Procurify42%Clear leader▼−3Since September 2026: 45% → 42%, −3 points. Inside the 10-point floor: within noise. Read over the models both editions asked.This pageEnterprise
Coupa43%Clear leader▼−1Since September 2026: 42% → 41%, −1 point. Inside the 10-point floor: within noise. Read over the models both editions asked.Standing →
Precoro28%Contested↗new leaderNew leader since September 2026: Precoro (28%) replaces Ramp (27% then, 17% now), 11 points clear, past the 10-point floor.Standing →Mid-marketProcurify42%Clear leader▼−3Since September 2026: 45% → 42%, −3 points. Inside the 10-point floor: within noise. Read over the models both editions asked.This pageEnterpriseCoupa43%Clear leader▼−1Since September 2026: 42% → 41%, −1 point. Inside the 10-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
01Procurify42%2%50endorsed leader▼−3Since September 2026: 45% → 42%, −3 points. Inside the 10-point floor: within noise. Read over the models both editions asked.45% → 42%
Procurify42%2%50endorsed leader▼−3Since September 2026: 45% → 42%, −3 points. Inside the 10-point floor: within noise. Read over the models both editions asked.45% → 42%02Precoro19%4%50accepted challenger▲+12Since September 2026: 6% → 18%, +12 points. Past the 10-point floor: movement. Read over the models both editions asked.6% → 18%
Precoro19%4%50accepted challenger▲+12Since September 2026: 6% → 18%, +12 points. Past the 10-point floor: movement. Read over the models both editions asked.6% → 18%03
Ramp12%9%22accepted challenger▼−5Since September 2026: 17% → 12%, −5 points. Inside the 10-point floor: within noise. Read over the models both editions asked.17% → 12%
Ramp12%9%22accepted challenger▼−5Since September 2026: 17% → 12%, −5 points. Inside the 10-point floor: within noise. Read over the models both editions asked.17% → 12%04
Tradogram5%0%12accepted challenger▼−2Since September 2026: 6% → 4%, −2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.6% → 4%
Tradogram5%0%12accepted challenger▼−2Since September 2026: 6% → 4%, −2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.6% → 4%05
Spendwise3%0%11accepted challenger=heldSince September 2026: 4% → 4%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.4% → 4%
Spendwise3%0%11accepted challenger=heldSince September 2026: 4% → 4%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.4% → 4%06Coupa3%50%50criticized challenger▲+2Since September 2026: 2% → 4%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.2% → 4%
Coupa3%50%50criticized challenger▲+2Since September 2026: 2% → 4%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.2% → 4%07
ProcureDesk2%0%16accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.4% → 2%
ProcureDesk2%0%16accepted challenger▼−2Since September 2026: 4% → 2%, −2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.4% → 2%08
Zip2%6%18accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 2%
Zip2%6%18accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 2%Show the five products at 0%, ordered by negative rate
13
SAP Ariba0%57%46criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%
SAP Ariba0%57%46criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%11
Jaggaer0%25%20criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%
Jaggaer0%25%20criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%12
Oracle Fusion Cloud Procurement0%20%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%
Oracle Fusion Cloud Procurement0%20%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%09
Ivalua0%11%18accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%
Ivalua0%11%18accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%10
GEP SMART0%0%12accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%
GEP SMART0%0%12accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 10-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 10 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.
For buyers
All twenty-eight head-to-head pages: the top eight products, each against each
Procurify vs SpendwiseProcurify vs CoupaProcurify vs ProcureDeskProcurify vs ZipPrecoro vs RampPrecoro vs TradogramPrecoro vs SpendwisePrecoro vs CoupaPrecoro vs ProcureDeskPrecoro vs ZipRamp vs TradogramRamp vs SpendwiseRamp vs CoupaRamp vs ProcureDeskRamp vs ZipTradogram vs SpendwiseTradogram vs CoupaTradogram vs ProcureDeskTradogram vs ZipSpendwise vs CoupaSpendwise vs ProcureDeskSpendwise vs ZipCoupa vs ProcureDeskCoupa vs ZipProcureDesk vs Zip
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 →
01020304050607080910111213Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Procurify42%
Procurify42%02Precoro19%
Precoro19%03Ramp12%
Ramp12%04Tradogram5%
Tradogram5%05Spendwise3%
Spendwise3%06Coupa3%
Coupa3%07ProcureDesk2%
ProcureDesk2%08Zip2%
Zip2%09Ivalua0%
Ivalua0%10GEP SMART0%
GEP SMART0%11Jaggaer0%
Jaggaer0%12Oracle Fusion Cloud Procurement0%
Oracle Fusion Cloud Procurement0%13SAP Ariba0%
SAP Ariba0%What they warned about
Six of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Mistral Small, DeepSeek V4 Flash, Llama 4 Maverick, Kimi K2 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.
SAP Ariba57%
26 of 46 labels negative · 13 of 14 models · 2 hard negative
“**SAP Ariba** | • **Expensive** pricing, not suitable for small businesses<br>• **Steep learning curve**” GLM 4.7 FlashX, negative prompt
Coupa50%
25 of 50 labels negative · 13 of 14 models · 1 hard negative
“What to Avoid: ... Coupa, SAP Ariba, Ivalua — Enterprise solutions with custom pricing” Kimi K2, budget prompt
Jaggaer25%
5 of 20 labels negative · 5 of 14 models
“"Our own rollout of Jaggaer cost $120 k in annual licensing, but the services team charged $210 k for configuration, data migration, and custom workflow scripting."” Muse Glimmer 30B, negative prompt
Global Sources80%
4 of 5 labels negative · 4 of 14 models · 2 hard negative
“Trustpilot reviews call out fraud on Global Sources ("maximum suppliers are fraud," no PayPal/credit card to avoid traceability)” Grok 4.1 Fast, 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
75 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. 1025 links across 257 sites, every framing counted. Ranked by the number of answers carrying the site or page. None of the 252 answers across every segment cited this index's own page for the category.
vendor site · ProcureDesk36 answers · 61 citations · 12 models
vendor site · G232 answers · 50 citations · 12 models
vendor site · Spendflo32 answers · 43 citations · 11 models
vendor site · G227 answers · 31 citations · 10 models
vendor site · Precoro23 answers · 29 citations · 10 models
23 answers · 26 citations · 10 models
vendor site · Procurify21 answers · 36 citations · 11 models
vendor site · Payhawk21 answers · 26 citations · 9 models
vendor site · Ramp18 answers · 20 citations · 11 models
vendor site · BILL17 answers · 18 citations · 9 models
vendor site · Order.co15 answers · 18 citations · 10 models
15 answers · 17 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.
19 answers · 10 models
18 answers · 9 models
15 answers · 8 models
15 answers · 8 models
14 answers · 9 models
14 answers · 8 models
13 answers · 9 models
12 answers · 7 models
11 answers · 7 models
11 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 procure-to-pay platform”, “procure-to-pay platform”, “procurement 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 |
|---|---|---|---|---|---|---|---|---|
| Procurify procurify.com | 42% | 220 | 4,400 | 43 | 62,225 | $282 | 14 | |
| Precoro precoro.com | 19% | 283 | 1,000 | 3 | 82,966 | 86 | ||
| Ramp ramp.com | 12% | 1042 | 246,000 | 49,733 | 770,056 | $13,511 | 120 | |
| Tradogram tradogram.com | 5% | 151 | 880 | 4 | 208,129 | 1 | ||
| Spendwise spendwise.com | 3% | 42 | 720 | 3 | 401 | 2 | ||
| Rillion rillion.com | 3% | 23 | 480 | 10 | 35,512 | $576 | 14 | |
| Coupa coupa.com | 3% | 674 | 60,500 | 1,210 | 41,319 | $339 | 91 | 2 |
| ProcureDesk procuredesk.com | 2% | 57 | 720 | 1 | 11,677 | 5 | 5 | |
| Zip zip.com | 2% | 95 | 301,000 | 498,436 | 18,669 | 34 | ||
| BILL bill.com | 2% | 444 | 2,740,000 | 1,063,001 | 1,783,039 | $199,231 | 119 |
Google's top ten for the three searches hold 24 sites; 3 of them are among the sites the models cited here (g2.com, learn.g2.com, procuredesk.com). The first result for “best procure-to-pay platform” is gartner.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 procure-to-pay platform for a mid-market B2B company?”Paraphrase“Which purchasing software would you recommend to a mid-sized B2B company?”Comparative“What are the top procurement platforms and how do they differ?”Budget-constrained“What is the best procure-to-pay platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a procure-to-pay platform. What should we look at?”Negative“Which procurement platforms should I avoid or be cautious about?”
Claude Haiku 4.5APSentrano first choiceChangedno first choicePrecorono first choiceagainst: Coupa, Precoro, SAP Ariba
Three alternatives
Precoro, Procurify, RillionThree alternatives
Cflow, Procurify, SpendwiseGPT-5.4 miniCoupa, ProcurifyProcurifyChangedagainst: Coupa, SAP AribaCoupa, GEP SMART, Ivalua, Jaggaer, SAPPrecorono first choicenothing named
Four alternatives
Ivalua, Jaggaer, Onventis, SAPTwo alternatives
Precoro, ZipThree alternatives
Basware, Oracle Fusion Cloud Procurement, ZipThree alternatives
ProcureDesk, Spendwise, TradogramGemini 3.5 FlashProcurifyagainst: Coupa, SAP AribaPrecoroChangedagainst: Coupa, SAP Aribano first choiceRampagainst: Coupa, SAP AribaZipagainst: Oracle Fusion Cloud Procurement, SAP Aribaagainst: Coupa, SAP Ariba
Four alternatives
Precoro, Ramp, Tipalti, ZipFour alternatives
Order.co, Procurify, Tradogram, ZipEight alternatives
Coupa, GEP SMART, Ivalua, Order.co, Precoro, Procurify, SAP Ariba, ZipThree alternatives
BILL, Precoro, TradogramSix alternatives
Airbase, Precoro, Procurify, Ramp, Stampli, TipaltiPerplexity SonarProcurifyagainst: CoupaProcurifyHeldno first choiceagainst: Monday.com, RampRampno first choiceagainst: Coupa, Jaggaer, Order.co, Precoro, Ramp, Rippling, SAP Ariba, TradeGecko, Workday P2P/T&E
Three alternatives
Precoro, Ramp, RillionFour alternatives
GEP SMART, Precoro, Spendesk, TeampayTwo alternatives
Precoro, TeampayGrok 4.1 FastProcurifyagainst: Coupa, SAP AribaProcurifyHeldno first choiceRampno first choiceagainst: SAP Aribaagainst: Alibaba, Global Sources, Made-in-China, SAP Ariba
Three alternatives
Precoro, Ramp, StampliThree alternatives
Precoro, Ramp, TradogramFour alternatives
Precoro, ProcureDesk, Procurify, SpendwiseMistral SmallProcurify, Rillionagainst: Coupa, Ivalua, Jaggaer, SAP AribaPrecoro, ProcurifyChangedno first choiceRamp, Spendwiseno first choiceagainst: TradeGecko
One alternative
PrecoroOne alternative
VISCOFour alternatives
Coupa, Jaggaer, SAP Ariba, ZycusOne alternative
PrecoroDeepSeek V4 FlashPrecoro, Procurifyagainst: CoupaProcurifyChangedagainst: CoupaCoupaagainst: SAP AribaTradogramagainst: ZohoPrecoro, Procurify, Stampliagainst: Coupa, SAP Aribaagainst: Amazon Marketplace / Amazon Business Third-Party Sellers, Global Sources, Jaggaer
Two alternatives
ProcureDesk, ZipTwo alternatives
Precoro, ProcureDeskFive alternatives
GEP SMART, Ivalua, Jaggaer, Procurify, ZipThree alternatives
Precoro, Procurify, SpendwiseFive alternatives
Ivalua, Jaggaer, Payhawk, Ramp, Zone & CoLlama 4 MaverickRillionno first choiceChangedno first choiceno first choiceno first choicenothing named
Two alternatives
Mindsprint, ProcureDeskQwen 3.7 FlashProcurifyProcurifyHeldno first choiceRampagainst: Coupa, SAP Aribano first choiceagainst: Coupa, Oracle Fusion Cloud Procurementagainst: Alibaba, DHgate, Facebook Marketplace, Fiverr Enterprise, Global Sources, Made-in-China, Upwork
Four alternatives
Airbase, BILL, Coupa, TipaltiThree alternatives
Archer Point Solutions, Jaggaer, Kosh.aiFour alternatives
Coupa, Oracle Fusion Cloud Procurement, Procurify, SAP AribaFour alternatives
Airbase, BILL, Kissflow Procurement, PleoKimi K2ProcurifyPrecoro, ProcurifyChangedagainst: CoupaCoupa, SAP AribaERPNext, Tradogramagainst: Coupa, Ivalua, Procurify, SAP Aribano first choiceagainst: Coupa, SAP Aribaagainst: Jaggaer, OpenGov Procurement, SAP Ariba
Two alternatives
Coupa, PrecoroOne alternative
PayhawkEight alternatives
GEP SMART, Ivalua, Jaggaer, Kissflow Procurement, Oracle Fusion Cloud Procurement, Precoro, Procurify, TradogramThree alternatives
Cflow, Precoro, RampThree alternatives
Precoro, Procurify, SpendfloGLM 4.7 FlashXProcurifyagainst: Coupa, SAP AribaProcurifyHeldagainst: ZipCoupaSpendwiseCoupa, ProcureDesk, Procurifyagainst: SAP Ariba, Workday P2P/T&E
Three alternatives
Fraxion, Precoro, ProcureDeskFour alternatives
Precoro, ProcureDesk, SourceReady, TeampayTen alternatives
BILL, GEP SMART, Ivalua, Jaggaer, Kissflow Procurement, Precoro, ProcureDesk, Procurify, SAP Ariba, ZipThree alternatives
Precoro, ProcureDesk, TradogramMiniMax M2.5Precoro, Procurifyagainst: Coupa, SAP AribaPrecoro, ProcurifyHeldCoupa, SAP AribaBILL, Rampno first choiceagainst: Coupa, SAP Ariba
Two alternatives
Ivalua, ProactisTwo alternatives
ProcureDesk, RampFive alternatives
GEP SMART, Ivalua, Oracle Fusion Cloud Procurement, Precoro, ProcurifyFour alternatives
Precoro, Spendwise, Teampay, TradogramGPT-6 LunaProcurifyagainst: CoupaProcurifyHeldagainst: Coupa, SAP Aribano first choiceRampno first choiceagainst: Coupa, SAP Ariba
Three alternatives
Precoro, Ramp, TipaltiOne alternative
PrecoroSeven alternatives
Coupa, GEP SMART, Ivalua, Jaggaer, Oracle Fusion Cloud Procurement, SAP Ariba, ZipTwo alternatives
BILL, ProcureDeskMuse Glimmer 30BProcurifyagainst: CoupaPrecoro, ProcurifyChangedSAP AribaPrecoro, TradogramSpendeskagainst: Alibaba, Global Sources, Jaggaer, Made-in-China.com, SAP Ariba
Four alternatives
Payhawk, Precoro, ProcureDesk, RampTwo alternatives
Coupa, Oracle Fusion Cloud ProcurementThree alternatives
ProcureDesk, Procurify, RampTwo alternatives
Procurify, StampliBold 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 10:35 | yes | 9 | 7 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:39 | yes | 3 | 5 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:44 | yes | 12 | 25 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 07:47 | yes | 15 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:29 | yes | 23 | 8 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 07:45 | yes | 14 | 7 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:34 | yes | 21 | 33 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:42 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:06 | no | 0 | 25 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:06 | yes | 13 | 19 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:45 | yes | 20 | 97 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:15 | yes | 10 | 24 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:23 | yes | 5 | 16 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:54 | yes | 13 | 20 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:39 | no | 0 | 4 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 08:33 | yes | 4 | 6 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 07:52 | yes | 13 | 24 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 10:45 | yes | 17 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:01 | yes | 24 | 6 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:06 | yes | 5 | 4 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:28 | yes | 25 | 23 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:40 | yes | 5 | 4 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:14 | no | 0 | 29 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:04 | yes | 15 | 18 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:47 | yes | 10 | 39 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:43 | yes | 10 | 20 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:39 | yes | 3 | 11 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:44 | yes | 14 | 17 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:58 | yes | 10 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:51 | yes | 5 | 8 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:38 | yes | 18 | 25 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 11:22 | yes | 17 | 6 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:14 | yes | 23 | 10 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:44 | yes | 10 | 7 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:45 | yes | 23 | 42 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:52 | yes | 5 | 4 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:52 | yes | 5 | 17 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:39 | yes | 20 | 28 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:58 | yes | 15 | 69 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:09 | yes | 10 | 20 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:18 | yes | 7 | 34 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:48 | yes | 15 | 24 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:49 | yes | 15 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:12 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:26 | yes | 25 | 21 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 08:43 | yes | 19 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:47 | yes | 24 | 7 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:42 | yes | 5 | 3 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 07:51 | yes | 24 | 42 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:16 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:08 | no | 0 | 30 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:14 | yes | 23 | 23 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:34 | yes | 20 | 112 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:44 | yes | 8 | 25 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:43 | yes | 3 | 13 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:37 | yes | 14 | 22 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:58 | no | 0 | 7 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:44 | no | 0 | 6 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:42 | yes | 8 | 26 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 08:57 | yes | 15 | 4 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:02 | yes | 15 | 8 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:22 | no | 0 | 8 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:23 | yes | 19 | 36 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 11:01 | yes | 5 | 3 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:14 | no | 0 | 36 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:27 | yes | 22 | 26 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:26 | yes | 24 | 24 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:56 | no | 0 | 8 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:24 | yes | 2 | 18 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:40 | yes | 20 | 40 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:12 | yes | 19 | 7 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:52 | yes | 4 | 7 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:53 | yes | 16 | 25 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 07:55 | yes | 18 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:57 | yes | 25 | 7 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:11 | yes | 10 | 6 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:05 | yes | 25 | 29 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:59 | yes | 5 | 4 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:09 | yes | 5 | 29 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:49 | yes | 25 | 29 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 08:11 | yes | 24 | 108 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:20 | yes | 14 | 29 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:28 | yes | 1 | 14 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:22 | yes | 25 | 30 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
Ariba read as SAP Ariba
Bill.com read as BILL
GEP read as GEP SMART
Kissflow read as Kissflow Procurement
NetSuite read as Oracle NetSuite
Oracle read as Oracle Fusion Cloud Procurement
Oracle Procurement Cloud read as Oracle Fusion Cloud Procurement
Unresolved, counted raw
Amazon Marketplace / Amazon Business Third-Party Sellers
Archer Point Solutions
Benchling
Facebook Marketplace
G2 Learning Hub
GlobalSources
Made-in-China.com
Mindsprint
Mirakl
NetSuite Procurement
Onventis
OpenGov Procurement
Opstream
Oxalys
Penny
Sourcing Force - eProcurement
TYASuite Procurement Software
TradeEdge
Zycase
Discontinued, still offered
No shut-down product was recommended here.
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