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Meta vs OpenAI: Token Pricing, Speed & Intelligence

Full comparison of Meta and OpenAI — 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

OpenAI

GPT-4o, o3, and the world's most widely-used AI API

OpenAI is the creator of the GPT model family and the ChatGPT product. Their API provides access to frontier models including GPT-4o, the o-series reasoning models, and the GPT-4.1 long-context family. Pricing is competitive for frontier-tier capability, with prompt caching available on most models.

CodingChatVisionReasoningAgents
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

OpenAI

Largest ecosystem & third-party integrations
Best-in-class function calling & structured outputs
Prompt caching on all major models
o3/o4-mini reasoning models for complex tasks
1M+ token context on GPT-4.1
No open-weight models — full vendor lock-in
Output pricing is among the highest for frontier tier
Rate limits can be restrictive on lower tiers

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.

OpenAI

The most widely-integrated LLM API — virtually every AI framework and tool supports OpenAI natively.

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.

OpenAI FAQs

How much does the OpenAI API cost?

GPT-4o costs $2.50/1M input tokens and $10/1M output tokens. GPT-4o-mini is $0.15/$0.60 per 1M tokens. Prompt caching cuts input costs by 50% on eligible requests.

What is the difference between GPT-4o and o3?

GPT-4o is a fast, multimodal model optimised for chat, vision, and coding. o3 is a reasoning model that uses chain-of-thought to solve complex problems — it is slower and more expensive but significantly more capable on math, science, and hard coding tasks.

Does OpenAI support prompt caching?

Yes. Prompt caching is available on GPT-4o, GPT-4.1, o3, and o4-mini. Cached input tokens are billed at 50% of the standard input price, making long-context and repeated-system-prompt workloads significantly cheaper.

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.

OpenAIGPT-4o, o3, and the world's most widely-used AI API

OpenAI is the creator of the GPT model family and the ChatGPT product. Their API provides access to frontier models including GPT-4o, the o-series reasoning models, and the GPT-4.1 long-context family. Pricing is competitive for frontier-tier capability, with prompt caching available on most models.

The most widely-integrated LLM API — virtually every AI framework and tool supports OpenAI natively.

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

OpenAI

  • Largest ecosystem & third-party integrations
  • Best-in-class function calling & structured outputs
  • Prompt caching on all major models

Provider category context

Meta is a open source host, founded in 2023. OpenAI is a frontier lab, founded in 2015. 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 OpenAI if you need largest ecosystem & third-party integrations. 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.