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Llama

  • What it is:Llama is a family of open-source large language models (LLMs) developed by Meta AI, ranging from 1B to 2T parameters with multimodal capabilities in recent versions like Llama 4.
  • Best for:Budget-conscious enterprises and startups, Developers requiring model control and customization, Companies with large-scale inference needs
  • Pricing:Starting from $0.020/M input, $0.060/M output
  • Rating:95/100Excellent
  • Expert's conclusion:Llama is the best open source LLM for serious production uses where having control over the model, minimizing costs, and achieving high-quality performance matter most.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Llama and What Does It Do?

Meta Platforms, Inc., formerly Facebook, is a global leader in social media and artificial intelligence. Meta’s primary business focus is providing social media services such as Facebook, Messenger, WhatsApp, and Instagram, as well as developing advanced AI systems using their research division, Meta AI (previously known as FAIR). In addition to its massive user base across the globe, Meta invests hundreds of millions of dollars into researching and developing new forms of AI technology.

Active
📍Menlo Park, CA
📅Founded 2004
🏢Public
TARGET SEGMENTS
ConsumersAdvertisersDevelopersEnterprises

What Are Llama's Key Business Metrics?

📊
3.05B+
Daily Active People
🏢
67,000+
Employees
📊
$1.3T+
Market Valuation
📊
8+ locations
AI Research Labs
📊
14+ across 10 countries
Global Offices

How Credible and Trustworthy Is Llama?

95/100
Excellent

Meta is a very large publicly traded corporation with enormous scale, significant financial resources, and leading position in AI research. However, Meta continues to face an ever-growing amount of regulatory oversight and enforcement.

BREAKDOWN
Product Maturity100/100
Company Stability98/100
Security & Compliance85/100
User Reviews75/100
Transparency80/100
Support Quality90/100
TRUST SIGNALS
Publicly traded with strong financialsBillions of daily active usersLeader in AI research via FAIRGlobal infrastructure and compliance frameworks

What is the history of Llama and its key milestones?

2004

Company Founded

Initially founded as "Thefacebook, Inc." by Mark Zuckerberg at Harvard University.

2012

IPO

Went public for approximately $104 billion in valuation.

2012

Acquired Instagram

Meta purchased Instagram for $1 billion.

2013

FAIR Established

Founded Facebook AI Research (now Meta AI).

2014

Acquired WhatsApp

Meta acquired WhatsApp for $19 billion.

2021

Rebranded to Meta

Meta rebranded from Facebook to Meta Platforms, Inc. to focus on metaverse and AI.

2025

Acquired Limitless and Manus AI

Acquired AI-wearable startup Limitless and Manus AI for $2 billion.

How Much Does Llama Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Llama Guard 3 8B$0.020/M input, $0.060/M outputSafety model, 131,072 token contextMeta-llama API Pricing 2026
Llama 3.2 3B Instruct$0.020/M input, $0.020/M outputLightweight model, 131,072 token context, 34.7 MMLU scoreMeta-llama API Pricing 2026
Llama 3.1 8B Instruct$0.020/M input, $0.050/M output8B parameter model, 16,384 token context, 47.6 MMLU scoreMeta-llama API Pricing 2026
Llama 3.2 11B Vision Instruct$0.049/M input, $0.049/M outputMultimodal model with vision capabilities, 131,072 token contextMeta-llama API Pricing 2026
Llama 3.3 70B Instruct$0.10/M input, $0.32/M output70B parameter model, 131,072 token context, 71.3 MMLU scoreMeta-llama API Pricing 2026
Llama 4 Scout$0.080/M input, $0.300/M outputLatest Llama 4 series, 327,680 token context, 75.2 MMLU scoreMeta-llama API Pricing 2026
Llama 4 Maverick$0.150/M input, $0.600/M outputFlagship model, 1,048,576 token context, 80.9 MMLU scoreMeta-llama API Pricing 2026
Llama 3.1 70B Instruct$0.400/M input, $0.400/M outputPrevious generation 70B, 131,072 token context, 67.6 MMLU scoreMeta-llama API Pricing 2026
Llama 3.1 405B Instruct$3.50/M input, $3.50/M outputLargest model, 405B parameters, 10,000 token context, 73.2 MMLU scoreMeta-llama API Pricing 2026
Llama Guard 3 8B$0.020/M input, $0.060/M output
Safety model, 131,072 token context
Meta-llama API Pricing 2026
Llama 3.2 3B Instruct$0.020/M input, $0.020/M output
Lightweight model, 131,072 token context, 34.7 MMLU score
Meta-llama API Pricing 2026
Llama 3.1 8B Instruct$0.020/M input, $0.050/M output
8B parameter model, 16,384 token context, 47.6 MMLU score
Meta-llama API Pricing 2026
Llama 3.2 11B Vision Instruct$0.049/M input, $0.049/M output
Multimodal model with vision capabilities, 131,072 token context
Meta-llama API Pricing 2026
Llama 3.3 70B Instruct$0.10/M input, $0.32/M output
70B parameter model, 131,072 token context, 71.3 MMLU score
Meta-llama API Pricing 2026
Llama 4 Scout$0.080/M input, $0.300/M output
Latest Llama 4 series, 327,680 token context, 75.2 MMLU score
Meta-llama API Pricing 2026
Llama 4 Maverick$0.150/M input, $0.600/M output
Flagship model, 1,048,576 token context, 80.9 MMLU score
Meta-llama API Pricing 2026
Llama 3.1 70B Instruct$0.400/M input, $0.400/M output
Previous generation 70B, 131,072 token context, 67.6 MMLU score
Meta-llama API Pricing 2026
Llama 3.1 405B Instruct$3.50/M input, $3.50/M output
Largest model, 405B parameters, 10,000 token context, 73.2 MMLU score
Meta-llama API Pricing 2026
💡Pricing Example: Processing 1 million tokens (500K input, 500K output) using Llama 3.3 70B
Llama 3.3 70B Instruct$210
(500K × $0.10) + (500K × $0.32) = $50 + $160
Llama 4 Maverick$375
(500K × $0.150) + (500K × $0.600) = $75 + $300
Llama 3.1 8B Instruct$35
(500K × $0.020) + (500K × $0.050) = $10 + $25

How Does Llama Compare to Competitors?

FeatureLlamaGPT-4oClaude 3.5 Sonnet
Starting Price$0.020/M tokens$2.50-$15/M tokens$3/$15 input/output
Open Source AvailableYesNoNo
Largest Model Size405B parameters—200K+ context
Vision CapabilitiesYes (3.2 11B)YesYes
Max Context Length1,048,576 tokens200,000 tokens200,000 tokens
Free TierNoYes (limited)Yes (limited)
Self-Hosting OptionYesNoNo
API AccessYesYesYes
Enterprise SupportYesYesYes
Starting Price
Llama$0.020/M tokens
GPT-4o$2.50-$15/M tokens
Claude 3.5 Sonnet$3/$15 input/output
Open Source Available
LlamaYes
GPT-4oNo
Claude 3.5 SonnetNo
Largest Model Size
Llama405B parameters
GPT-4o—
Claude 3.5 Sonnet200K+ context
Vision Capabilities
LlamaYes (3.2 11B)
GPT-4oYes
Claude 3.5 SonnetYes
Max Context Length
Llama1,048,576 tokens
GPT-4o200,000 tokens
Claude 3.5 Sonnet200,000 tokens
Free Tier
LlamaNo
GPT-4oYes (limited)
Claude 3.5 SonnetYes (limited)
Self-Hosting Option
LlamaYes
GPT-4oNo
Claude 3.5 SonnetNo
API Access
LlamaYes
GPT-4oYes
Claude 3.5 SonnetYes
Enterprise Support
LlamaYes
GPT-4oYes
Claude 3.5 SonnetYes

How Does Llama Compare to Competitors?

vs OpenAI GPT-4o

Llama is generally less expensive than GPT-4o ($0.020 – $3.50/M tokens v/s $2.50 – $15/M tokens) and available under an open-source license to host locally.

Use Llama for either cost or the ability to deploy your application however you choose to implement it; use GPT-4o if you want the greatest capabilities and easiest-to-use solution. The first 13 are a summary of the advantages of using this model, while items 14 through 31 summarize disadvantages of using the model. Here is an example of how to paraphrase each disadvantage:

vs Anthropic Claude

Llama's lowest-cost models ($0.020/M) are 150 – 750 times more inexpensive than Claude's low-end price point ($3 – $15 per million).

Use Llama for price-sensitive applications; Claude for critical-safety and specialized reasoning work.

vs Open-source competitors (Mistral, DeepSeek)

Llama holds the lead in open-source LLMs with a larger number of models (15 different models as of January 2026) and the support of Meta. DeepSeek is priced competitively through alternative service providers ($0.03 – $0.40/M); Llama also has an advantage due to the breadth of its integration with providers of tools and services (Together.ai, Groq, etc.).

Llama for well-established commercial deployments; new competition for experimental cost-reducing efforts.

What are the strengths and limitations of Llama?

Pros

  • Very low-cost price point -- the lowest model prices at $0.020/M tokens, and is approximately 100-750x less expensive than competing closed-source solutions.
  • Open-source availability -- users can host their own models and have complete control over the hosting and data.
  • A wide range of model sizes -- from 1 billion to 405 billion parameters, accommodating every potential use case and hardware constraint.
  • Larger extended context windows -- as much as 1,048,576 tokens (Llama 4 Maverick) which support longer documents and analysis.
  • The ability to analyze multimodal inputs -- vision enabled models (Llama 3.2 11B Vision), supporting images, are included.
  • An existing ecosystem -- there are several API providers (Together.ai, Groq, DeepInfra), that provide redundancy and optimization.
  • Proven performance -- the model achieves excellent benchmark results (MMLU up to 80.9) and is competitive with other proprietary models when it comes to code generation.

Cons

  • API rate limits will vary by provider -- there is currently no standard limit on rate, so users should check provider documentation to find the current limits.
  • The largest 405 billion parameter model has a smaller extended context window -- only 10,000 tokens compared to many of its competitors that support up to 200,000 tokens.
  • Deploying the model on-premises (self-hosted) will require considerable resources -- including GPU costs ($1.49-$6.98/hour) for on-site deployment.
  • There are fewer documented edge cases -- than there are documented examples of how the model performs in the wild compared to established models such as GPT-4.
  • Model fragmentation -- 15 different versions of the model create "choice paralysis" and make compatibility considerations important.
  • Fine-tuning the model may be less documented -- than fine-tuning OpenAI's models, where there are many publicly available examples of successful fine-tuning.
  • Inference speed varies depending on the provider -- Groq has one of the fastest inference speeds (840 tokens/sec), but others have much slower speeds unless they are deployed on specialized hardware.

Who Is Llama Best For?

Best For

  • Budget-conscious enterprises and startups — The cost-per-token for this model is the lowest in the industry -- making high volume applications economical.
  • Developers requiring model control and customization — Because the model is open-source, users can fine-tune, deploy on private infrastructure, and modify for their specific use cases. START_TEXT
  • Companies with large-scale inference needs — The ability to select among multiple API providers enables enterprises to implement their own load balancing and optimization techniques to help keep the total cost of ownership as low as possible.
  • Organizations needing long document processing — In addition to supporting entire codebases, books, and long conversations in a single request, extended context windows (up to 1 million tokens) also allow enterprises to create contextually rich, conversational interfaces that can be used for both human-human communication and human-machine interaction.
  • Teams with data privacy requirements — For organizations operating within highly regulated environments (healthcare, finance, government), the self-hosting option eliminates the risk of exposing customer data to third-party entities through API-based services.
  • Multimodal application developers — Using vision-enabled models (e.g., Llama 3.2 11B Vision) will help minimize the number of different vision APIs needed to complete an application or solution.

Not Suitable For

  • Enterprises requiring 24/7 dedicated support — Compared to OpenAI and Anthropic, enterprise support for Llama is limited. However, there are paid support tiers available from Together.ai and DeepInfra.
  • Use cases requiring absolute maximum model capability — While the largest Llama (405B) model provides a larger parameter space than previous versions, it still has a much smaller maximum context window than GPT-4o and, therefore, a lower performance ceiling. If you want to provide the most cutting-edge capabilities, consider using either GPT-4o or Claude 3.5 Opus.
  • Teams without ML/infrastructure expertise — To self-host models, organizations require access to a developer/operations professional who understands the complexities involved in running a large-scale AI service. There are managed API providers such as Together.ai and Groq that provide these same capabilities without requiring internal expertise.
  • Applications requiring instant zero-cold-start inference — As self-hosted models typically incur some amount of latency during initial launch, API providers can have varying levels of latency depending on their respective infrastructures. Organizations may choose to implement warm start strategies or other methods for mitigating this effect, or they can deploy the model on an alternative platform.

Are There Usage Limits or Geographic Restrictions for Llama?

API Rate Limits
Varies by provider: Groq (840 tokens/sec), DeepInfra (200 concurrent), Together.ai (OpenAI-compatible limits)
Model Context Window
Ranges from 8,192 (Llama 3) to 1,048,576 tokens (Llama 4 Maverick)
Pricing Tier Cutoff
Prices shown for prompts ≤200K tokens; longer prompts may incur different tiered pricing
Self-Hosting GPU Requirements
H100 GPUs ($1.49-$3.90/hour) for optimal performance; A100 ($1.10-$3.40/hour) for smaller models
Cache Memory
Prompt caching supported on most models; cache read/write costs available through specific providers
Concurrent Requests
Provider-dependent: DeepInfra supports 200 concurrent, others vary
Data Retention
API providers typically retain logs for 30 days; varies by provider and compliance requirements
Geographic Availability
Llama models available globally through multiple providers; some regional restrictions possible depending on provider infrastructure
Compliance & Certifications
No specific SOC 2, HIPAA, or compliance certifications published by Meta; varies by API provider (Fireworks.ai offers SOC 2/HIPAA)

Is Llama Secure and Compliant?

Open-Source Model TransparencyLlama models publicly available on GitHub and Hugging Face, enabling community security audits and transparency
Model GuardrailsLlama Guard 3 8B and Llama Guard 4 12B safety models available for content filtering and policy enforcement
Self-Hosting Data ControlOn-premise deployment options allow organizations to retain full data control and meet HIPAA/GDPR requirements
API Provider ComplianceSecurity varies by provider: Fireworks.ai offers SOC 2 Type II and HIPAA BAA; Together.ai and others provide enterprise security options
No Training Data RetentionMeta Llama API documentation does not indicate training on API usage data for model improvement
License TermsMeta Llama Community License allows commercial and research use but prohibits certain harmful activities
Responsible DisclosureMeta maintains security vulnerability reporting process; specific bug bounty program details not publicly listed

What Customer Support Options Does Llama Offer?

Channels
Available through Llama website and GitHub discussionsAvailable on www.meta.ai, Facebook, Messenger, Instagram, WhatsApp
Hours
Community support 24/7, self-service documentation
Response Time
Community-dependent; no guaranteed SLAs
Satisfaction
N/A - open source model, no formal ratings available
Specialized
None available; enterprise users self-deploy with internal teams
Support Limitations
•No official paid support or ticketing system for Llama users
•Relies on self-hosted deployments with community assistance only
•Meta AI chat provides general assistance, not product-specific support

What APIs and Integrations Does Llama Support?

API Type
Direct model inference API; no hosted REST/GraphQL API from Meta. Users deploy via Hugging Face Transformers, vLLM, or Llama.cpp
Authentication
Self-hosted: application-managed. Hugging Face: API tokens for model access
Webhooks
Not applicable; users implement custom webhooks in their deployments
SDKs
Official: None from Meta. Community: Python (transformers, llama-cpp-python), JavaScript, C++, Go via llama.cpp; Ollama for local deployment
Documentation
Comprehensive model cards and inference guides at llama.meta.com/docs; extensive community tutorials on GitHub and Hugging Face
Sandbox
Hugging Face Spaces for testing; Ollama provides local sandbox environment; no official Meta-hosted sandbox
SLA
None provided by Meta; uptime depends on user deployment infrastructure
Rate Limits
None enforced by Meta; user-managed based on hardware and serving framework (vLLM, TGI)
Use Cases
Text generation, chatbots, RAG applications, customer service automation, code generation; self-hosted for privacy-sensitive deployments

What Are Common Questions About Llama?

Organizations can download Llama models directly from either the Hugging Face or Meta website. Once downloaded, organizations can leverage various frameworks (Ollama for local deployment, Hugging Face Transformers for Python applications, llama.cpp for optimized inference) for integrating the model into their existing systems. A good place to begin would be with the model card documentation which includes information about prompts and example usage for each system.

Yes, Llama models are completely open-source and licensed under a permissive license that allows for both commercial and research uses of the models. Any organization wishing to use Llama commercially is required to comply with the licensing requirements, including providing proper attribution and implementing safety guardrails where necessary for certain applications.

Since Llama is entirely open-source and downloadable for self-hosting, organizations gain direct control over how their data is processed and stored. This provides a level of data privacy and customization control that is unavailable to organizations using cloud-hosted APIs such as those provided by OpenAI. Additionally, while the pricing of GPT models from OpenAI may be competitive, the operational costs associated with high volume deployments are likely to be significantly lower for organizations using Llama.

Yes, since Llama runs entirely on an organization's infrastructure. Data never leaves an organization's network. Therefore, organizations retain full control over all aspects of data privacy, security, and compliance when self-hosting Llama.

Llama 3.1 8B can be run on a variety of consumer-grade GPUs (such as RTX 3060+ or higher) or top-of-the-line processors. While Llama 3.1 70B may require an A100/H100 GPU(s) or a multi-GPU setup for optimal performance, quantized versions of these models (i.e., 4-bit) will significantly lower memory usage requirements while still maintaining performance.

All Llama models are capable of fine-tuning with the use of fine-tuning tools such as PEFT from Hugging Face, Unsloth, or Axolotl. Fine-tuning guidelines along with suggested hyperparameters are provided in detail by Meta within the documentation of each model.

Performance-wise, Llama 3.1 405B has matched or exceeded that of GPT-4 on many benchmark tests. Additionally, Mistral offers strong performance with small model sizes and Claude provides a strong level of safety with its closed-source API-only configuration. However, Llama excels when used in self-hosted and cost-controlled environments.

To obtain support for Llama, you should use one of the several communities available to you. For example, there are the GitHub Discussions forums, Hugging Face forums, Reddit (r/LocalLLaMA), and the official Llama Discord community. In addition, the documentation for deploying Llama and other tips and recommendations for optimizing performance and achieving safe usage are located at llama.meta.com.

Is Llama Worth It?

With its GPT-4 level performance and complete control over deployment, Llama represents the gold standard for open source LLMs, providing enterprises with complete flexibility and zero cost for inference. Due to the fast-paced and aggressive release cadence employed by Meta in conjunction with the large number of parameters (up to 405B), Llama is positioned as the leading choice for enterprises looking to maximize their ability to customize their solutions, maintain data sovereignty, and avoid API vendor lock-in. Finally, the mature ecosystem of serving frameworks and the extensive support available through the Llama community remove almost all technical barriers to entry for adopting this solution.

Recommended For

  • Enterprise customers requiring data privacy and/or the need for self-hosting
  • Teams developing production-level AI-based applications with high-volume inference
  • Researchers who require the highest degree of model transparency and customization
  • Organizations constrained by budget, seeking to minimize the cost of API vendor lock-in

!
Use With Caution

  • Teams lacking GPU infrastructure and/or DevOps expertise
  • Applications requiring <200ms latency, without significant optimization efforts
  • Small teams that prefer managed cloud service options rather than self-hosting

Not Recommended For

  • Users who want a simple, quick way to establish a basic chatbot application, without requiring setup or technical knowledge
  • Budget-constrained startups, lacking technical infrastructure
  • Prototype quickly using hosted APIs such as GPT or Claude since they can be implemented fast
Expert's Conclusion

Llama is the best open source LLM for serious production uses where having control over the model, minimizing costs, and achieving high-quality performance matter most.

Best For
Enterprise customers requiring data privacy and/or the need for self-hostingTeams developing production-level AI-based applications with high-volume inferenceResearchers who require the highest degree of model transparency and customization

What do expert reviews and research say about Llama?

Key Findings

Llama is a family of Meta's flagship open source LLMs that include models up to 405B parameters that match the performance of closed-source market leaders. It is fully self-hostable and has an established mature ecosystem of tools available through Hugging Face and vLLM. There is no commercial hosting or support for Llama from Meta, and its success as a viable solution for large scale, open-source, community driven applications has been demonstrated by several successful enterprise customer service automation implementations such as Smartly. Documentation and safety tools have been made available for users.

Data Quality

Good - comprehensive technical documentation from Meta, active community resources, enterprise case studies available. Limited commercial support/pricing information as open source project.

Risk Factors

!
Larger models require significant amounts of GPU infrastructure
!
Rapid development and growth of models requires continuous training
!
Quality of support from the community varies
!
Complexity of deploying models for non-technical teams
Last updated: January 2026

What Additional Information Is Available for Llama?

Community Ecosystem

The Llama ecosystem has garnered approximately 100K+ GitHub stars across its various repositories. Additionally, there are active communities on Hugging Face (which has experienced millions of downloads), r/LocalLLaMA (with over 100K+ subscribers), and the official Llama Discord. The community also holds regular calls and releases new models.

Deployment Ecosystem

Llama has been supported by several prominent inference servers including vLLM (used for production), Ollama (used locally), llama.cpp (for edge devices), and Text Generation Inference (used in enterprises). Several additional tools exist to help deploy the model, such as LlamaIndex and LangChain which both provide RAG and agent frameworks.

Enterprise Adoption

The model has been used successfully in multiple applications by companies such as Smartly (who have achieved 80% reduction in time spent supporting customers), AT&T, DoorDash, and Goldman Sachs for creating AI agents that assist in providing customer service. These deployments utilize on-premises Kubernetes and GPU acceleration to ensure compliance with data privacy requirements.

Model Releases

With the release of version 3.1 of Llama (July 2024), Meta released a 405B parameter model that performs better than GPT-4o. Additionally, multilingual support was added for 8 languages. Each subsequent release of Llama will continue to add safety features to the model and will include red-team reports detailing how the model performed under adversarial testing conditions.

Research Backing

Llama has been developed by Meta AI using the contributions of 1,000+ researchers. Llama has been presented in numerous publications at the top machine learning conferences (e.g., NeurIPS, ICML). Llama has open weights, enabling the full reproducibility of the model and enabling independent safety evaluations.

What Are the Best Alternatives to Llama?

  • •
    Mistral AI Models: Mixtral (French), open-source LLMs (8x22B) that achieve high-quality performance from a smaller model using an Apache License; easier to deploy on CPUs than Llama; a top choice for teams that prioritize model performance over leading benchmarks. (mistral.ai)
  • •
    Grok (xAI): Reasoning-focused, open-weights models from x.ai with strong coding and math performance as well as live, streaming data integrations; API available with access to weights. The ecosystem is still developing compared to Llama. Best for real-time applications. (x.ai)
  • •
    OpenAI GPT Models: Closed-source models via API (GPT-4o, o1) that are among the best in the industry; no CPU overhead for deployment due to extensive global infrastructure and safety tooling; the most convenient option, however it is also the most locked in as to vendor and cost of inference. Best for non-technical teams looking for quick prototyping options. (openai.com)
  • •
    Claude (Anthropic): AI that aligns with the Constitution and follows instructions very well, with a wide range of context in its API offerings; industry-best safety record, but does not allow for self-hosting. Best for companies operating in heavily regulated markets where alignment is paramount. (anthropic.com)
  • •
    Gemma (Google): Open models that can run lightweight (2B-27B) on edge devices or mobile platforms; easy to deploy and includes robust safety training; while they have lower performance than Llama flagship models, they are ideal for constrained environments such as mobile and embedded systems. Best for developers working with mobile/embedded AI. (ai.google)

What Are the Model Specifications of Llama?

Parameters
8B, 70B, 405B
Architecture
Autoregressive decoder-only transformer with RMSNorm, SwiGLU, RoPE, and GQA
Context Length
128K tokens
Training Data Cutoff
December 2023
Model Variants
Llama 3.1 8B, 70B, 405B (pretrained and instruction-tuned); Llama 4 Scout (109B total, 17B active), Maverick (400B total, 17B active)
Multimodal
Llama 3.1: Text-only; Llama 4 variants: Text and image input, text and code output

How Does Llama's Benchmark Performance Compare?

BenchmarkLlama 3.1 405BLlama 3.1 70BNotes
MMLUCompetitive with GPT-4oCompetitive with similar-sized modelsMulti-task language understanding
HumanEvalState-of-the-artStrong performanceCode generation
GSM8KState-of-the-artStrong performanceMath reasoning
GPQACompetitiveCompetitiveExpert-level questions
TruthfulQAEvaluatedEvaluatedFactual accuracy
LMArenaLlama 4 bests GPT-4oConversational benchmark

What Supported Modalities Does Llama Offer?

Text Input

Natural Language Prompts in eight+ languages

Text Output

Generated Text, Code, Multilingual Responses

Image Input (Llama 4)

Vision Understanding in Scout and Maverick versions

Code Output

Code Generation and Tool Use Capabilities

What Is Llama's Api Details?

Api Type
Open weights; inference via Hugging Face, Azure AI, AWS, etc.
Authentication
Platform-specific (API keys for hosted services)
Rate Limits
Varies by hosting provider
Sdks
Hugging Face Transformers, vLLM, Ollama; official Meta inference tools
Streaming
Supported via compatible inference engines
Function Calling
Strong tool use capabilities in instruction-tuned models

How Does Llama's Pricing Models Compare?

Access TypeCostNotes
Open Source WeightsFreeDownloadable model weights under Llama license
Self-Hosted InferenceInfrastructure costs onlyRun on own hardware or cloud GPUs
Azure AI / PartnersPay-per-useHosted inference pricing varies by provider
Commercial UseFree with restrictionsAcceptable Use Policy required

What Unique Features Does Llama Offer?

Open Weights

Models that can be downloaded for free and match the performance of closed-source models

128K Context

Longer Context Window Options for Long Documents and Conversational Threads

Multilingual

Native Support for Eight+ Languages Including Hindi, Arabic, Thai

Tool Use

Agentic Function Calling and State of the Art Capabilities

Model Distillation

Enables creation of smaller high quality Distilled Models (405B)

Mixture of Experts (Llama 4)

High Performance Scaling with 17B Active Parameters

What Platforms Does Llama Support?

Hugging FaceAzure AIAWS BedrockGoogle CloudOllamavLLMSelf-HostedLlama.cpp

Open source models deployable anywhere with GPU/CPU support

What Safety Features Does Llama Offer?

RLHF Alignment

Uses Reinforcement Learning with Human Feedback to Optimize Helpfulness and Safety

SFT + DPO

Fine-Tunes Using Supervised Training Methods and Direct Preference Optimization

Red Teaming

Systematic safety evaluation across capabilities

Acceptable Use Policy

Clear guidelines for responsible deployment

Synthetic Data Filtering

High-quality data processing for alignment

Multilingual Safety

Safety training across 8+ languages

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