Meta vs SambaNova Cloud: Token Pricing, Speed & Intelligence
Full comparison of Meta and SambaNova Cloud — 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.
SambaNova Cloud
High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Key metric comparison
—
—
—
—
—
—
—
—
—
—
—
—
Live token pricing
Strengths and weaknesses
Meta
SambaNova Cloud
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.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
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.
SambaNova Cloud FAQs
What makes SambaNova Cloud different?
SambaNova runs inference on its own Reconfigurable Dataflow Unit (RDU) chips rather than NVIDIA GPUs. This delivers very high throughput — over 800 tokens/sec on gpt-oss-120b — at competitive per-token rates.
What models does SambaNova Cloud offer?
As of October 2026: gpt-oss-120b ($0.22/$0.59), GLM-5.3 ($1.40/$4.40, cached $0.14), MiniMax-M3 ($0.60/$2.40, cached $0.06), DeepSeek-V3.2 ($3.00/$4.50), Meta-Llama-3.3-70B ($0.60/$1.20), and Gemma 4 31B ($0.38/$1.15). See cloud.sambanova.ai/plans/pricing for current rates.
Does SambaNova offer prompt caching?
Yes, on GLM-5.3 (cached input $0.14/1M vs $1.40/1M standard) and MiniMax-M3 (cached input $0.06/1M vs $0.60/1M standard).
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.
SambaNova Cloud — High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
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
SambaNova Cloud
- Proprietary RDU chips deliver very high throughput — 800+ tokens/sec on gpt-oss-120b
- Competitive pricing on gpt-oss-120b ($0.22/$0.59 per 1M)
- Prompt caching on GLM-5.3 (cached input at $0.14/1M)
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
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
SambaNova Cloud
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
- RDU architecture may behave differently from GPU inference for some workloads
Provider category context
Meta is a open source host, founded in 2023. SambaNova Cloud is a inference api, founded in 2017. 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 SambaNova Cloud if you need High-throughput inference. 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.