ML platforms · November 2026 Edition
PyTorch vs TensorFlow
Zero of fourteen models named PyTorch first on the direct prompt; zero named TensorFlow. PyTorch was named by eight of the fourteen models and TensorFlow by six and both carry 10 labels, so the shares below are directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share3%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate10%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 15; printed, not drawn.
Labels1010Equal, which is what makes the shares comparable.
The two percentage rows are drawn on one 0 to 100 track, PyTorch reading right to left. Rank and label count are printed, not drawn.Databricks Mosaic AI was named alongside these two in twelve of the fourteen direct answers. Databricks Mosaic AI vs PyTorch · Databricks Mosaic AI vs TensorFlow · Azure Machine Learning vs PyTorch
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; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the ML platforms page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PyTorchFirst choices, of fourteen modelsTensorFlow
Direct00
Paraphrase00
Comparative00
Budget-constrained22
Scale-constrained001 against PyTorch · 1 against TensorFlow
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Across every category in the November 2026 Edition, PyTorch and TensorFlow were named in the same answer fourteen times, of the 17 answers naming PyTorch and the 19 naming TensorFlow. In those answers TensorFlow took the first choice zero times and PyTorch zero.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where PyTorch and TensorFlow stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | |||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | |||||
| Perplexity Sonar | | |||||
| Grok 4.20 | | |||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | | |||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B | | |
PyTorch TensorFlow first choice named as an alternative argued againstblank: not namedEach cell is one answer, PyTorch on the left and TensorFlow on the right.The direct prompt
The plain question, one answer per model, grouped by where PyTorch and TensorFlow stood in it.
Neither was named
14 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5no first choice alternatives: Amazon SageMaker, Azure Machine Learning, Clearly.ai, DataRobot, Databricks Mosaic AI, Google Vertex AI, H2O AutoML
GPT-5.4 miniDatabricks Mosaic AI alternatives: Amazon SageMaker, Snowflake Cortex AI
Gemini 3.5 FlashDataiku alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, GCP Vertex AI, Google BigQuery ML, Snowflake Cortex AI
Perplexity SonarAzure Machine Learning alternatives: Amazon SageMaker, Databricks Mosaic AI, Dataiku
Grok 4.20Dataiku alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI, Google Vertex AI
Mistral SmallAzure Machine Learning, Snowflake Cortex AI alternatives: DataRobot, Databricks Mosaic AI, Google Vertex AI
DeepSeek V4 FlashAzure Machine Learning alternatives: Amazon SageMaker, DataRobot, Databricks Mosaic AI, Snowflake Cortex AI
Llama 4 MaverickAzure Machine Learning alternatives: DataRobot, Google Vertex AI, IBM watsonx.ai, Snowflake Cortex AI
Qwen 3.7 FlashDataRobot alternatives: Amazon SageMaker, Azure Machine Learning, Databricks Mosaic AI
Kimi K2Azure Machine Learning, Dataiku alternatives: Amazon SageMaker
GLM 4.7 FlashXAmazon SageMaker, Azure Machine Learning alternatives: Databricks Mosaic AI, Google Vertex AI
MiniMax M2.5Databricks Mosaic AI, Snowflake Cortex AI alternatives: Amazon SageMaker, Azure Machine Learning, Dataiku, Domino Data Lab, Google Vertex AI
GPT-6 LunaDatabricks Mosaic AI alternatives: Azure Machine Learning, Gemini Enterprise Agent Platform, Snowflake Cortex AI
Muse Glimmer 30BAzure Machine Learning, Dataiku alternatives: Amazon SageMaker, Databricks Mosaic AI, Google Vertex AI, Snowflake Cortex AI
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
TensorFlow leads by two points.
TensorFlow2%#– of 15PyTorch0%#– of 15Mid-marketThe figures above
Level: the same share of first choices.
PyTorch3%#7 of 15TensorFlow3%#8 of 15Enterprise
Level: the same share of first choices.
PyTorch0%#– of 11TensorFlow0%#– of 11What the models said about PyTorch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Open-source frameworks like TensorFlow, PyTorch, and scikit-learn cost nothing to license... but it breaks down the moment you need organization-wide adoption or regulatory auditability.” Muse Glimmer 30B · scale prompt · soft negative
“Start with open-source frameworks (TensorFlow/PyTorch) if your team has the technical expertise” Claude Haiku 4.5 · budget prompt · first choice
“If you have zero budget, go with PyTorch or TensorFlow on your own hardware.” Kimi K2 · budget prompt · first choice
“tools like PyTorch + MLflow + Docker + a basic cloud VM can be the cheapest scalable option” GPT-5.4 mini · budget prompt · alternative
What the models said about TensorFlow
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Open-source frameworks like TensorFlow, PyTorch, and scikit-learn cost nothing to license... but it breaks down the moment you need organization-wide adoption or regulatory auditability.” Muse Glimmer 30B · scale prompt · soft negative
“Start with open-source frameworks (TensorFlow/PyTorch) if your team has the technical expertise” Claude Haiku 4.5 · budget prompt · first choice
“If you have zero budget, go with PyTorch or TensorFlow on your own hardware.” Kimi K2 · budget prompt · first choice
“use TensorFlow / PyTorch via Anaconda environments for code-based work” Muse Glimmer 30B · budget prompt · alternative
Also compared
Databricks Mosaic AI vs PyTorchDatabricks Mosaic AI vs TensorFlowAzure Machine Learning vs PyTorchAzure Machine Learning vs TensorFlowDataiku vs PyTorchDataiku vs TensorFlowAmazon SageMaker vs PyTorchAmazon SageMaker vs TensorFlowMLflow vs PyTorchMLflow vs TensorFlowSnowflake Cortex AI vs PyTorchSnowflake Cortex AI vs TensorFlowAll twenty-eight head-to-head pages and the full ML platforms standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.