Deep Infra vs Meta: Token Pricing, Speed & Intelligence
Full comparison of Deep Infra and Meta — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Deep Infra
The cheapest inference API for open-weight models — Llama, Mistral, and more
Deep Infra is an inference-focused API provider specialising in open-weight models at extremely competitive prices. Consistently among the cheapest providers for Llama 3, Mistral, and DeepSeek models, making it the go-to choice for cost-sensitive production inference.
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.
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Live token pricing
Strengths & weaknesses
Deep Infra
Meta
Key differentiators
The most price-competitive inference API for open-weight models — often 30–50% cheaper than comparable providers for the same Llama or Mistral model.
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.
Frequently asked questions
Deep Infra FAQs
How cheap is Deep Infra compared to other providers?
Deep Infra is consistently among the cheapest providers for open-weight models. For example, Llama 3.1 8B is available at $0.02–0.05/1M tokens, and Llama 3.3 70B at around $0.10/1M tokens — often 30–50% below comparable providers.
What models does Deep Infra support?
Deep Infra hosts a wide range of open-weight models including the full Llama 3.x family, Mistral, Mixtral, DeepSeek V3 and R1, Qwen, and many others. The catalog is updated frequently as new models are released.
Is Deep Infra OpenAI-compatible?
Yes. Deep Infra provides an OpenAI-compatible API, so you can use the OpenAI SDK by pointing it at the Deep Infra endpoint. This makes migration straightforward.
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.
Provider resources
Deep Infra — The cheapest inference API for open-weight models — Llama, Mistral, and more
Deep Infra is an inference-focused API provider specialising in open-weight models at extremely competitive prices. Consistently among the cheapest providers for Llama 3, Mistral, and DeepSeek models, making it the go-to choice for cost-sensitive production inference.
The most price-competitive inference API for open-weight models — often 30–50% cheaper than comparable providers for the same Llama or Mistral model.
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.
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.
Key strengths compared
Deep Infra
- ▸Consistently lowest prices for open-weight models
- ▸Wide model catalog including Llama, Mistral, DeepSeek
- ▸OpenAI-compatible API
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
Provider category context
Deep Infra is a inference api, founded in 2023. Meta is a open source host, 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 Deep Infra if you need consistently lowest prices for open-weight models. Choose Meta if you need open-weight models — self-host on any gpu cloud for lowest per-token cost at scale. 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.