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Meta vs Voyage AI: Token Pricing, Speed & Intelligence

Full comparison of Meta and Voyage AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

Meta

Llama 4 & Muse Spark — the world's most widely deployed open-weight models

Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.

Self-hosted inferenceCost-optimised at scaleEdge/on-deviceChatVisionCoding
Proprietary modelsHosts open weights

Voyage AI

State-of-the-art embedding and reranking models

Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants consistently top the MTEB leaderboard for retrieval quality.

RAG pipelinesSemantic searchDocument retrievalReranking
Proprietary models

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

Live token pricing

Strengths & weaknesses

Meta

Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
Llama 4 Behemoth: 2T parameter frontier model with 1M context window
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 — switch inference providers without changing model weights
Self-hosting requires GPU infrastructure expertise
Meta's own API has limited availability vs third-party hosts
Llama 4 Behemoth pricing not yet publicly listed
Smaller proprietary model lineup vs OpenAI/Anthropic

Voyage AI

Top MTEB leaderboard performance
Multimodal embedding support
Very competitive pricing
Embeddings and reranking only — no chat models
Smaller ecosystem than OpenAI

Key differentiators

Meta

The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.

Voyage AI

Voyage 3.5 Lite offers top-tier retrieval quality at just $0.02/1M tokens — the most cost-effective high-quality embedding available.

Frequently asked questions

Meta FAQs

What is the Llama 4 context window?

Llama 4 Scout and Maverick support 1,000,000-token (1M) context windows. Llama 4 Behemoth also targets 1M context. This makes Llama 4 competitive with Gemini 1.5 Pro for long-document and multi-document tasks.

How much does the Meta Llama API cost?

Llama 3.2 1B is $0.02/1M tokens in/out. Llama 3.2 3B is $0.03/$0.05. Llama 3.1 8B is $0.02/$0.05. Llama 3.2 90B Vision is $1.20/$1.20. Muse Spark 1.1 is $1.25/$4.25. Llama 4 Behemoth pricing is not yet publicly listed.

Can I self-host Llama models?

Yes — all Llama 3.x and Llama 4 Scout/Maverick weights are publicly available under the Llama Community License. You can run them on any GPU cloud provider. A single H100 at ~$2.50/hr can serve Llama 3.1 8B at very high throughput, making self-hosting cost-effective above ~10M tokens/day.

Voyage AI FAQs

What is Voyage AI used for?

Voyage AI provides embedding and reranking models for RAG pipelines, semantic search, and document retrieval. It does not offer chat or text generation models.

How does Voyage AI compare to OpenAI embeddings?

Voyage 3.5 consistently outperforms OpenAI text-embedding-3-large on MTEB benchmarks while being significantly cheaper. It is the preferred choice for production RAG systems.

Provider resources

MetaLlama 4 & Muse Spark — the world's most widely deployed open-weight models

Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. 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 — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.

Voyage AIState-of-the-art embedding and reranking models

Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants consistently top the MTEB leaderboard for retrieval quality.

Voyage 3.5 Lite offers top-tier retrieval quality at just $0.02/1M tokens — the most cost-effective high-quality embedding available.

Key strengths compared

Meta

  • Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
  • Llama 4 Behemoth: 2T parameter frontier model with 1M context window
  • Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others

Voyage AI

  • Top MTEB leaderboard performance
  • Multimodal embedding support
  • Very competitive pricing

Provider category context

Meta is a open source host, founded in 2023. Voyage AI is a inference api, founded in 2023. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.

How to choose between them

Choose Meta if you need open-weight models — self-host on any gpu cloud for lowest per-token cost at scale. Choose Voyage AI if you need top mteb leaderboard performance. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.