Meta
Muse brand — Muse Spark 1.3, Muse Glimmer, Muse Image, Muse Voice via Meta Model API
Meta AI is the creator of the Llama model family and the Muse proprietary product line. As of September 2026, Meta's managed API (Meta Model API) offers Muse Spark 1.3 (flagship multimodal), Muse Glimmer (efficient tier), Muse Image (image generation), and Muse Voice (speech synthesis). Llama 4 Scout, Maverick, and Behemoth remain open-weight and available through dozens of third-party providers. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.
The only frontier-class model family available as open weights — plus the Muse product line (Spark, Glimmer, Image, Voice) for teams that want managed API access without self-hosting.
- Open-weight Llama 4 models — self-host on any GPU cloud for lowest per-token cost at scale
- Muse Spark 1.3: flagship multimodal model with 1M context window
- Muse Image and Muse Voice for image generation and speech synthesis
- Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others
- Llama 3.2 1B/3B models run on-device (mobile, edge)
- No vendor lock-in on Llama — switch inference providers without changing model weights
- Self-hosting requires GPU infrastructure expertise
- Meta Model API has limited availability vs third-party hosts
- Muse Spark 1.3 pricing not yet publicly listed at launch
- Smaller proprietary model lineup vs OpenAI/Anthropic
Meta Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
llama-4-behemoth Llama 4 | Chat | frontier | — No current price available | — | 2.0s | 30 t/s | 1.0M | 90 | ||
llama-3.2-1b Llama 3.2 | Chat | budget | $0.02/ $0.02 in / out per 1M EST Jul 2026 | — | 80ms | 600 t/s | 128K | 38 | ||
llama-3.1-8b Llama 3.1 | Chat | budget | $0.02/ $0.05 in / out per 1M EST Jul 2026 | — | 100ms | 500 t/s | 131K | 52 | ||
llama-3.2-3b Llama 3.2 | Chat | budget | $0.03/ $0.05 in / out per 1M EST Jul 2026 | — | 120ms | 400 t/s | 128K | 48 | ||
llama-3-8b Llama 3 | Chat | budget | $0.04/ $0.04 in / out per 1M EST Jul 2026 | — | 110ms | 450 t/s | 8K | 46 | ||
llama-3-70b Llama 3 | Chat | efficient | $0.51/ $0.74 in / out per 1M EST Jul 2026 | — | 400ms | 90 t/s | 8K | 64 | ||
llama-3.2-90b-vision Llama 3.2 | Chat | advanced | $1.20/ $1.20 in / out per 1M EST Jul 2026 | — | 800ms | 60 t/s | 128K | 72 | ||
muse-spark-1.1 Muse Spark | Chat | advanced | $1.25/ $4.25 in / out per 1M EST Jul 2026 | — | 500ms | 130 t/s | 1.0M | 85 |
Meta — Frequently Asked Questions (FAQs)
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Meta is a proprietary AI lab that develops and hosts its own large language models. Meta AI is the creator of the Llama model family and the Muse proprietary product line. As of September 2026, Meta's managed API (Meta Model API) offers Muse Spark 1.3 (flagship multimodal), Muse Glimmer (efficient tier), Muse Image (image generation), and Muse Voice (speech synthesis). Llama 4 Scout, Maverick, and Behemoth remain open-weight and available through dozens of third-party providers. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale. Common use cases include Self-hosted inference, Cost-optimised at scale, Edge/on-device, Chat. Headquartered in Menlo Park, CA, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Meta vs other LLM providers
Meta competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The only frontier-class model family available as open weights — plus the Muse product line (Spark, Glimmer, Image, Voice) for teams that want managed API access without self-hosting. Use the LLM provider comparison tool to see Meta token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Meta?
Meta stands out because: The only frontier-class model family available as open weights — plus the Muse product line (Spark, Glimmer, Image, Voice) for teams that want managed API access without self-hosting.. The API currently offers 8 models, spanning 1 frontier, 2 advanced, 1 efficient, 4 budget tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Meta token pricing
Meta charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.02/1M tokens. Prices shown are sourced from Meta's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Meta context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Meta's largest context window is 1.0M tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Meta makes sense
Managed inference APIs like Meta eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Meta API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Meta token price data is available in the LLM price history charts.
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