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Cohere vs PyTorch comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Cohere
Ranking in AI Development Platforms
19th
Average Rating
7.4
Reviews Sentiment
6.6
Number of Reviews
3
Ranking in other categories
AI Writing Tools (9th), Large Language Models (LLMs) (6th), AI Proofreading Tools (8th)
PyTorch
Ranking in AI Development Platforms
6th
Average Rating
8.6
Reviews Sentiment
7.2
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of Cohere is 1.1%, up from 0.1% compared to the previous year. The mindshare of PyTorch is 3.5%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
PyTorch3.5%
Cohere1.1%
Other95.4%
AI Development Platforms
 

Featured Reviews

Gokul Anil - PeerSpot reviewer
Has streamlined test creation and analysis while needing better semantic accuracy for specific domain knowledge
Cohere is very useful because I have been in scenarios where code was written with multiple reusable concepts containing many functionalities covered as different functions, but without descriptions of what particular functions were doing. We used Cohere intelligence and its knowledge on Oracle ERP PPM, and it was able to read through all the TypeScript code and create descriptions intelligently, which were almost 90% correct when reviewed. It was very useful because we had 500-plus reusables, and it was able to analyze all of them and put them into a catalog. This makes it very easy to find and use the catalog to determine whether existing functionality is already implemented, preventing redundant implementations. When it creates a new test, it creates it almost 70 to 80% correctly without errors. The time savings are significant - what previously took one or two days can now be completed in two to three hours maximum. We can complete many more tests in a day or sprint with Cohere's help. Along with test automation, we handle analysis tasks, and now we have more time for better analysis. We are planning to implement test analysis capabilities as well. Once you receive the requirements and test cases, you can directly use them as input, and it will generate all artifacts and test data.
Rohan Sharma - PeerSpot reviewer
Enabled creation of innovative projects through developer-friendly features
The aspect I like most about PyTorch is that it is really developer-friendly. Developers can constantly create new things, and everyone around the world can use it for free because it's an open-source product. What I personally like is that PyTorch has enabled users to use Apple's M1 chip natively for GPU users. Unlike other libraries using CUDA, PyTorch utilizes Metal Performance Shaders (MPS) to enable GPU usage on M1 chips.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The very first thing that I really like about it is the support team. They're really available on Discord, and they answer all of your questions."
"When it creates a new test, it creates it almost 70 to 80% correctly without errors; the time savings are significant—what previously took one or two days can now be completed in two to three hours maximum."
"A direct benefit of using Cohere's reranking model is that we can tell clients we have this module rather than missing this piece, as reranking is a very important component that companies discuss to enhance RAG quality."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install."
"I like PyTorch's scalability."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"The framework of the solution is valuable."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"We use PyTorch libraries, which are working well. It's very easy."
 

Cons

"It's challenging for us to make a conclusion about quality, but the speed is good."
"When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved."
"Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that."
"I would like to see better learning documents."
"PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"The product has breakdowns when we change the versions a lot."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The training of the models could be faster."
"The product has certain shortcomings in the automation of machine learning."
 

Pricing and Cost Advice

Information not available
"PyTorch is an open-source solution."
"It is free."
"PyTorch is open-sourced."
"PyTorch is open source."
"The solution is affordable."
"It is free."
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Top Industries

By visitors reading reviews
Manufacturing Company
11%
Computer Software Company
10%
Financial Services Firm
8%
Comms Service Provider
7%
Manufacturing Company
22%
Comms Service Provider
10%
Educational Organization
9%
University
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise4
 

Questions from the Community

What is your experience regarding pricing and costs for Cohere?
Cohere has a free tier. You can use the API in development mode, so you can just use it for free. But if you go to production, you will have to pay. I would advise someone to really consider it fir...
What needs improvement with Cohere?
Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that.
What is your primary use case for Cohere?
I use it for a personal project, a Discord bot for my Discord server. I haven't used it that much, but so far it's amazing. I like the support team. They are very good.
What is your experience regarding pricing and costs for PyTorch?
I haven't gone for a paid plan yet. I've just been using the free trial or open-source version.
What needs improvement with PyTorch?
PyTorch needs improvement in working on ARM-based chips. Although they have unified memory for GPU and RAM, they are unable to utilize these GPUs for processing efficiently. They take so much time....
 

Comparisons

 

Overview

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