Time series databases
Asked as “time series database”, and as “database for metrics and sensor data”, on behalf of a mid-market B2B company. 51 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each. Added to the October 2026 Edition on October 4, 2026; its answers are read by the distilled judge (ai-indexes-judge-qwen3-14b-run3), not the claude-opus-5 judge of the earlier categories: how the two compare.
Standing · first-choice share
63%
TimescaleDBClear leader
63TimescaleDB16InfluxDB08VictoriaMetrics14others
63% of first choices, clear leader.
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.
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
Show the three products at 0%, ordered by negative rate
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 fifteen head-to-head pages: the top six products, each against each
One product takes 63% of first choices here, so the chart would put eight markers in one corner and one at the far edge. The two measurements it plots are columns in the standing above: share, and the negative label rate. One product carries a negative rate above 25% in this category.
What they warned about
Six of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, Perplexity Sonar, Grok 4.1 Fast, Mistral Small, DeepSeek V4 Flash, Llama 4 Maverick, GLM 4.7 FlashX and MiniMax M2.5 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.
InfluxDB19%
13 of 70 labels negative · 10 of 14 models · 3 hard negative
“Older versions (v1) reached end-of-life years ago, with no security patches or support—avoid for new projects.” Grok 4.1 Fast, negative prompt
OpenTSDB
89%
8 of 9 labels negative · 8 of 14 models · 5 hard negative
“it's outdated (last major updates ~2010s), hard to operate at scale ... When to skip: Anything but massive, distributed legacy setups” Grok 4.1 Fast, negative prompt
Prometheus19%
8 of 43 labels negative · 8 of 14 models
“Prometheus local storage as your only durable, highly available store.** ... its local TSDB isn't clustered or replicated.” GPT-6 Luna, negative prompt
MySQL100%
6 of 6 labels negative · 6 of 14 models · 1 hard negative
“standard relational databases like MySQL, SQL Server, or PostgreSQL can handle small-scale time series data, they are not optimized for large volumes” Mistral Small, 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
72 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. 923 links across 176 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 · TigerData56 answers · 112 citations · 12 models
vendor site · Guideflow53 answers · 53 citations · 12 models
vendor site · QuestDB43 answers · 54 citations · 13 models
vendor site · InfluxData38 answers · 58 citations · 13 models
29 answers · 31 citations · 11 models
29 answers · 29 citations · 9 models
22 answers · 23 citations · 9 models
vendor site · SourceForge17 answers · 19 citations · 10 models
vendor site · G216 answers · 17 citations · 8 models
vendor site · Amazon14 answers · 21 citations · 9 models
14 answers · 16 citations · 8 models
vendor site · FitGap13 answers · 17 citations · 9 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.
53 answers · 12 models
49 answers · 12 models
37 answers · 12 models
29 answers · 9 models
27 answers · 10 models
21 answers · 9 models
18 answers · 8 models
13 answers · 6 models
12 answers · 7 models
11 answers · 7 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 time series database”, “time series database”, “time series databases”. 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 |
|---|---|---|---|---|---|---|---|---|
| TigerData tigerdata.com TimescaleDB 63%, Tiger Cloud 2%, TigerData 0% | 65% | 183 | 3,600 | 147 | 23,549 | $658 | 27 | 2 |
| InfluxDB influxdata.com | 16% | 176 | 4,400 | 350 | 46,684 | 44 | 3 | |
| VictoriaMetrics victoriametrics.com | 8% | 107 | 1,300 | 52 | 4,097 | 0 | 6 | |
| QuestDB questdb.com | 4% | 142 | 880 | 6 | 14,289 | 2 | 4 | |
| Prometheus prometheus.io | 2% | 257 | 246,000 | 11,819 | 22,996 | |||
| InfluxDB OSS | 2% | |||||||
| InfluxDB 3.x | 2% | |||||||
| PostgreSQL postgresql.org | 2% | 82 | 74,000 | 14,143 | 465,585 | |||
| ClickHouse Cloud clickhouse.com | 0% | 178 | 1,000 | 15 | 43,805 | 47 | ||
| Amazon parent site Amazon Timestream 0%, Amazon Timestream for InfluxDB 0% | 0% | 65 | 151,000,000 | 1,380,939 |
Google's top ten for the three searches hold 24 sites; 4 of them are among the sites the models cited here (aws.amazon.com, influxdata.com, questdb.com, tigerdata.com). The first result for “best time series database” is reddit.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 time series database for a mid-market B2B company?”Paraphrase“Which database for metrics and sensor data would you recommend to a mid-sized B2B company?”Comparative“What are the top time series databases and how do they differ?”Budget-constrained“What is the best time series database for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a time series database. What should we look at?”Negative“Which time series databases should I avoid or be cautious about?”
Claude Haiku 4.5InfluxDB, TimescaleDBagainst: Grafana LokiInfluxDBChangedno first choicePrometheusno first choiceagainst: Apache Druid, ClickHouse Cloud, Graphite, InfluxDB, MongoDB Time Series Collections, MySQL, PostgreSQL, TimescaleDB
One alternative
PrometheusTwo alternatives
TimescaleDB, VictoriaMetricsFour alternatives
InfluxDB 3 Core, QuestDB, TimescaleDB, VictoriaMetricsGPT-5.4 miniTimescaleDBTimescaleDBHeldno first choiceagainst: ClickHouse CloudPostgreSQL, TimescaleDBno first choiceagainst: InfluxData, VictoriaMetrics
Three alternatives
ClickHouse Cloud, InfluxDB, QuestDBThree alternatives
ClickHouse Cloud, InfluxDB, PostgreSQLThree alternatives
ClickHouse Cloud, InfluxDB OSS, VictoriaMetricsGemini 3.5 FlashTimescaleDBTimescaleDBHeldTimescaleDBTimescaleDBno first choiceagainst: Apache Cassandra, Blueflood, Elasticsearch, Graphite, InfluxDB, KairosDB, MySQL, OpenTSDB, Prometheus, ScyllaDB, Standard PostgreSQL
Two alternatives
ClickHouse Cloud, InfluxDBTwo alternatives
ClickHouse Cloud, InfluxDBFive alternatives
ClickHouse Cloud, InfluxDB, Prometheus, QuestDB, VictoriaMetricsThree alternatives
ClickHouse Cloud, QuestDB, VictoriaMetricsFive alternatives
ClickHouse Cloud, InfluxDB, QuestDB, Timescale Cloud, VictoriaMetricsPerplexity SonarTimescaleDBTiger Cloud, TimescaleDBChangedTimescaleDBInfluxDB OSS, VictoriaMetricsagainst: Amazon Timestream, Timescale Cloudno first choiceagainst: InfluxDB, Microsoft SQL Server, MySQL, PostgreSQL, Prometheus, QuestDB
Three alternatives
Amazon Timestream, InfluxDB, QuestDBThree alternatives
Amazon Timestream, InfluxDB, QuestDBSix alternatives
Amazon Timestream, ClickHouse Cloud, InfluxDB, Prometheus, QuestDB, VictoriaMetricsOne alternative
QuestDBGrok 4.1 FastInfluxDBTimescaleDBChangedno first choiceTimescaleDBTimescaleDBagainst: PostgreSQLagainst: Amazon Timestream, Graphite, InfluxDB, KairosDB, MySQL, OpenTSDB, PostgreSQL, Prometheus, RRDtool
Two alternatives
ClickHouse Cloud, TimescaleDBThree alternatives
InfluxDB, Prometheus, QuestDBFour alternatives
InfluxDB, Prometheus, QuestDB, VictoriaMetricsThree alternatives
ClickHouse Cloud, InfluxDB, QuestDBMistral SmallTimescaleDBInfluxDBChangedno first choiceInfluxDB, TimescaleDBno first choiceagainst: Microsoft SQL Server, MySQL, PostgreSQL
Two alternatives
InfluxDB, QuestDBTwo alternatives
Prometheus, QuestDBDeepSeek V4 FlashTimescaleDBInfluxDBChangedInfluxDBTimescaleDB, VictoriaMetricsno first choiceagainst: Amazon Timestream, Azure Time Series, Google Cloud Bigtableagainst: CrateDB, Gnocchi, InfluxDB, KairosDB, OpenTSDB, Prometheus, TDengine, TimescaleDB
Three alternatives
ClickHouse Cloud, InfluxDB, QuestDBThree alternatives
Prometheus, TimescaleDB, VictoriaMetricsSeven alternatives
ClickHouse Cloud, Prometheus, QuestDB, TDengine, TimescaleDB, VictoriaMetrics, kdb+Three alternatives
InfluxDB, Prometheus, QuestDBLlama 4 Maverickno first choiceQuestDBChangedno first choiceVictoriaMetricsno first choicenothing named
Three alternatives
InfluxDB, Prometheus, VictoriaMetricsTwo alternatives
DolphinDB, PostgreSQLQwen 3.7 FlashTimescaleDBTimescaleDBHeldagainst: MySQL, PostgreSQLno first choiceTimescaleDBno first choiceagainst: Apache Cassandra, Graphite, InfluxDB, MongoDB, OpenTSDB, RRDtool, Raw PostgreSQL / MySQL, kdb+
Two alternatives
InfluxDB, QuestDBThree alternatives
InfluxDB, QuestDB, TigerDataThree alternatives
ClickHouse Cloud, InfluxDB, VictoriaMetricsKimi K2TimescaleDBTimescaleDBHeldInfluxDBVictoriaMetricsagainst: InfluxDBno first choiceagainst: InfluxDB, OpenTSDB, Prometheus
Two alternatives
InfluxDB, QuestDBTwo alternatives
InfluxDB, TDengineFive alternatives
ClickHouse Cloud, Prometheus, QuestDB, TimescaleDB, VictoriaMetricsTwo alternatives
Prometheus, TimescaleDBOne alternative
TimescaleDBGLM 4.7 FlashXTimescaleDBagainst: InfluxDB, PrometheusInfluxDBChangedno first choiceInfluxDB 3.x, TimescaleDBno first choiceagainst: AWS Timestream LiveAnalytics, Arc, Azure Time Series Insights, DalmatinerDB, InfluxDB, InfluxDB 1.x / 2.x, InfluxDB line protocol, Netflix Astyanax, QuestDB, Spotify Heroic Time Series Database, TDengine
Two alternatives
ClickHouse Cloud, QuestDBThree alternatives
Prometheus, TimescaleDB, VictoriaMetricsThree alternatives
Prometheus, TDengine, VictoriaMetricsFour alternatives
Amazon Timestream, InfluxDB, Prometheus, TimescaleDBMiniMax M2.5InfluxDB, TimescaleDBTimescaleDBChangedno first choiceTimescaleDBno first choiceagainst: InfluxDB, KairosDB, OpenTSDB, Prometheus
Two alternatives
Amazon Timestream, QuestDBThree alternatives
ClickHouse Cloud, InfluxDB, VictoriaMetricsThree alternatives
InfluxDB, Prometheus, VictoriaMetricsGPT-6 LunaTimescaleDBTimescaleDBHeldagainst: Timestream for LiveAnalyticsno first choiceTimescaleDBagainst: InfluxDB 3 Coreno first choiceagainst: InfluxDB, InfluxDB 3 Core, OpenTSDB, Prometheus
Three alternatives
ClickHouse Cloud, InfluxDB, PrometheusThree alternatives
Amazon Timestream for InfluxDB, ClickHouse Cloud, InfluxDBSeven alternatives
Apache IoTDB, ClickHouse Cloud, InfluxDB, Prometheus, QuestDB, TimescaleDB, VictoriaMetricsOne alternative
VictoriaMetricsFive alternatives
ClickHouse Cloud, InfluxDB, QuestDB, TimescaleDB, VictoriaMetricsMuse Glimmer 30BTimescaleDBTimescaleDBHeldTimescaleDBQuestDBagainst: TDengineno first choiceagainst: Amazon Timestream for LiveAnalytics, InfluxDB 1.x, KairosDB, OpenTSDB, PostgreSQL
Two alternatives
InfluxDB, QuestDBOne alternative
InfluxDBFour alternatives
Amazon Timestream, InfluxDB, Prometheus, QuestDBThree alternatives
InfluxDB 3 Core, TimescaleDB, VictoriaMetricsBold 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 | 2026-10-04 23:44 | no | 0 | 5 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-05 00:44 | yes | 2 | 4 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-04 22:27 | yes | 13 | 35 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-05 01:38 | yes | 17 | 3 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-05 00:33 | yes | 20 | 13 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-05 00:14 | yes | 7 | 6 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-04 23:51 | yes | 21 | 38 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 01:22 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-05 01:29 | yes | 8 | 33 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-05 01:21 | yes | 11 | 18 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-05 01:43 | yes | 10 | 105 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-05 00:54 | yes | 5 | 29 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-04 22:33 | yes | 3 | 10 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-04 23:29 | yes | 17 | 22 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-04 23:20 | yes | 18 | 11 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-04 22:16 | no | 0 | 4 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-05 01:45 | no | 0 | 17 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-05 01:55 | yes | 17 | 2 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-04 23:41 | yes | 13 | 9 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-05 00:35 | no | 0 | 2 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-04 22:17 | yes | 22 | 31 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 01:52 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-04 22:17 | yes | 9 | 32 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-05 01:02 | yes | 13 | 18 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-04 23:05 | yes | 18 | 49 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-04 22:33 | yes | 6 | 36 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-05 01:06 | yes | 4 | 14 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-04 23:36 | yes | 12 | 23 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-05 00:16 | yes | 9 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-05 00:24 | yes | 6 | 6 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-04 23:33 | yes | 19 | 29 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-05 00:37 | yes | 18 | 4 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-04 23:56 | yes | 19 | 10 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-05 00:06 | yes | 10 | 8 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-05 01:46 | yes | 16 | 96 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 00:13 | yes | 5 | 2 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-04 22:47 | no | 0 | 25 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-05 00:19 | yes | 12 | 34 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-05 00:07 | yes | 15 | 29 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-05 01:06 | yes | 9 | 29 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-05 01:48 | yes | 7 | 14 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-04 22:53 | yes | 12 | 21 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-05 00:33 | yes | 17 | 9 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-05 01:04 | no | 0 | 4 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-05 01:18 | yes | 11 | 30 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-04 23:11 | yes | 15 | 2 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-04 22:54 | yes | 22 | 11 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-05 01:37 | yes | 10 | 6 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-05 01:07 | yes | 20 | 45 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 00:58 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-04 23:16 | yes | 5 | 43 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-05 01:51 | yes | 16 | 33 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-05 01:17 | yes | 10 | 19 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-05 01:43 | yes | 14 | 43 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-05 01:48 | yes | 3 | 14 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-05 00:55 | yes | 11 | 16 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-05 00:06 | no | 0 | 5 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-04 22:30 | no | 0 | 7 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-05 00:22 | yes | 7 | 23 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-04 23:19 | yes | 15 | 6 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-05 00:27 | yes | 14 | 10 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-05 00:45 | no | 0 | 10 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-04 23:47 | yes | 19 | 40 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 01:56 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-04 22:47 | no | 0 | 27 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-04 23:56 | yes | 19 | 25 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-05 01:12 | yes | 22 | 114 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-05 00:19 | no | 0 | 37 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-05 00:17 | yes | 4 | 19 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-05 01:01 | yes | 11 | 19 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-04 23:18 | yes | 18 | 9 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-05 00:14 | yes | 3 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-04 22:25 | yes | 19 | 34 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-04 23:01 | yes | 19 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-05 00:04 | yes | 21 | 11 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-04 23:43 | yes | 5 | 6 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-04 22:45 | yes | 25 | 36 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-05 00:39 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-04 23:51 | no | 0 | 27 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-04 22:54 | yes | 23 | 27 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-04 23:33 | yes | 24 | 151 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-04 22:20 | yes | 25 | 36 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-05 01:32 | yes | 4 | 13 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-05 00:19 | yes | 24 | 27 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
ShowHide
Category-scoped readings
AWS Timestream read as Amazon Timestream
Unresolved, counted raw
AWS Timestream LiveAnalytics
Azure Time Series
Blueflood
Cisco Cleveland
DalmatinerDB
Gnocchi
InfluxDB 1.x
InfluxDB 1.x / 2.x
InfluxDB 3.x
InfluxDB line protocol
M3
MongoDB Time Series Collections
Netflix Astyanax
Raw PostgreSQL / MySQL
Spotify Heroic Time Series Database
Standard PostgreSQL
Timestream for LiveAnalytics
Discontinued, still offered
No shut-down product was recommended here.