Code Llama 70B Instruct achieved a 67.8% score on HumanEval benchmark as of January 2024, marking Meta’s most capable open-source code generation model. The broader Llama family reached 1.2 billion downloads by April 2025, with approximately one million downloads occurring daily. Code Llama supports seven major programming languages and provides context windows reaching 100,000 tokens for processing extensive codebases.
Code Llama Key Statistics
- Code Llama 70B Instruct scores 67.8% on HumanEval benchmark, surpassing GPT-4 and Gemini Pro in zero-shot scenarios as of January 2024
- Llama models reached 1.2 billion downloads by April 2025, growing from 350 million in August 2024
- Code Llama supports seven programming languages including Python, C++, Java, PHP, TypeScript/JavaScript, C#, and Bash
- AI code tools market reached $7.37 billion in 2025 and projects to $23.97 billion by 2030 at 26.60% CAGR
- 81% of developers now use AI-powered coding assistants, with 41% of all code globally involving AI assistance
Code Llama Model Architecture and Scale
Meta released Code Llama as a specialized code-generation variant built on the Llama 2 foundation. The model family includes four parameter configurations ranging from 7 billion to 70 billion parameters.
Code Llama 7B, 13B, and 34B variants launched in August 2023 with training on 500 billion tokens. The 70B variant arrived in January 2024 after training on one trillion tokens, double the data of smaller models.
| Model Variant | Parameters | Training Tokens | Release Date |
|---|---|---|---|
| Code Llama 7B | 7 Billion | 500 Billion | August 2023 |
| Code Llama 13B | 13 Billion | 500 Billion | August 2023 |
| Code Llama 34B | 34 Billion | 500 Billion | August 2023 |
| Code Llama 70B | 70 Billion | 1 Trillion | January 2024 |
All models trained on sequences of 16,000 tokens while demonstrating improvements on inputs extending up to 100,000 tokens. The 7B model operates on a single GPU, while 34B and 70B models deliver optimal results for complex coding assistance tasks.
Code Llama Benchmark Performance
HumanEval and MBPP benchmarks serve as industry-standard metrics for evaluating code generation proficiency. Code Llama 70B Instruct recorded the highest score among open-source models.
The 70B Instruct variant achieved 67.8% on HumanEval and 62.2% on MBPP. This marked significant improvement over the first Code Llama version, which reached up to 48.8% on HumanEval.
| Model | HumanEval Score | MBPP Score |
|---|---|---|
| GPT-4 | 85.4% | – |
| GPT-3.5 | 72.3% | – |
| Code Llama 70B Instruct | 67.8% | 62.2% |
| Code Llama 70B | 65.2% | – |
| Code Llama 34B | 53.7% | 56.2% |
Code Llama Programming Language Support
Code Llama demonstrates proficiency across seven major programming languages. Meta developed specialized variants targeting specific development environments.
The Code Llama Python variant received fine-tuning on an additional 100 billion tokens of Python-specific code. Training on the Python-heavy dataset yielded performance gains between 4.3 and 8.3 percentage points on HumanEval pass@1.
Python serves as the primary language with dedicated model support. C++, Java, PHP, TypeScript/JavaScript, C#, and Bash receive support through the base model configuration.
Llama Family Download Growth
The Llama model family, which includes Code Llama, demonstrated remarkable adoption growth throughout 2024 and 2025. Meta CEO Mark Zuckerberg confirmed the milestone achievements.
Downloads reached 350 million in August 2024, representing a 10x year-over-year increase. The family crossed one billion downloads by mid-March 2025, growing from 650 million in December 2024.
By April 2025, downloads reached 1.2 billion with approximately one million downloads occurring daily. Thousands of developers contributed tens of thousands of derivative models downloaded hundreds of thousands of times each month.
AI Code Tools Market Expansion
Code Llama operates within a rapidly expanding AI code tools market. Market research firms project substantial growth through 2030 and beyond.
The global AI code tools market reached $7.37 billion in 2025. Forecasts project expansion to $23.97 billion by 2030, representing a 26.60% compound annual growth rate.
| Market Metric | 2024 Value | 2030 Projection | CAGR |
|---|---|---|---|
| Global AI Code Tools Market | $7.37 Billion | $23.97 Billion | 26.60% |
| AI Code Generation Market | $4.91 Billion | $30.1 Billion (2032) | 27.1% |
| North American AI Code Market | $848.65 Million (2023) | $105.5 Billion | 72.17% |
Cloud-based deployment dominates the market with a 76.23% market share. Code completion functionality holds a 43.3% market share among available features.
Developer Adoption of AI Coding Assistants
Developer adoption of AI coding assistants, including open-source options like Code Llama, accelerated significantly through 2025. CodeSignal data reveals widespread integration into development workflows.
81% of surveyed developers now use AI-powered coding assistants. 41% of all code generated globally involves AI assistance as of 2025.
76% of professional developers use or plan to adopt AI tools. 82% of developers using AI assistants report daily or weekly usage patterns.
84.4% of programmers have tried at least one AI code tool. GitHub Copilot users complete 126% more projects per week compared to manual coders, demonstrating significant productivity improvements.
Time savings on coding, debugging, and documentation range from 30% to 75% depending on task complexity and developer experience level.
Code Llama Technical Specifications
Code Llama’s technical specifications enable developers to select appropriate configurations for their deployment requirements. The models provide extended context capabilities compared to base Llama 2.
Code Llama models provide stable code generations with context windows reaching 100,000 tokens, approximately 25 times larger than base Llama 2’s context window. Fill-in-the-Middle support exists for 7B, 13B, and 70B variants.
| Specification | Value |
|---|---|
| Maximum Context Window | 100,000 tokens |
| Training Sequence Length | 16,000 tokens |
| Fill-in-the-Middle Support | 7B, 13B, 70B variants |
| Training GPU Hours (9 Models) | 400,000 hours |
| Training Dataset | 1TB code-specific |
| CO2 Emissions | 65.3 tCO2eq (100% offset) |
| License Type | Community License |
Training all nine original Code Llama models required 400,000 GPU hours on A100-80GB hardware. The training dataset comprises one terabyte of code-specific data.
Meta offset 100% of CO2 emissions from training, totaling 65.3 tCO2eq. The Community License permits both research and commercial use cases.
Enterprise Open-Source LLM Adoption
Enterprise adoption of open-source models like Code Llama continues accelerating as organizations seek cost efficiency and customization capabilities. Gartner and McKinsey data reveals shifting adoption patterns.
78% of organizations used AI in at least one business function in 2024. 72% of enterprises plan to increase their GenAI spending in 2025.
| Enterprise Adoption Metric | Statistics |
|---|---|
| Organizations using AI (2024) | 78% |
| Enterprises increasing AI spending (2025) | 72% |
| Enterprises spending $250K+ on LLMs | 37% |
| Businesses adopting open-source LLMs (2025) | 60%+ |
| Time-to-market improvement | 23% faster |
Gartner forecasts that more than 60% of businesses will adopt open-source LLMs for at least one AI application by 2025, up from 25% in 2023. 37% of enterprises spend over $250,000 annually on LLMs.
McKinsey’s Global AI Survey indicates that businesses utilizing open-source AI models experience 23% faster time-to-market for AI projects. Major companies including Spotify, AT&T, DoorDash, Goldman Sachs, and Zoom utilize Llama models for production applications.
Code Llama Market Position
The coding AI agent and copilot market remains highly concentrated. The top three players hold approximately 70% market share.
GitHub leads with an estimated $800 million in ARR from AI-powered coding offerings. Open-source alternatives like Code Llama provide developers with cost-effective customization options for specialized use cases.
FAQ
What is the largest Code Llama model available?
Code Llama 70B is the largest variant with 70 billion parameters, trained on one trillion tokens of code and code-related data as of January 2024.
How does Code Llama 70B perform on coding benchmarks?
Code Llama 70B Instruct achieves 67.8% on HumanEval and 62.2% on MBPP, surpassing GPT-4 and Gemini Pro in zero-shot prompting scenarios.
How many programming languages does Code Llama support?
Code Llama supports seven major programming languages: Python, C++, Java, PHP, TypeScript/JavaScript, C#, and Bash with specialized Python variant available.
How many times have Llama models been downloaded?
As of April 2025, Llama models reached 1.2 billion downloads globally with approximately one million downloads occurring daily across all variants.
What is Code Llama’s maximum context window?
Code Llama models provide context windows reaching up to 100,000 tokens, enabling processing of extensive codebases and complex programming tasks.
