Llama 4
Meta’s open-weight multimodal family for research and commercial self-hosting.
Meta’s Llama 4 family — open-weight multimodal models designed for research and commercial use under Meta’s community license.
Why Llama 4 matters
Llama 4 is the open-model baseline many organizations use when they need to fine-tune, self-host, or avoid proprietary APIs. It is how open weights stay competitive with closed frontier models in real stacks.
Vision · Tool calling · Coding
Last reviewed: 31 July 2026
When to choose Llama 4
Decision guidance for architects—not a feature list.
Best for
- Self-hosted / on-prem assistants
- Fine-tuning and research
- Open multimodal applications
- Cost-controlled inference at scale
Avoid if
- You want zero-ops managed API only
- License constraints for very large user bases are unacceptable
Strengths
DataAIHub qualitative scores from 1 to 5, for architects. Not official benchmark results or a public ranking.
- Open weights★★★★★
- Deployment flexibility★★★★★
- Community tooling★★★★★
- Peak vs closed frontier★★★☆☆
- Managed simplicity★★☆☆☆
Ecosystem
Built by
- Meta
Llama 4 is Meta’s open-weight multimodal foundation-model family.
Competes with
- Qwen3
Primary open-weight rival for multilingual and self-hosted stacks.
- DeepSeek R1
Competes in open reasoning and cost-efficient self-hosted inference.
Works with
- vLLM
Default high-throughput serving stack for Llama open weights.
- Hugging Face
Weights, demos, and community tooling concentrate on the Hub.
Recommended for
- Llama models
Canonical open-weight Llama generation for self-hosting guides.
How Llama 4 evolved
Key moments in chronological order.
- Model
Llama 4 family
Open-weight multimodal Scout / Maverick-class models for research and commercial use.
- Research
MoE variants
Mixture-of-Experts variants expand efficiency for open multimodal serving.
- Model
Llama 3 quality jump
Major open-weight quality leap that sets up the Llama 4 generation.
- Open source
Llama 2 commercial license
Open weights become broadly usable for commercial products under Meta’s terms.
- Research
LLaMA research weights
Research release that sparks the modern open-model wave.
Overview
Capabilities
- Vision: Yes
- Audio input: No
- Tool calling: Yes
- Thinking: No
- MCP: No
- Coding: Yes
- Structured output: Yes
Technical specifications
- Provider
- Meta
- License
- Llama Community License
- Context window
- 256K
- Parameters
- Family (Scout / Maverick-class)
- Architecture
- Mixture-of-Experts (select variants)
- Release
- 2025
- Modalities
- Text, Image
- Vision
- Yes
- Audio input
- No
- Tool calling
- Yes
- Thinking
- No
- MCP
- No
- Open weights
- Yes
- API
- Yes
- Pricing (input)
- Self-host or cloud hosts
- Pricing (output)
- Self-host or cloud hosts
Supported modalities
Text · Image
Context window
256K (256,000 tokens)
Pricing
Weights free under license; inference cost is infra.
Availability
Hugging Face, together.ai, Fireworks, Bedrock, and more.
Use cases
- Open multimodal applications
- Enterprise self-hosted assistants
- Fine-tuning and research
- On-prem RAG stacks
Strengths
- Open weights with broad community tooling
- Strong multimodal variants
- Flexible deployment (vLLM, Ollama, clouds)
Limitations
- License restrictions for very large user bases
- Peak capability may trail top proprietary models
Related guides
Related benchmarks
Related research
- Llama model cards & research
Original Paper
- Top Open-Source & Open-Weight AI Models 2026
Evaluation
Related GitHub
Related tools
Related rankings
Companies
Explore more models
- Muse GlimmerMetaMeta Superintelligence Labs’ Muse Glimmer — Apache-2.0 ~30B dense multimodal agent model for on-device and single-GPU local agents. Sibling to closed Muse Spark and Muse Wonder; distinct from Llama 4.
- Muse SparkMetaMeta Superintelligence Labs’ Muse Spark 1.3 — closed multimodal model for agentic tasks, long-horizon coding, computer use, and 1M-context workflows via Muse Code and the Meta Model API. As of 2026-10-12 it is the lower-priced API under Muse Wonder (muse-wonder-1.0), not Meta’s peak. Succeeds Spark 1.2; max reasoning is still withheld for safety testing. Muse Spark Open (2026-10-15) is a separate weight release, not this API id.
- Muse WonderMetaMeta Superintelligence Labs’ Muse Wonder (API id muse-wonder-1.0, 2026-10-12) — closed model for longer agentic coding and professional tasks on the Meta Model API and in Muse Code. It sits above Muse Spark 1.3, which remains the lower-priced API. Muse Wonder Full is a Muse Code subscriber variant; the public pricing page lists only muse-wonder-1.0. Preview. Closed weights.
- DeepSeek V4DeepSeekDeepSeek’s V4 generation — live Flash is V4.1-Flash-1017 (API id deepseek-flash, upgraded 2026-10-17): Causal Encoder–Decoder MoE, 552B backbone (8B active prefill / 16B decode), native image+text, and 1M context. API id and prices are unchanged from the 2026-09-10 Flash list. MIT weights on Hugging Face. Legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp aliases temporarily route here. deepseek-v4-pro still serves V4-Pro-0813 after 2026-09-14 at unchanged Pro rates (DeepSeek withdrew the planned Flash reroute).
- Kimi K3Moonshot AIMoonshot’s Kimi K3 — 2.8T MoE (104B active) open-weight multimodal agentic model with 1M context, native vision, and strong long-horizon coding. Weights on Hugging Face under the Kimi K3 License.
- MiMo V2.6XiaomiXiaomi’s MiMo-V2.6 family — MIT-licensed native-omnimodal MoE with 1M context. Flagship open checkpoint is MiMo-V2.6-Pro-RL (1.02T total / 42B activated); Flash-RL is 309B / 15B activated. Hosted API ids: mimo-v2.6-pro, mimo-v2.6-flash, mimo-v2.6-pro-ultraspeed. Xiaomi publishes an Artificial Analysis Intelligence Index score of 46 for Pro; that figure is vendor-published and is not a DataAIHub ranking.