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Meta vs GMI Cloud
Meta sells its own Muse models from a new API. GMI Cloud sells 200+ third-party models on owned GPUs with data centers across the US and Asia-Pacific.
By The Subconscious Team · Updated
Meta vs GMI Cloud: key differences
GMI Cloud is an infrastructure provider. It owns NVIDIA hardware in Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia, and its Inference Engine offers 200+ models through an OpenAI compatible API, including 45+ LLMs, 50+ video models, 25+ image models and 15+ audio models from providers like Google Veo, Kling, MiniMax and ElevenLabs. Meta is a model lab selling its own closed Muse Spark 1.3 at $1.25 in and $4.25 out, with Muse Image at $0.01 per image and Muse Voice Transcribe at $0.18 per hour.
Both give one bill for text and media, at very different catalog sizes. Meta's is three house models, simple and cheap. GMI's spans many vendors, including video generation, which Meta does not offer. GMI's edge is regional: in-country facilities in Taiwan, Thailand and Malaysia for APAC data residency, and a path to reserved H100 or H200 capacity. It has less developer mindshare and a smaller LLM catalog. Meta's API has a short track record. APAC companies and video-heavy apps fit GMI. Agent builders who want one strong model fit Meta.
What Meta and GMI Cloud do
Meta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta launched the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. At Meta Connect in September, Meta made the API generally available globally. Muse Spark 1.3 has a 1M token context and costs $1.25 in and $4.25 out per million tokens, with cached input at $0.15. Muse Wonder, released October 12, 2026, sits above it for longer, harder agent work at $4 in and $20 out, also with 1M context. The endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Wonder, Muse Spark 1.3, Muse Spark Open
Full Meta profileGMI Cloud
GMI Cloud is a vertically integrated GPU cloud and inference platform that owns its NVIDIA hardware. It runs Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia, and as an NVIDIA Cloud Partner it gets priority access to H100, H200, B200 and GB200 supply, with GB300 on pre-order. In March 2026 it announced a 1GW sovereign AI factory in Kagoshima, Japan. The company pivoted from crypto mining into AI, which gave it experience standing up high-density power and cooling fast. In September 2026 it raised $668M: a $223M Series B led by ARCHIV with NVIDIA participating, plus a $445M credit facility led by CTBC. It reports over $600M in contracted ARR and about 4T tokens a week on its inference platform.
Example models: DeepSeek V4 Flash, GLM-5.2, Google Veo
Full GMI Cloud profileShould you choose Meta or GMI Cloud?
Meta
Choose Meta for
- One agentic model with cheap image and speech add-ons
- Drop-in OpenAI and Anthropic compatibility
- Low-cost prototyping on the Contributor tier
GMI Cloud
Choose GMI Cloud for
- APAC data residency in Taiwan, Thailand or Malaysia
- Video generation next to LLMs on one API
- Reserved GPU capacity as usage grows
Meta vs GMI Cloud at a glance
| Attribute | Meta | GMI Cloud |
|---|---|---|
| Model access | Closed API; open Muse Spark Open and Glimmer | Open and third-party models |
| Flagship models | Muse Wonder, Muse Spark 1.3, Muse Spark Open | DeepSeek V4 Flash, GLM-5.2, Google Veo |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | Near bare-metal performance |
| Price | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | H100 from $2.60/GPU-hr |
| Customization | Open Muse weights to fine-tune | Unknown |
| Deployment | Meta Model API, Oracle Cloud | Shared, autoscaling, reserved GPUs |
| Long context | 1M | Varies by model |
Frequently asked questions
What is the difference between Meta and GMI Cloud?
Meta sells its own Muse models from a new API. GMI Cloud sells 200+ third-party models on owned GPUs with data centers across the US and Asia-Pacific.
When should I choose Meta over GMI Cloud?
One agentic model with cheap image and speech add-ons; Drop-in OpenAI and Anthropic compatibility; Low-cost prototyping on the Contributor tier.
When should I choose GMI Cloud over Meta?
APAC data residency in Taiwan, Thailand or Malaysia; Video generation next to LLMs on one API; Reserved GPU capacity as usage grows.
Is Meta or GMI Cloud cheaper?
Meta: $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper. GMI Cloud: H100 from $2.60/GPU-hr. The cheaper choice depends on the model and workload.
Which has more context, Meta or GMI Cloud?
Meta: 1M. GMI Cloud: Varies by model.
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