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Code Llama
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Code Generation & Assistants (176)

Code Llama Verified Tool

Code Llama is Meta’s family of coding language models for generating and discussing code, including foundational, Python-specialized and instruction-tuned variants. Developers should review the model license and deployment terms, protect prompts and source code, verify generated logic and dependencies, test for vulnerabilities and regressions, monitor model limitations and retain accountable human engineering approval.

Last Update: August 20, 2026

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Starting price Free

Tool Information

Code Llama is Meta’s family of coding language models for generating and discussing code, including foundational, Python-specialized and instruction-tuned variants. Developers should review the model license and deployment terms, protect prompts and source code, verify generated logic and dependencies, test for vulnerabilities and regressions, monitor model limitations and retain accountable human engineering approval.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, customer messages, code, trades and financial details, preserve originals and version history, and retain accountable human approval before publication, outreach, deployment or operational action.

The published Code Llama model family is available free for research and permitted commercial use under its applicable license. Compute, hosting, fine-tuning, support and third-party inference costs vary.

AI output can be inaccurate, biased, derivative, insecure, incomplete or misleading. Review consent, copyright, financial risk, training and retention terms, renewals, refunds, platform rules and applicable law. Medical, education, finance, investment, compliance, marketing, travel and customer-facing workflows require qualified human review.

F.A.Q (3)

Code Llama is Meta’s family of coding language models for generating and discussing code, including foundational, Python-specialized and instruction-tuned variants. Developers should review the model license and deployment terms, protect prompts and source code, verify generated logic and dependencies, test for vulnerabilities and regressions, monitor model limitations and retain accountable human engineering approval.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, customer messages, code, trades and financial details, preserve originals and version history, and retain accountable human approval before publication, outreach, deployment or operational action.

Verified pricing: Free. The published Code Llama model family is available free for research and permitted commercial use under its applicable license. Compute, hosting, fine-tuning, support and third-party inference costs vary.

Pros and Cons

Pros

  • Generates source code from natural-language and code prompts
  • Explains code using natural-language responses
  • Supports code completion and debugging workflows
  • Offers foundational; Python-specialized; and instruction-tuned variants
  • Provides 7B; 13B; 34B; and 70B parameter sizes
  • Lets deployers trade model size against latency and hardware cost
  • Supports Python; C++; Java; PHP; TypeScript; JavaScript; C#; and Bash
  • Adds fill-in-the-middle completion to supported 7B and 13B variants
  • Was trained for 16;000-token sequences with evaluated gains on longer inputs
  • Makes model weights available for self-managed deployment
  • Permits research and commercial use under the Llama 2 community license
  • Can operate on privately controlled infrastructure when properly hosted
  • Can be adapted for specialized coding domains
  • Includes a Python variant further trained on Python code
  • Meta provides a research paper describing its design and evaluations
  • Meta publishes responsible-use guidance for deploying the models

Cons

  • Code Llama is based on the older Llama 2 generation
  • Its 2023 and 2024 benchmark comparisons no longer represent the current model market
  • The Llama 2 community license is not a standard open-source license
  • Commercial users must comply with attribution and acceptable-use obligations
  • Base and Python variants are not designed for general instruction following
  • Generated code can be insecure; incorrect; inefficient; or fabricated
  • Long-context capacity does not guarantee repository-wide reasoning accuracy
  • The 34B and 70B variants require substantial compute and operational expertise
  • Self-hosters must implement authentication; monitoring; logging; and abuse controls
  • Outputs can resemble copyrighted code or introduce incompatible licensing terms
  • The model can produce malware or unsafe commands despite safeguards
  • Training-data provenance cannot be fully inspected by adopters
  • Performance differs across supported programming languages
  • Fine-tuning can degrade safety or broad coding ability
  • The model lacks current library knowledge unless paired with retrieval
  • Developers still need tests; linters; type checks; review; and security scans

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Applicable Tasks

code programming LLaMA Meta

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  • Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
  • App-store pricing can differ from website pricing by country, device and tax.
  • Share only authorised data, and read the provider's encryption, retention and model-training terms first.
  • AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
  • Commercial usage rights vary by plan, provider terms and jurisdiction.
  • None of these tools replaces a qualified professional for medical, legal or financial decisions.

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