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xAI vs GMI Cloud
xAI sells Grok models and X data from the US. GMI Cloud sells 200+ third-party models and owned GPUs, with data centers in Taiwan, Thailand and Malaysia.
By The Subconscious Team · Updated
xAI vs GMI Cloud: key differences
GMI Cloud is a GPU cloud that owns its NVIDIA hardware, with Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia. Its Inference Engine exposes 200+ models through an OpenAI compatible API, including 45+ LLMs and 50+ video models from providers like Google Veo, Kling and MiniMax. xAI is a single lab selling Grok, with Grok 4.7 at $2 in and $6 out, 1M context on Grok 4.20, and separate first-party image, video and audio APIs.
Region and catalog drive the choice. APAC companies that need inference kept in-country, or multimodal apps that want LLMs and video generation on one bill, fit GMI, which also lets customers move from shared endpoints to reserved H100 or H200 capacity. Its LLM catalog is smaller than larger peers, and third-party benchmarking is thin. xAI fits teams that want a closed frontier-class model with live X data, as long as prompts stay under 200K tokens, where the price doubles.
What xAI and GMI Cloud do
xAI
xAI sells the Grok models through its own API and Amazon Bedrock. SpaceX acquired it in February 2026; it became SpaceXAI in July and SpaceX Super Intelligence (SpaceXSI) in October, the name its docs now use. Grok 4.7, released September 21, 2026, is the current flagship, pitched for coding, agentic tasks and knowledge work. It has a 500K context window and costs $2 in and $6 out per million tokens under 200K prompt tokens. Older Grok 4.20 and 4.3 models keep a 1M window at $1.25 in and $2.50 out, which is aggressive for that capability class.
Example models: Grok 4.7, Grok 4.20
Full xAI 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 xAI or GMI Cloud?
xAI vs GMI Cloud at a glance
| Attribute | xAI | GMI Cloud |
|---|---|---|
| Model access | Closed | Open and third-party models |
| Flagship models | Grok 4.7, Grok 4.20, grok-build | DeepSeek V4 Flash, GLM-5.2, Google Veo |
| Speed | ~54 tok/s on Grok 4.6 | Near bare-metal performance |
| Price | $2 in, $6 out (Grok 4.7); 2x past 200K | H100 from $2.60/GPU-hr |
| Customization | Unknown | Unknown |
| Deployment | First-party API, Amazon Bedrock | Shared, autoscaling, reserved GPUs |
| Long context | 500K (4.7), 1M (4.20, 4.3) | Varies by model |
Frequently asked questions
What is the difference between xAI and GMI Cloud?
xAI sells Grok models and X data from the US. GMI Cloud sells 200+ third-party models and owned GPUs, with data centers in Taiwan, Thailand and Malaysia.
When should I choose xAI over GMI Cloud?
Closed Grok models with native X Search; Cheap output on reasoning workloads; Teams that want a model API, not a GPU cloud.
When should I choose GMI Cloud over xAI?
APAC data residency in Taiwan, Thailand or Malaysia; LLMs and video models on one bill; Reserved GPU capacity on the same API.
Is xAI or GMI Cloud cheaper?
xAI: $2 in, $6 out (Grok 4.7); 2x past 200K. GMI Cloud: H100 from $2.60/GPU-hr. The cheaper choice depends on the model and workload.
Which has more context, xAI or GMI Cloud?
xAI: 500K (4.7), 1M (4.20, 4.3). GMI Cloud: Varies by model.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.