ML platforms for enterprise buyers
Asked as “machine learning platform”, and as “MLOps platform for training and deploying models”, on behalf of an enterprise B2B company. 42 first choices recorded across the direct, paraphrase, budget and scale prompts, twelve models each.
Standing · first-choice share
33%
Contested · Amazon SageMaker 19%
33Databricks Mosaic AI19Amazon SageMaker12Azure Machine Learning36others
33% of first choices, contested.
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 twelve models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the one product at 0%, ordered by negative rate
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.
Google Vertex AI is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice one time of 4; the other eleven models one time of 44. Lab treatment is defined on the method page; the row is marked, not excluded.
Head to head
Databricks Mosaic AI vs Azure Machine LearningDatabricks Mosaic AI vs Amazon SageMakerDatabricks Mosaic AI vs KNIME Analytics Platform
Head-to-head pages are published for mid-market buyers, so these open on mid-market figures, not enterprise.All twenty-eight head-to-head pages: the top eight products, each against each
Databricks Mosaic AI vs Google Vertex AIDatabricks Mosaic AI vs DataikuDatabricks Mosaic AI vs TensorFlowDatabricks Mosaic AI vs MLflowAzure Machine Learning vs Amazon SageMakerAzure Machine Learning vs KNIME Analytics PlatformAzure Machine Learning vs Google Vertex AIAzure Machine Learning vs DataikuAzure Machine Learning vs TensorFlowAzure Machine Learning vs MLflowAmazon SageMaker vs KNIME Analytics PlatformAmazon SageMaker vs Google Vertex AIAmazon SageMaker vs DataikuAmazon SageMaker vs TensorFlowAmazon SageMaker vs MLflowKNIME Analytics Platform vs Google Vertex AIKNIME Analytics Platform vs DataikuKNIME Analytics Platform vs TensorFlowKNIME Analytics Platform vs MLflowGoogle Vertex AI vs DataikuGoogle Vertex AI vs TensorFlowGoogle Vertex AI vs MLflowDataiku vs TensorFlowDataiku vs MLflowTensorFlow vs MLflow
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 →
01
02
03
04
05
06
07I08
09
10Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative50%
Key
01Databricks Mosaic AI33%
Databricks Mosaic AI33%02Amazon SageMaker19%
Amazon SageMaker19%03Azure Machine Learning12%
Azure Machine Learning12%04Dataiku7%
Dataiku7%05Domino Data Lab7%
Domino Data Lab7%06Google Vertex AI5%
Google Vertex AI5%07DataRobot5%
DataRobot5%08IBM watsonx.ai2%
09H2O.ai2%
H2O.ai2%10MLflow0%
MLflow0%What they warned about
One of twelve models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Mistral Small, DeepSeek V4 Flash, Llama 4 Maverick, Qwen 3.7 Flash, Kimi K2, 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.
Google Vertex AI19%
10 of 52 labels negative · 8 of 12 models · 2 hard negative
“Google Vertex AI has historically scored significantly lower... Why avoid it: It is often cited as being complex and fragmented.” GLM 4.7 FlashX, negative prompt
Amazon SageMaker21%
12 of 56 labels negative · 8 of 12 models · 1 hard negative
“Platforms to Avoid for Predictable Costs ... AWS SageMaker | Pay-per-hour for notebooks, training, inference, storage—costs explode with usage” Kimi K2, budget prompt
Azure Machine Learning12%
6 of 51 labels negative · 5 of 12 models · 1 hard negative
“Similar usage-based model; compute costs vary dramatically” Kimi K2, budget prompt
Kubeflow62%
5 of 8 labels negative · 5 of 12 models · 1 hard negative
“Kubeflow | Avoid for simple use cases | Unnecessary complexity and overhead” Kimi K2, negative prompt
What they cite
Twelve of the twelve models returned a source list in this edition: four return the pages they cited and eight the search results they consulted.
Sites the answers cite
62 of 72 answers in this category came back with a source list, from 12 of 12 models: citations where the model returns them, or the search results it consulted. 989 links across 356 sites, every framing counted. Ranked by the number of answers carrying the site or page.
vendor site · Dataiku26 answers · 30 citations · 10 models
25 answers · 34 citations · 10 models
vendor site · G222 answers · 27 citations · 10 models
vendor site · G220 answers · 30 citations · 10 models
vendor site · TrueFoundry17 answers · 17 citations · 9 models
16 answers · 16 citations · 9 models
14 answers · 20 citations · 7 models
13 answers · 15 citations · 8 models
11 answers · 18 citations · 4 models
vendor site · Databricks11 answers · 15 citations · 5 models
11 answers · 12 citations · 6 models
vendor site · Google10 answers · 17 citations · 6 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.
20 answers · 9 models
20 answers · 9 models
16 answers · 9 models
15 answers · 9 models
15 answers · 8 models
10 answers · 7 models
10 answers · 5 models
10 answers · 7 models
10 answers · 6 models
10 answers · 8 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 machine learning platform”, “machine learning platform”, “machine learning 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 |
|---|---|---|---|---|---|---|---|
| Databricks databricks.com Databricks Mosaic AI 33% | 33% | 125 | 135,000 | 8,368 | 689,756 company | $34,664 | 120 |
| Amazon parent site Amazon SageMaker 19% | 19% | 159 | 151,000,000 | 1,380,939 | |||
| Azure parent site Azure Machine Learning 12%, Azure AI Foundry 0%, Azure OpenAI Service 0% | 12% | 148 | 301,000 | 47,904 | |||
| Dataiku dataiku.com | 7% | 74 | 9,900 | 266 | 12,714 | 93 | |
| Domino domino.ai Domino Data Lab 7% | 7% | 28 | 1,900 | 57 | 18,579 company | 36 | |
| Google Vertex AI cloud.google.com | 5% | 135 | 5,400 | 226 | 7,405,347 | ||
| DataRobot datarobot.com | 5% | 79 | 5,400 | 169 | 9,791 | 25 | |
| IBM parent site IBM watsonx.ai 2%, IBM Watson Machine Learning 0%, IBM Watson Studio 0% | 2% | 16 | 201,000 | 34,098 | |||
| Snowflake Cortex AI snowflake.com | 2% | 28 | 2,900 | 87 | 718,749 | 118 | |
| H2O.ai h2o.ai | 2% | 28 | 880 | 78 | 59,586 |
Google's top ten for the three searches hold 22 sites; 1 of them are among the sites the models cited here (gartner.com). The first result for “best machine learning platform” is anaconda.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 machine learning platform for an enterprise B2B company?”Paraphrase“Which MLOps platform for training and deploying models would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade machine learning platforms and how do they differ?”Budget-constrained“What is the best machine learning platform 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 machine learning platform. What should we look at?”Negative“Which machine learning platforms should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5Amazon SageMakerDatabricks Mosaic AIChangedagainst: Kubeflowno first choiceAmazon SageMakerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI
Six alternatives
Databricks Mosaic AI, Domino Data Lab, Google Vertex AI, H2O.ai, IBM watsonx.ai, Snowflake Cortex AIThree alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AISeven alternatives
Azure Machine Learning, Databricks Mosaic AI, Domino Data Lab, Google Vertex AI, H2O.ai, NVIDIA AI Enterprise, Snowflake Cortex AIGPT-5.4 miniDatabricks Mosaic AIAzure Machine Learning, Databricks Mosaic AIChangedno first choiceSnowflake Cortex AIno first choicenothing named
Three alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AITwo alternatives
Amazon SageMaker, Google Vertex AIOne alternative
Databricks Mosaic AIGemini 3.5 FlashGoogle Vertex AIDatabricks Mosaic AIChangedDatabricks Mosaic AIDataikuagainst: Amazon SageMaker, Databricks Mosaic AI, Google Vertex AIno first choiceagainst: Alteryx, Apache Airflow, DataRobot, H2O Driverless AI, IBM SPSS, Kubeflow, SAS
Six alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Databricks Mosaic AI, Dataiku, IBM watsonx.aiSix alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Domino Data Lab, Google Vertex AI, MLflowFive alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Google Vertex AI, IBM watsonx.aiTwo alternatives
DataRobot, Domino Data LabPerplexity SonarAzure Machine LearningDatabricks Mosaic AIChangedno first choiceDataikuno first choicenothing named
Five alternatives
Databricks Mosaic AI, Gemini Enterprise Agent Platform, Google Vertex AI, IBM watsonx.ai, SAS ViyaSeven alternatives
Amazon SageMaker, Azure Machine Learning, Dataiku, Domino Data Lab, Google Vertex AI, Kubeflow, MLflowThree alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AIGrok 4.20Databricks Mosaic AIDatabricks Mosaic AIHeldno first choiceDataRobot, Domino Data Labno first choiceagainst: Amazon SageMaker, DataRobot, Google Vertex AI, H2O.ai, Kubeflow, MLflow, RapidMiner, TensorFlow Extended/TFX
Four alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Google Vertex AIThree alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AIFive alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Dataiku, Google Vertex AIMistral SmallAzure Machine Learning, Databricks Mosaic AIDatabricks Mosaic AIChangedDatabricks Mosaic AIGemini Enterprise Agent Platform, Machine Learning at Scaleagainst: Amazon SageMakerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, HPE Ezmeral
Three alternatives
Amazon SageMaker, DataRobot, Google Vertex AIThree alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AINine alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Dataiku, Domino Data Lab, Google Vertex AI, H2O.ai, IBM Watson Studio, SAS ViyaDeepSeek V4 FlashAmazon SageMakeragainst: DataRobotDatabricks Mosaic AIChangedno first choiceDomino Data Labagainst: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI, H2O.aino first choiceagainst: AWS DeepComposer, AWS DeepLens, Amazon Bedrock, Amazon Lookout for Equipment, Amazon SageMaker, Anthropic, Azure Machine Learning, DataRobot, Dataiku, Google Vertex AI, H2O Driverless AI, OpenAI
Six alternatives
Azure AI Foundry, Azure Machine Learning, Databricks Mosaic AI, Dataiku, Google Vertex AI, IBM watsonx.aiThree alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AIEight alternatives
Amazon SageMaker, DataRobot, Databricks Mosaic AI, Dataiku, Domino Data Lab, Google Vertex AI, H2O.ai, Microsoft Azure Machine Learning / Azure AI FoundryThree alternatives
Anaconda, DataRobot, DataikuLlama 4 Maverickno first choiceDatabricks Mosaic AIChangedno first choiceDataRobot, Domino Data Labno first choiceagainst: Databricks Mosaic AI, MLflow
Five alternatives
Amazon SageMaker, Azure Machine Learning, Dataiku, Google Vertex AI, TrueFoundryThree alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AIQwen 3.7 Flashno first choiceDatabricks Mosaic AIChangedagainst: Kubeflowno first choiceAzure Machine Learningagainst: Amazon SageMakerno first choiceagainst: Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Hugging Face, Jupyter Notebook, Kaggle
Seven alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Databricks Mosaic AI, Google Vertex AI, Palantir Foundry, Snowflake Cortex AIFive alternatives
Amazon SageMaker, Azure Machine Learning, DataRobot, Domino Data Lab, Google Vertex AIFive alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Dataiku, Google Vertex AIThree alternatives
Databricks Mosaic AI, Hugging Face, Weights & BiasesKimi K2Amazon SageMakerDatabricks Mosaic AIChangedno first choiceDataiku, H2O.aiagainst: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AIno first choiceagainst: Amazon SageMaker, ChatGPT, Claude, DagsHub, Google Vertex AI, Kubeflow
Four alternatives
Azure Machine Learning, DataRobot, Databricks Mosaic AI, Google Vertex AIFour alternatives
Amazon SageMaker, Azure Machine Learning, Google Vertex AI, MLflowOne alternative
AlteryxGLM 4.7 FlashXDatabricks Mosaic AIAmazon SageMakerChangedAmazon SageMaker, Databricks Mosaic AIAmazon SageMakerno first choiceagainst: Amazon SageMaker, Flowise, Google Vertex AI, Open WebUI, n8n
Eight alternatives
Amazon SageMaker, Azure Machine Learning, C3 AI, DataRobot, Dataiku, Google Vertex AI, IBM watsonx.ai, Palantir FoundryThree alternatives
Azure Machine Learning, Databricks Mosaic AI, IBM Watson Machine LearningThree alternatives
Azure Machine Learning, DataRobot, Google Vertex AITwo alternatives
Azure Machine Learning, DataRobotMiniMax M2.5Amazon SageMaker, Google Vertex AIAmazon SageMaker, Azure Machine LearningChangedno first choiceIBM watsonx.aino first choicenothing named
Three alternatives
Azure Machine Learning, Databricks Mosaic AI, DataikuThree alternatives
Dataiku, Google Vertex AI, Weights & BiasesThree alternatives
Amazon SageMaker, Azure Machine Learning, Databricks Mosaic 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.
Seventy-two 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 | 2026-09-17 11:07 | yes | 9 | 9 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 12:58 | no | 0 | 4 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 12:33 | yes | 9 | 20 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-09-17 13:10 | yes | 20 | 6 s |
| Direct recommendation | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 13:56 | yes | 37 | 16 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 10:12 | yes | 11 | 8 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 13:01 | yes | 10 | 16 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 09:46 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 11:21 | no | 0 | 33 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:43 | yes | 10 | 22 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:15 | yes | 9 | 191 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 12:54 | yes | 5 | 20 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:26 | no | 0 | 6 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 11:31 | yes | 3 | 6 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 12:08 | yes | 14 | 120 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-09-17 10:24 | yes | 20 | 7 s |
| Paraphrase | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 10:50 | yes | 40 | 16 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 14:11 | yes | 12 | 13 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 11:56 | yes | 18 | 18 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:30 | yes | 5 | 3 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 12:31 | yes | 10 | 61 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 12:51 | yes | 15 | 29 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:05 | yes | 28 | 48 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 13:52 | yes | 5 | 25 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 10:14 | yes | 9 | 11 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 09:42 | yes | 6 | 13 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 09:38 | yes | 11 | 26 s |
| Comparative | Perplexity Sonar | sonar | 2026-09-17 13:02 | yes | 20 | 8 s |
| Comparative | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 12:37 | yes | 19 | 11 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 11:31 | yes | 5 | 9 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 12:38 | yes | 17 | 21 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 13:50 | yes | 5 | 3 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 13:55 | yes | 10 | 48 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:29 | yes | 15 | 40 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 13:41 | yes | 25 | 293 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 10:35 | yes | 10 | 28 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 10:11 | yes | 16 | 8 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 11:08 | yes | 3 | 5 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 10:40 | yes | 23 | 33 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-09-17 12:28 | yes | 20 | 6 s |
| Budget constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 10:49 | yes | 40 | 21 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 11:34 | yes | 5 | 6 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 12:52 | yes | 27 | 39 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 10:08 | yes | 5 | 2 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 14:11 | no | 0 | 43 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:10 | yes | 19 | 37 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 12:43 | yes | 33 | 103 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 13:34 | yes | 20 | 108 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:50 | no | 0 | 9 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 12:23 | no | 0 | 8 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 13:46 | yes | 8 | 25 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-09-17 11:18 | yes | 15 | 17 s |
| Scale constrained | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 13:44 | yes | 15 | 12 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 12:12 | no | 0 | 9 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 11:19 | yes | 23 | 30 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 11:28 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 10:59 | no | 0 | 38 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 14:14 | yes | 19 | 39 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 11:16 | yes | 24 | 133 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 10:20 | yes | 5 | 46 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-09-17 13:36 | yes | 18 | 10 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-09-17 10:55 | yes | 5 | 8 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-09-17 12:39 | yes | 23 | 29 s |
| Negative framing | Perplexity Sonar | sonar | 2026-09-17 10:13 | yes | 20 | 8 s |
| Negative framing | Grok 4.20 | spacexai/grok-4.1-fast-non-reasoning via vertex (asked for grok-4.20-non-reasoning; the API answered as this version) | 2026-09-17 12:47 | yes | 43 | 23 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-09-17 13:42 | yes | 15 | 11 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via fireworks | 2026-09-17 09:57 | yes | 30 | 41 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-09-17 12:12 | yes | 5 | 3 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-09-17 14:04 | no | 0 | 41 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-09-17 11:56 | yes | 30 | 84 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-09-17 11:40 | yes | 28 | 69 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-09-17 13:56 | no | 0 | 51 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
Azure OpenAI read as Azure OpenAI Service
Databricks read as Databricks Mosaic AI
Databricks (Mosaic AI / MLflow ecosystem) read as Databricks Mosaic AI
Databricks (Mosaic AI) read as Databricks Mosaic AI
Databricks Data + AI Platform read as Databricks Mosaic AI
Databricks Data Intelligence Platform read as Databricks Mosaic AI
Databricks Lakehouse read as Databricks Mosaic AI
Domino read as Domino Data Lab
IBM Watsonx read as IBM watsonx.ai
IBM watsonx read as IBM watsonx.ai
Palantir read as Palantir Foundry
Snowflake read as Snowflake Cortex AI
Snowflake (with Snowpark) read as Snowflake Cortex AI
Snowflake Cortex read as Snowflake Cortex AI
Snowflake ML read as Snowflake Cortex AI
Unresolved, counted raw
AWS DeepComposer
AWS DeepLens
Amazon Lookout for Equipment
CreateOS
HPE Ezmeral
IBM SPSS
Machine Learning at Scale
Open WebUI
Opstream
Profound
TensorFlow Extended/TFX
Discontinued, still offered
No shut-down product was recommended here.