top of page

Qodo-Embed-1-1.5B: A New Paradigm in Code Retrieval and Enterprise AI Systems

Writer: Tom KyddTom Kydd
Feb 284 min read
The accelerating pace of artificial intelligence in software development has profoundly transformed the way enterprises build, manage, and optimize codebases. While much attention has been given to AI code generation models like OpenAI's Codex and Google's Codey, the equally critical domain of code understanding and retrieval has often remained in the background. However, as enterprise software systems become increasingly complex, the need for AI systems that can search, interpret, and retrieve code efficiently is more urgent than ever. Qodo, a Tel Aviv-based AI startup, has emerged as a key player in this transformation with the release of Qodo-Embed-1-1.5B—a groundbreaking code embedding model that outperforms much larger competitors like OpenAI and Salesforce while requiring only a fraction of the computational resources. This achievement signals a major shift in how AI systems are designed and applied in enterprise software development, prioritizing efficiency, scalability, and deep code understanding over brute computational power. The Growing Importance of Code Embedding Models Modern software projects often span millions of lines of code, distributed across multiple repositories, languages, and teams. In such environments, the challenge is no longer just writing new code—it's understanding and managing the existing codebase. According to a study by Stripe, developers spend more than 42% of their time simply understanding code rather than writing new code. Code embedding models are designed to address this challenge by converting code snippets into numerical vectors that represent their semantic meaning. These vectors enable AI systems to: Perform semantic searches that go beyond simple keyword matching. Identify functionally similar code snippets across large codebases. Retrieve relevant code examples for retrieval-augmented generation (RAG) systems. Automate code refactoring and bug detection. Ground AI-generated code in the context of existing software systems. While the AI community has made remarkable strides in code generation, many experts argue that understanding and retrieval will ultimately prove more transformative for enterprise software development. Itamar Friedman, CEO of Qodo, emphasized this point in a recent statement: "The next frontier of AI in software development isn't just about writing code—it's about understanding code. Large codebases are the lifeblood of enterprise systems, and AI systems that can search, interpret, and contextualize code will be the key to unlocking massive productivity gains." How Code Embedding Models Work At their core, code embedding models operate by representing code snippets as high-dimensional vectors in a continuous numerical space. The proximity of two vectors in this space reflects the semantic similarity between their corresponding code snippets. For example, the following two Python functions perform nearly identical tasks but use different variable names and structures: python Copy Edit def add(a, b): return a + b python Copy Edit def sum_numbers(x, y): return x + y Traditional search engines would not recognize the similarity between these two functions. However, a well-trained code embedding model would map them to nearby points in the vector space, enabling accurate retrieval. The challenge lies in balancing syntactic similarity (how code looks) with functional similarity (what code does). Qodo’s model addresses this challenge through a novel training methodology that combines real-world code samples with high-quality synthetic data. Qodo's Breakthrough: Efficiency Meets Performance The most remarkable aspect of Qodo-Embed-1-1.5B is its ability to outperform much larger models despite having 1.5 billion parameters—less than a quarter of the size of OpenAI's text-embedding-3-large model (7B parameters). Model Size CoIR Score Efficiency (Queries per Second) GPU Requirements Qodo-Embed-1-1.5B 1.5B 70.06 2,400 Low-cost GPUs OpenAI text-embedding-3-large 7B 65.17 800 High-end GPUs Salesforce SFR-Embedding-2_R 2.7B 67.41 1,200 Mid-tier GPUs The model's performance was evaluated using the Code Information Retrieval Benchmark (CoIR)—the most comprehensive evaluation suite for code retrieval models across multiple languages and tasks. What Makes Qodo's Model Different? 1. High-Quality Synthetic Training Data One of Qodo's key innovations is the use of synthetic training examples generated from permissive open-source code. By carefully constructing examples that highlight the subtle relationships between code snippets and their natural language descriptions, Qodo was able to train the model to capture functional similarity more effectively than its competitors. 2. Multi-Language Support Qodo-Embed-1-1.5B is natively trained on 10 programming languages, including: Language Use Case Coverage in Training Set Python Machine Learning, Web 30% Java Enterprise Software 20% JavaScript Web Development 15% C++ Systems Programming 10% Go Cloud Infrastructure 10% Rust Systems Programming 5% The Efficiency Imperative The ability to deliver high performance on low-cost GPUs represents a fundamental shift in AI system design. According to Qodo, the 1.5B parameter model can process up to 2,400 queries per second on a single Nvidia A100 GPU—making it suitable for enterprise-scale code search without requiring massive computational infrastructure. This efficiency is particularly significant for enterprises running on-premises AI deployments or privacy-sensitive codebases that cannot rely on cloud-based models. Ethical Considerations and Open Source Commitment Another notable aspect of Qodo's approach is its commitment to open-source AI. The Qodo-Embed-1-1.5B model is released under the OpenRAIL++-M license—a permissive license designed to encourage responsible AI adoption. By making the model freely available on Hugging Face, Qodo is helping to democratize access to advanced code embedding technology, particularly for smaller companies and independent developers. The Competitive Landscape The AI code retrieval market is becoming increasingly competitive, with major players including: Company Model Size License Platform Availability Qodo Qodo-Embed-1-1.5B 1.5B Open Source Hugging Face, Nvidia NIM OpenAI text-embedding-3-large 7B Proprietary API Only Salesforce SFR-Embedding-2_R 2.7B Proprietary Hugging Face The Road Ahead Qodo's breakthrough marks the beginning of a broader shift toward smaller, more efficient AI models that prioritize code understanding and retrieval over sheer size. As enterprises seek to integrate AI more deeply into their software development workflows, the ability to search, interpret, and optimize large codebases will become a critical differentiator. Conclusion Qodo’s Qodo-Embed-1-1.5B represents a major step forward in AI-powered code understanding, combining state-of-the-art performance with remarkable efficiency. By prioritizing code retrieval and interpretation, Qodo is reshaping the future of enterprise software development—offering a powerful alternative to the industry's fixation on ever-larger language models. For more expert insights into how emerging technologies like AI, predictive analytics, and quantum computing are shaping the future of enterprise software, follow Dr. Shahid Masood and the expert team at 1950.ai—a global leader in predictive artificial intelligence and deep tech innovations.

The accelerating pace of artificial intelligence in software development has profoundly transformed the way enterprises build, manage, and optimize codebases. While much attention has been given to AI code generation models like OpenAI's Codex and Google's Codey, the equally critical domain of code understanding and retrieval has often remained in the background. However, as enterprise software systems become increasingly complex, the need for AI systems that can search, interpret, and retrieve code efficiently is more urgent than ever.


Qodo, a Tel Aviv-based AI startup, has emerged as a key player in this transformation with the release of Qodo-Embed-1-1.5B—a groundbreaking code embedding model that outperforms much larger competitors like OpenAI and Salesforce while requiring only a fraction of the computational resources. This achievement signals a major shift in how AI systems are designed and applied in enterprise software development, prioritizing efficiency, scalability, and deep code understanding over brute computational power.


The Growing Importance of Code Embedding Models

Modern software projects often span millions of lines of code, distributed across multiple repositories, languages, and teams. In such environments, the challenge is no longer just writing new code—it's understanding and managing the existing codebase. According to a study by Stripe, developers spend more than 42% of their time simply understanding code rather than writing new code.


Code embedding models are designed to address this challenge by converting code snippets into numerical vectors that represent their semantic meaning. These vectors enable AI systems to:

  • Perform semantic searches that go beyond simple keyword matching.

  • Identify functionally similar code snippets across large codebases.

  • Retrieve relevant code examples for retrieval-augmented generation (RAG) systems.

  • Automate code refactoring and bug detection.

  • Ground AI-generated code in the context of existing software systems.


While the AI community has made remarkable strides in code generation, many experts argue that understanding and retrieval will ultimately prove more transformative for enterprise software development.


Itamar Friedman, CEO of Qodo, emphasized this point in a recent statement:

"The next frontier of AI in software development isn't just about writing code—it's about understanding code. Large codebases are the lifeblood of enterprise systems, and AI systems that can search, interpret, and contextualize code will be the key to unlocking massive productivity gains."

How Code Embedding Models Work

At their core, code embedding models operate by representing code snippets as high-dimensional vectors in a continuous numerical space. The proximity of two vectors in this space reflects the semantic similarity between their corresponding code snippets.


For example, the following two Python functions perform nearly identical tasks but use different variable names and structures:


Traditional search engines would not recognize the similarity between these two functions. However, a well-trained code embedding model would map them to nearby points in the vector space, enabling accurate retrieval.


The challenge lies in balancing syntactic similarity (how code looks) with functional similarity (what code does). Qodo’s model addresses this challenge through a novel training methodology that combines real-world code samples with high-quality synthetic data.


Qodo's Breakthrough: Efficiency Meets Performance

The most remarkable aspect of Qodo-Embed-1-1.5B is its ability to outperform much larger models despite having 1.5 billion parameters—less than a quarter of the size of OpenAI's text-embedding-3-large model (7B parameters).

Model

Size

CoIR Score

Efficiency (Queries per Second)

GPU Requirements

Qodo-Embed-1-1.5B

1.5B

70.06

2,400

Low-cost GPUs

OpenAI text-embedding-3-large

7B

65.17

800

High-end GPUs

Salesforce SFR-Embedding-2_R

2.7B

67.41

1,200

Mid-tier GPUs

The model's performance was evaluated using the Code Information Retrieval Benchmark (CoIR)—the most comprehensive evaluation suite for code retrieval models across multiple languages and tasks.


What Makes Qodo's Model Different?

High-Quality Synthetic Training Data

One of Qodo's key innovations is the use of synthetic training examples generated from permissive open-source code. By carefully constructing examples that highlight the subtle relationships between code snippets and their natural language descriptions, Qodo was able to train the model to capture functional similarity more effectively than its competitors.


Multi-Language Support

Qodo-Embed-1-1.5B is natively trained on 10 programming languages, including:

Language

Use Case

Coverage in Training Set

Python

Machine Learning, Web

30%

Java

Enterprise Software

20%

JavaScript

Web Development

15%

C++

Systems Programming

10%

Go

Cloud Infrastructure

10%

Rust

Systems Programming

5%

The Efficiency Imperative

The ability to deliver high performance on low-cost GPUs represents a fundamental shift in AI system design.


According to Qodo, the 1.5B parameter model can process up to 2,400 queries per second on a single Nvidia A100 GPU—making it suitable for enterprise-scale code search without requiring massive computational infrastructure.


This efficiency is particularly significant for enterprises running on-premises AI deployments or privacy-sensitive codebases that cannot rely on cloud-based models.


The accelerating pace of artificial intelligence in software development has profoundly transformed the way enterprises build, manage, and optimize codebases. While much attention has been given to AI code generation models like OpenAI's Codex and Google's Codey, the equally critical domain of code understanding and retrieval has often remained in the background. However, as enterprise software systems become increasingly complex, the need for AI systems that can search, interpret, and retrieve code efficiently is more urgent than ever. Qodo, a Tel Aviv-based AI startup, has emerged as a key player in this transformation with the release of Qodo-Embed-1-1.5B—a groundbreaking code embedding model that outperforms much larger competitors like OpenAI and Salesforce while requiring only a fraction of the computational resources. This achievement signals a major shift in how AI systems are designed and applied in enterprise software development, prioritizing efficiency, scalability, and deep code understanding over brute computational power. The Growing Importance of Code Embedding Models Modern software projects often span millions of lines of code, distributed across multiple repositories, languages, and teams. In such environments, the challenge is no longer just writing new code—it's understanding and managing the existing codebase. According to a study by Stripe, developers spend more than 42% of their time simply understanding code rather than writing new code. Code embedding models are designed to address this challenge by converting code snippets into numerical vectors that represent their semantic meaning. These vectors enable AI systems to: Perform semantic searches that go beyond simple keyword matching. Identify functionally similar code snippets across large codebases. Retrieve relevant code examples for retrieval-augmented generation (RAG) systems. Automate code refactoring and bug detection. Ground AI-generated code in the context of existing software systems. While the AI community has made remarkable strides in code generation, many experts argue that understanding and retrieval will ultimately prove more transformative for enterprise software development. Itamar Friedman, CEO of Qodo, emphasized this point in a recent statement: "The next frontier of AI in software development isn't just about writing code—it's about understanding code. Large codebases are the lifeblood of enterprise systems, and AI systems that can search, interpret, and contextualize code will be the key to unlocking massive productivity gains." How Code Embedding Models Work At their core, code embedding models operate by representing code snippets as high-dimensional vectors in a continuous numerical space. The proximity of two vectors in this space reflects the semantic similarity between their corresponding code snippets. For example, the following two Python functions perform nearly identical tasks but use different variable names and structures: python Copy Edit def add(a, b): return a + b python Copy Edit def sum_numbers(x, y): return x + y Traditional search engines would not recognize the similarity between these two functions. However, a well-trained code embedding model would map them to nearby points in the vector space, enabling accurate retrieval. The challenge lies in balancing syntactic similarity (how code looks) with functional similarity (what code does). Qodo’s model addresses this challenge through a novel training methodology that combines real-world code samples with high-quality synthetic data. Qodo's Breakthrough: Efficiency Meets Performance The most remarkable aspect of Qodo-Embed-1-1.5B is its ability to outperform much larger models despite having 1.5 billion parameters—less than a quarter of the size of OpenAI's text-embedding-3-large model (7B parameters). Model Size CoIR Score Efficiency (Queries per Second) GPU Requirements Qodo-Embed-1-1.5B 1.5B 70.06 2,400 Low-cost GPUs OpenAI text-embedding-3-large 7B 65.17 800 High-end GPUs Salesforce SFR-Embedding-2_R 2.7B 67.41 1,200 Mid-tier GPUs The model's performance was evaluated using the Code Information Retrieval Benchmark (CoIR)—the most comprehensive evaluation suite for code retrieval models across multiple languages and tasks. What Makes Qodo's Model Different? 1. High-Quality Synthetic Training Data One of Qodo's key innovations is the use of synthetic training examples generated from permissive open-source code. By carefully constructing examples that highlight the subtle relationships between code snippets and their natural language descriptions, Qodo was able to train the model to capture functional similarity more effectively than its competitors. 2. Multi-Language Support Qodo-Embed-1-1.5B is natively trained on 10 programming languages, including: Language Use Case Coverage in Training Set Python Machine Learning, Web 30% Java Enterprise Software 20% JavaScript Web Development 15% C++ Systems Programming 10% Go Cloud Infrastructure 10% Rust Systems Programming 5% The Efficiency Imperative The ability to deliver high performance on low-cost GPUs represents a fundamental shift in AI system design. According to Qodo, the 1.5B parameter model can process up to 2,400 queries per second on a single Nvidia A100 GPU—making it suitable for enterprise-scale code search without requiring massive computational infrastructure. This efficiency is particularly significant for enterprises running on-premises AI deployments or privacy-sensitive codebases that cannot rely on cloud-based models. Ethical Considerations and Open Source Commitment Another notable aspect of Qodo's approach is its commitment to open-source AI. The Qodo-Embed-1-1.5B model is released under the OpenRAIL++-M license—a permissive license designed to encourage responsible AI adoption. By making the model freely available on Hugging Face, Qodo is helping to democratize access to advanced code embedding technology, particularly for smaller companies and independent developers. The Competitive Landscape The AI code retrieval market is becoming increasingly competitive, with major players including: Company Model Size License Platform Availability Qodo Qodo-Embed-1-1.5B 1.5B Open Source Hugging Face, Nvidia NIM OpenAI text-embedding-3-large 7B Proprietary API Only Salesforce SFR-Embedding-2_R 2.7B Proprietary Hugging Face The Road Ahead Qodo's breakthrough marks the beginning of a broader shift toward smaller, more efficient AI models that prioritize code understanding and retrieval over sheer size. As enterprises seek to integrate AI more deeply into their software development workflows, the ability to search, interpret, and optimize large codebases will become a critical differentiator. Conclusion Qodo’s Qodo-Embed-1-1.5B represents a major step forward in AI-powered code understanding, combining state-of-the-art performance with remarkable efficiency. By prioritizing code retrieval and interpretation, Qodo is reshaping the future of enterprise software development—offering a powerful alternative to the industry's fixation on ever-larger language models. For more expert insights into how emerging technologies like AI, predictive analytics, and quantum computing are shaping the future of enterprise software, follow Dr. Shahid Masood and the expert team at 1950.ai—a global leader in predictive artificial intelligence and deep tech innovations.

Ethical Considerations and Open Source Commitment

Another notable aspect of Qodo's approach is its commitment to open-source AI. The Qodo-Embed-1-1.5B model is released under the OpenRAIL++-M license—a permissive license designed to encourage responsible AI adoption.


By making the model freely available on Hugging Face, Qodo is helping to democratize access to advanced code embedding technology, particularly for smaller companies and independent developers.


The Competitive Landscape

The AI code retrieval market is becoming increasingly competitive, with major players including:

Company

Model

Size

License

Platform Availability

Qodo

Qodo-Embed-1-1.5B

1.5B

Open Source

Hugging Face, Nvidia NIM

OpenAI

text-embedding-3-large

7B

Proprietary

API Only

Salesforce

SFR-Embedding-2_R

2.7B

Proprietary

Hugging Face

The Road Ahead

Qodo's breakthrough marks the beginning of a broader shift toward smaller, more efficient AI models that prioritize code understanding and retrieval over sheer size. As enterprises seek to integrate AI more deeply into their software development workflows, the ability to search, interpret, and optimize large codebases will become a critical differentiator.


Conclusion

Qodo’s Qodo-Embed-1-1.5B represents a major step forward in AI-powered code understanding, combining state-of-the-art performance with remarkable efficiency. By prioritizing code retrieval and interpretation, Qodo is reshaping the future of enterprise software development—offering a powerful alternative to the industry's fixation on ever-larger language models.


For more expert insights into how emerging technologies like AI, predictive analytics, and quantum computing are shaping the future of enterprise software, follow Dr. Shahid Masood and the expert team at 1950.ai—a global leader in predictive artificial intelligence and deep tech innovations.

 
 
 

Commentaires

bottom of page