Incident management and on-call
Asked as “incident management platform”, and as “on-call scheduling and alerting tool”, on behalf of a mid-market B2B company. 60 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
32%
Contested · PagerDuty 15%
32incident.io15PagerDuty10Freshservice43others
32% of first choices, contested.
Since September 2026▲+5Since September 2026: 25% → 31%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.incident.io held the lead, +5 points on 25%, inside the 11-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
incident.io35%Contested▲+4Since September 2026: 30% → 34%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketincident.io32%Contested▲+5Since September 2026: 25% → 31%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageEnterprise
PagerDuty57%Clear leader▲+9Since September 2026: 50% → 59%, +9 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →
incident.io35%Contested▲+4Since September 2026: 30% → 34%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketincident.io32%Contested▲+5Since September 2026: 25% → 31%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageEnterprisePagerDuty57%Clear leader▲+9Since September 2026: 50% → 59%, +9 points. Inside the 11-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
01incident.io32%5%57endorsed leader▲+5Since September 2026: 25% → 31%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.25% → 31%
incident.io32%5%57endorsed leader▲+5Since September 2026: 25% → 31%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.25% → 31%02PagerDuty15%33%72criticized challenger▼−6Since September 2026: 22% → 15%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.22% → 15%
PagerDuty15%33%72criticized challenger▼−6Since September 2026: 22% → 15%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.22% → 15%03
Freshservice10%6%17accepted challenger▲+10Since September 2026: 0% → 10%, +10 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 10%
Freshservice10%6%17accepted challenger▲+10Since September 2026: 0% → 10%, +10 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 10%04
Squadcast8%4%25accepted challenger▼−2Since September 2026: 10% → 8%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 8%
Squadcast8%4%25accepted challenger▼−2Since September 2026: 10% → 8%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 8%05
Grafana IRM7%14%22accepted challenger▲+6Since September 2026: 0% → 6%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 6%
Grafana IRM7%14%22accepted challenger▲+6Since September 2026: 0% → 6%, +6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 6%06
Opsgenie7%72%50criticized challenger=heldSince September 2026: 8% → 8%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 8%
Opsgenie7%72%50criticized challenger=heldSince September 2026: 8% → 8%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 8%07
Rootly5%3%33accepted challenger▼−2Since September 2026: 8% → 6%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 6%
Rootly5%3%33accepted challenger▼−2Since September 2026: 8% → 6%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.8% → 6%08
FireHydrant2%10%21accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
FireHydrant2%10%21accepted challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%09
Splunk On-Call2%33%15criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%
Splunk On-Call2%33%15criticized challenger▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 2%Show the two products at 0%, ordered by negative rate
11
ServiceNow ITSM0%64%22criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
ServiceNow ITSM0%64%22criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%10
Jira Service Management0%20%25accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Jira Service Management0%20%25accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 11 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
incident.io vs Grafana IRMincident.io vs Opsgenieincident.io vs Rootlyincident.io vs FireHydrantPagerDuty vs FreshservicePagerDuty vs SquadcastPagerDuty vs Grafana IRMPagerDuty vs OpsgeniePagerDuty vs RootlyPagerDuty vs FireHydrantFreshservice vs SquadcastFreshservice vs Grafana IRMFreshservice vs OpsgenieFreshservice vs RootlyFreshservice vs FireHydrantSquadcast vs Grafana IRMSquadcast vs OpsgenieSquadcast vs RootlySquadcast vs FireHydrantGrafana IRM vs OpsgenieGrafana IRM vs RootlyGrafana IRM vs FireHydrantOpsgenie vs RootlyOpsgenie vs FireHydrantRootly vs FireHydrant
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 →
0102030405060708091011Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative50%
Key
01incident.io32%
incident.io32%02PagerDuty15%
PagerDuty15%03Freshservice10%
Freshservice10%04Squadcast8%
Squadcast8%05Grafana IRM7%
Grafana IRM7%06Opsgenie7%
Opsgenie7%07Rootly5%
Rootly5%08FireHydrant2%
FireHydrant2%09Splunk On-Call2%
Splunk On-Call2%10Jira Service Management0%
Jira Service Management0%11ServiceNow ITSM0%
ServiceNow ITSM0%What they warned about
Three 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, 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.
Opsgenie72%
36 of 50 labels negative · 14 of 14 models · 26 hard negative
“Do not buy standalone Opsgenie right now unless you are fully committing to migrating to the broader, consolidated Jira Service Management (JSM) ecosystem.” Gemini 3.5 Flash, scale prompt
PagerDuty33%
24 of 72 labels negative · 11 of 14 models · 1 hard negative
“Avoid PagerDuty unless you truly need enterprise-grade features” Kimi K2, budget prompt
ServiceNow ITSM64%
14 of 22 labels negative · 9 of 14 models · 3 hard negative
“**ServiceNow** currently requires extra scrutiny due to its recent critical security disclosures” Kimi K2, negative prompt
Jira Service Management20%
5 of 25 labels negative · 5 of 14 models · 1 hard negative
“the main platforms to be cautious about are Opsgenie, Jira Service Management... warn that its ITSM complexity can slow incident response” Perplexity Sonar, 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
77 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. 1041 links across 190 sites, every framing counted. Ranked by the number of answers carrying the site or page. 1 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 · incident.io55 answers · 140 citations · 14 models
vendor site · Rootly40 answers · 77 citations · 14 models
vendor site · Motadata35 answers · 35 citations · 11 models
vendor site · G231 answers · 45 citations · 12 models
vendor site · PagerDuty27 answers · 43 citations · 10 models
25 answers · 26 citations · 10 models
vendor site · ComplyJet24 answers · 24 citations · 11 models
vendor site · InvGate22 answers · 26 citations · 10 models
19 answers · 19 citations · 9 models
vendor site · Guideflow18 answers · 26 citations · 11 models
vendor site · Atlassian18 answers · 19 citations · 9 models
vendor site · Augment Code18 answers · 18 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.
34 answers · 11 models
25 answers · 10 models
24 answers · 11 models
19 answers · 9 models
18 answers · 10 models
18 answers · 8 models
18 answers · 9 models
16 answers · 10 models
15 answers · 8 models
12 answers · 6 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 incident management platform”, “incident management platform”, “incident management 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 |
|---|---|---|---|---|---|---|---|---|
| incident.io incident.io | 32% | 172 | 1,900 | 70 | 65,369 | $108 | 12 | 1 |
| PagerDuty pagerduty.com | 15% | 249 | 27,100 | 685 | 82,470 | $1,433 | 118 | |
| Freshservice freshworks.com | 10% | 381 | 8,100 | 221 | 111,586 | $14,486 | 113 | |
| Squadcast squadcast.com | 8% | 57 | 2,900 | 41 | 47 | |||
| Grafana IRM grafana.com | 7% | 40 | 260 | 4 | 84,520 | 116 | ||
| Opsgenie atlassian.com Opsgenie 7%, Jira Service Management 0%, Atlassian Statuspage 0% | 7% | 292 | 6,600 | 188 | 4,724,388 | $5,906 | 61 | 4 |
| Rootly rootly.com | 5% | 105 | 1,900 | 30 | 13,774 | 23 | 2 | |
| Better Stack betterstack.com | 5% | 285 | 1,600 | 1,017 | 41,811 | 119 | ||
| Spiceworks spiceworks.com Spiceworks 2%, Spiceworks Cloud Help Desk 2% | 3% | 30 | 5,400 | 122 | 2,284,947 | |||
| FireHydrant firehydrant.com | 2% | 41 | 49,500 | 19 | 20,080 | 6 |
Google's top ten for the three searches hold 18 sites; 4 of them are among the sites the models cited here (atlassian.com, augmentcode.com, incident.io, rootly.com). The first result for “best incident management platform” is incident.io.
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 incident management platform for a mid-market B2B company?”Paraphrase“Which on-call scheduling and alerting tool would you recommend to a mid-sized B2B company?”Comparative“What are the top incident management platforms and how do they differ?”Budget-constrained“What is the best incident management platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating an incident management platform. What should we look at?”Negative“Which incident management platforms should I avoid or be cautious about?”
Claude Haiku 4.5Freshserviceagainst: PagerDutyOpsgenieChangedno first choiceagainst: OpsgenieSquadcastno first choiceagainst: HaloITSM, Instatus, Jira Service Management, ManageEngine ServiceDesk Plus, Opsgenie, PagerDuty, incident.io
Seven alternatives
HaloITSM, Hyperping, ManageEngine ServiceDesk Plus, Rootly, Zenduty, ilert, incident.ioThree alternatives
Grafana IRM, PagerDuty, SquadcastTwo alternatives
Hyperping, Spike.shGPT-5.4 miniincident.ioPagerDutyChangedagainst: OpsgeniePagerDuty, incident.ioagainst: Opsgenieincident.ioagainst: Opsgenie, PagerDutyno first choiceagainst: Blameless, Datadog On-Call, FireHydrant, Grafana IRM, Opsgenie, PagerDuty, Splunk On-Call, xMatters
Two alternatives
PagerDuty, RootlyOne alternative
Jira Service ManagementOne alternative
xMattersTwo alternatives
Grafana IRM, RootlyGemini 3.5 Flashincident.ioagainst: Opsgenie, PagerDutyRootly, incident.ioChangedagainst: Grafana IRM, Opsgenie, PagerDutyPagerDuty, incident.ioagainst: Freshservice, Opsgenie, ServiceNow ITSMBetter Stackincident.ioagainst: Opsgenie, PagerDutyagainst: BMC Remedy, FireHydrant, Opsgenie, PagerDuty, ServiceNow ITSM
Three alternatives
FireHydrant, Freshservice, RootlyOne alternative
Better StackTwo alternatives
Jira Service Management, RootlySix alternatives
GoAlert, Grafana IRM, Jira Service Management, OneUptime, Squadcast, ZendutyOne alternative
RootlyPerplexity SonarFreshserviceHyperping, incident.ioChangedagainst: PagerDuty, UptimeRobotno first choiceSpiceworks, incident.iono first choiceagainst: BMC Helix ITSM, BigPanda, Jira Service Management, Moogsoft, Opsgenie, PagerDuty, ServiceNow ITSM, xMatters
Three alternatives
PagerDuty, Rootly, incident.ioOne alternative
xMattersFour alternatives
Freshservice, ManageEngine ServiceDesk Plus, Rootly, SquadcastGrok 4.1 FastFreshserviceagainst: ServiceNow ITSMOpsgenieChangedagainst: Splunk On-CallPagerDutyincident.ioPagerDuty, incident.ioagainst: Opsgenie, PagerDuty, ServiceNow ITSM, Splunk On-Call
Five alternatives
InvGate Service Management, Jira Service Management, ManageEngine ServiceDesk Plus, PagerDuty, incident.ioThree alternatives
Grafana IRM, PagerDuty, incident.ioSix alternatives
Opsgenie, Rootly, ServiceNow ITSM, Squadcast, incident.io, xMattersFour alternatives
Jira Service Management, Opsgenie, PagerDuty, SquadcastOne alternative
FreshserviceMistral SmallFreshservice, PagerDutyOpsgenie, PagerDutyChangedPagerDutyagainst: OpsgenieSplunk On-Callno first choiceagainst: Atlassian’s Jira Service Management (JSM) bundled with Opsgenie, Opsgenie
One alternative
ManageEngine ServiceDesk PlusOne alternative
AlertOpsFive alternatives
Datadog Incident Management, FireHydrant, Freshservice, Jira Service Management, incident.ioThree alternatives
Rootly, Squadcast, incident.ioDeepSeek V4 Flashincident.ioagainst: Opsgenie, PagerDutyincident.ioHeldagainst: OpsgeniePagerDutyagainst: ServiceNow ITSM, Splunk On-CallGrafana IRMPagerDuty, incident.ioagainst: Opsgenie, Rootlyagainst: Jira Service Management, Opsgenie, PagerDuty, ServiceNow ITSM, Splunk On-Call, Squadcast
Two alternatives
Freshservice, Jira Service ManagementThree alternatives
Grafana IRM, PagerDuty, RootlyFour alternatives
FireHydrant, Grafana IRM, Opsgenie, incident.ioThree alternatives
Better Stack, Zenduty, incident.ioTwo alternatives
Jira Service Management, Splunk On-CallLlama 4 Maverickno first choiceno first choiceHeldno first choiceSquadcastno first choiceagainst: Opsgenie
Two alternatives
PagerDuty, incident.ioQwen 3.7 FlashPagerDutyRootlyChangedPagerDutyagainst: OpsgenieFireHydrantagainst: PagerDutyPagerDutyagainst: IBM QRadar SOAR, Splunk SOARagainst: NinjaOne, OnPage, Opsgenie, PagerDuty, ServiceNow ITSM, SolarWinds, incident.io
Three alternatives
Datadog Incident Management, FireHydrant, OpsgenieThree alternatives
Grafana IRM, PagerDuty, incident.ioTwo alternatives
Rootly, incident.ioThree alternatives
Atlassian Statuspage, Opsgenie, UptimeRobotFour alternatives
FireHydrant, Opsgenie, Phoebe, StatuspageKimi K2incident.ioagainst: Opsgenie, PagerDutyOpsgenie, incident.ioChangedagainst: PagerDutyPagerDutyagainst: Atlassian, Datadog Incident Management, ServiceNow ITSMBetter Stack, Grafana IRMagainst: Opsgenie, PagerDutyno first choiceagainst: Opsgenie, ServiceNow ITSMagainst: CrowdStrike, PagerDuty, ServiceNow ITSM
One alternative
SquadcastOne alternative
SquadcastThree alternatives
FireHydrant, Rootly, incident.ioTwo alternatives
Jira Service Management, incident.ioGLM 4.7 FlashXRootly, incident.ioGrafana IRMChangedPagerDutyagainst: OpsgenieSpiceworks Cloud Help Desk, Zammadno first choiceagainst: ClickUp, Jira Service Management, ServiceNow ITSM, Zendesk
Two alternatives
Jira Service Management, PagerDutyTwo alternatives
PagerDuty, SquadcastTwo alternatives
Rootly, incident.ioFive alternatives
Grafana IRM, Jira Service Management, Mattermost Incident Response, PagerDuty, incident.ioMiniMax M2.5Freshservice, PagerDutySquadcast, incident.ioChangedagainst: OpsgeniePagerDutyagainst: OpsgenieSquadcast, Zendutyagainst: PagerDutyno first choiceagainst: PagerDuty, ServiceNow ITSM
Two alternatives
Jira Service Management, incident.ioOne alternative
PagerDutyFive alternatives
FireHydrant, Jira Service Management, ServiceNow ITSM, Splunk On-Call, incident.ioOne alternative
incident.ioGPT-6 Lunaincident.ioagainst: Opsgenieincident.ioHeldagainst: OpsgeniePagerDutyagainst: OpsgenieBetter Stackno first choiceagainst: Jira Service Management, Opsgenie, PagerDuty, ServiceNow ITSM, incident.io
Two alternatives
PagerDuty, RootlyOne alternative
PagerDutySix alternatives
Better Stack, Datadog Incident Management, FireHydrant, Rootly, ServiceNow ITSM, incident.ioOne alternative
PagerDutyMuse Glimmer 30BFreshserviceagainst: OpsgenieGrafana IRM, SquadcastChangedagainst: Grafana IRM, Opsgenie, PagerDutyPagerDuty, incident.ioagainst: Opsgenieincident.ioPagerDutyagainst: Opsgenie, PagerDuty
Three alternatives
PagerDuty, Rootly, incident.ioTwo alternatives
Pagerly, RootlySeven alternatives
BigPanda, Freshservice, Grafana IRM, Jira Service Management, Rootly, ServiceNow ITSM, Splunk On-CallSix alternatives
AlertOps, Better Stack, FireHydrant, Grafana IRM, Opsgenie, SquadcastTwo alternatives
Opsgenie, Splunk On-CallBold 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 08:35 | yes | 9 | 13 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:59 | yes | 4 | 7 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:26 | yes | 15 | 23 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 11:27 | yes | 19 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 13:11 | yes | 21 | 10 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:15 | yes | 5 | 3 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:10 | yes | 23 | 28 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:19 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:40 | no | 0 | 37 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:53 | yes | 17 | 24 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:53 | yes | 23 | 53 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:56 | yes | 11 | 26 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:11 | yes | 4 | 15 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:46 | yes | 14 | 15 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:29 | no | 0 | 5 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:40 | yes | 3 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:58 | yes | 14 | 28 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 12:44 | yes | 18 | 3 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:05 | yes | 22 | 7 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:45 | yes | 5 | 5 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:50 | yes | 23 | 25 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:43 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:07 | yes | 10 | 40 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:50 | yes | 19 | 23 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:14 | yes | 25 | 42 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:03 | yes | 10 | 15 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:17 | yes | 3 | 15 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:17 | yes | 13 | 22 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:57 | yes | 9 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 13:52 | yes | 7 | 14 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:24 | yes | 13 | 26 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 09:17 | yes | 19 | 5 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 07:37 | yes | 22 | 8 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:31 | yes | 13 | 10 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:09 | yes | 25 | 47 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:03 | yes | 5 | 3 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:13 | yes | 10 | 49 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:33 | yes | 20 | 38 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 13:23 | yes | 25 | 46 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:14 | yes | 13 | 21 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:38 | yes | 8 | 30 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:07 | yes | 14 | 29 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:01 | yes | 9 | 10 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:31 | yes | 2 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:15 | yes | 20 | 22 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 09:38 | yes | 17 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:58 | yes | 18 | 5 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:39 | yes | 5 | 2 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:25 | yes | 24 | 19 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:17 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 11:06 | no | 0 | 33 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:54 | yes | 15 | 28 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 09:52 | yes | 24 | 113 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:05 | yes | 5 | 8 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 08:31 | yes | 2 | 11 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:48 | yes | 13 | 33 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 10:37 | no | 0 | 7 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:13 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:43 | yes | 12 | 28 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 07:53 | yes | 19 | 5 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:35 | yes | 12 | 13 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:07 | no | 0 | 8 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:33 | yes | 25 | 46 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:50 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 13:33 | no | 0 | 37 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:16 | yes | 13 | 26 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 13:16 | yes | 19 | 68 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:36 | yes | 5 | 9 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:21 | yes | 4 | 16 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:12 | yes | 12 | 23 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 13:38 | yes | 16 | 9 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:05 | yes | 2 | 5 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:00 | yes | 14 | 21 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 07:45 | yes | 20 | 3 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:31 | yes | 20 | 9 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:09 | yes | 5 | 3 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:05 | yes | 23 | 40 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:12 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:01 | yes | 14 | 35 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:25 | yes | 25 | 20 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:06 | yes | 24 | 24 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 09:35 | yes | 15 | 19 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:38 | yes | 5 | 17 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:12 | yes | 25 | 26 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
BMC Helix read as BMC Helix ITSM
ServiceNow read as ServiceNow ITSM
ServiceNow (IT Incident Module) read as ServiceNow ITSM
ServiceNow IT Service Management read as ServiceNow ITSM
Unresolved, counted raw
Atlassian’s Jira Service Management (JSM) bundled with Opsgenie
Blameless
ClickUp Incident Management
ManageEngine Service Desk
Phoebe
Discontinued, still offered
No shut-down product was recommended here.