Meta vs Modal: Token Pricing, Speed & Intelligence
Full comparison of Meta and Modal — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
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.
Modal
Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Key metric comparison
—
—
—
—
—
—
—
—
—
—
—
—
Live token pricing
Strengths and weaknesses
Meta
Modal
Key differentiators
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.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
Frequently asked questions
Meta FAQs
What is the Muse brand from Meta?
Muse is Meta's proprietary managed API product line, launched in 2026. It includes Muse Spark 1.3 (flagship multimodal, 1M context), Muse Glimmer (efficient tier), Muse Image (image generation), and Muse Voice (speech synthesis) — all available via the Meta Model API.
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 3.x for long-document and multi-document tasks.
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. Self-hosting becomes cost-effective at high token volumes — check the live GPU table for current H100 rates.
Modal FAQs
What models does Modal offer on Shared Endpoints?
Modal Shared Endpoints currently serve 10 models: GLM 5.3 Flash NVFP4, DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, GLM 5.3 Flash, Muse Spark Open, GLM 5.3, Inkling NVFP4, Qwen3.8-Max, Qwen3.8-Max VL, and Kimi K3. Use the model table above for per-model pricing and capability details. Source: Modal's published Shared Endpoints catalog, re-read 2026-10-31.
How much does Modal charge per 1M tokens?
Modal Shared Endpoints input prices range from $0.15/1M (GLM 5.3 Flash NVFP4) to $3.00/1M (Kimi K3). All models include cached-input pricing. See the model table for full per-model rates.
Does Modal offer prompt caching?
Yes. All 10 models in the Modal Shared Endpoints catalog have published cached-input pricing, ranging from $0.005/1M (DeepSeek V4.1 Flash 1017) to $0.30/1M (Kimi K3). Depending on the model, the cached-input rate is 77.5% to 98% below the standard input rate. Source: Modal's published Shared Endpoints rates, re-read 2026-10-31.
Provider resources
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.
Modal — Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
Key strengths compared
Meta
- 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
- Self-hosting requires GPU infrastructure expertise
- Meta Model API has limited availability vs third-party hosts
Modal
- Managed hardware and autoscaling — no infrastructure to operate
- Published per-token rates including cached-input pricing
- OpenAI-style per-token billing across all 10 models
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
Key weaknesses compared
Meta
- 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
Modal
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
- Prices are published rates re-read 2026-10-31, not continuously collected
Provider category context
Meta is a open source host, founded in 2023. Modal is a inference api. 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 llama 4 models — self-host on any gpu cloud for lowest per-token cost at scale. Choose Modal if you need managed hardware and autoscaling — no infrastructure to operate. 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.