vs
Meta vs General Compute
Meta sells its own Muse models through a closed API in preview. General Compute serves open models from other labs with decode on ASICs.
By The Subconscious Team · Updated
Meta vs General Compute: key differences
Meta's Muse Spark 1.3 has a 1M context at $1.25 in and $4.25 out per million tokens, runs about 145 to 233 tokens per second, and speaks OpenAI, Anthropic Messages and a stateful agentic format. A Contributor tier cuts prices to $0.10 in and $0.20 out if Meta can train on your data. General Compute serves MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B from $0.21 in, over an OpenAI-compatible API only. It reports over 1,000 tokens per second on GPT-OSS 120B in its own benchmark, and accepts LoRA or fully fine-tuned checkpoints.
Meta is the pick for a first-party agentic model with long context, image and voice on one key, or near-free prototyping where data sharing is fine. The API is still in preview. General Compute fits teams that want open models they can fine-tune and serve fast, plus dedicated racks once its larger deployments go live in Q1 2027.
What Meta and General Compute 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 opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Spark 1.3, Muse Glimmer
Full Meta profile
General Compute
General Compute is a San Francisco inference cloud built on alternative chips. It splits each request across hardware, with NVIDIA GPUs handling prefill and ASICs from Cerebras, SambaNova, Positron AI and d-Matrix handling decode. In its own open benchmark on GPT-OSS 120B it measured 1,025 to 1,234 output tokens per second against 100 to 121 on an unnamed GPU cloud, and up to 16.2x faster end to end. Founders Finn Puklowski and Jason Goodison raised a $15M seed led by FUSE, an up to $400M debt facility from Upper90 and, on October 27, 2026, a $100M Series A co-led by Blackbird and Abstract.
Example models: MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Full General Compute profileShould you choose Meta or General Compute?
Meta
Choose Meta for
- A first-party agentic model with 1M context
- Near-free prototyping on the Contributor tier
- Image and transcription on the same key
General Compute
Choose General Compute for
- Fast decode on open models
- Serving your own fine-tuned checkpoints
- Dedicated ASIC capacity
Meta vs General Compute at a glance
| Attribute | Meta |  General Compute |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Open weights |
| Flagship models | Muse Spark 1.3, Muse Glimmer | MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | 1,000+ tok/s on GPT-OSS 120B (vendor) |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | $0.21–$0.28 in, $0.38–$1.20 out per 1M |
| Customization | Open Muse Glimmer weights to fine-tune | Custom LoRA and fine-tuned checkpoints |
| Deployment | Meta Model API (preview) | Serverless API, dedicated racks, bare metal |
| Long context | 1M | 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Meta and General Compute?
Meta sells its own Muse models through a closed API in preview. General Compute serves open models from other labs with decode on ASICs.
When should I choose Meta over General Compute?
A first-party agentic model with 1M context; Near-free prototyping on the Contributor tier; Image and transcription on the same key.
When should I choose General Compute over Meta?
Fast decode on open models; Serving your own fine-tuned checkpoints; Dedicated ASIC capacity.
Is Meta or General Compute cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. General Compute: $0.21–$0.28 in, $0.38–$1.20 out per 1M. The cheaper choice depends on the model and workload.
Which has more context, Meta or General Compute?
Meta: 1M. General Compute: 192K (MiniMax M2.7).
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.