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Cerebras vs Meta
Meta's Muse API bundles a 1M-context agentic model with cheap image and speech. Cerebras sells extreme speed on a narrow open catalog.
By The Subconscious Team · Updated
Cerebras vs Meta: key differences
Meta's Model API is a new closed platform in public preview. Muse Spark 1.3 offers 1M context at $1.25 in and $4.25 out, with OpenAI, Anthropic and stateful agentic formats, and the same key reaches Muse Image at $0.01 per image and transcription at $0.18 per audio hour. Cerebras is a chip company serving open weights fast: GPT-OSS 120B near 3,000 tokens per second at $0.35 in and $0.75 out, plus Gemma 4 31B on the shared API. Meta covers more of an app. Cerebras covers one step of it much faster.
Meta's Contributor tier cuts prices to $0.10 in and $0.20 out, but it trains on your data and drops rate limits to 100 requests per minute, which rules it out for most business traffic. Cerebras trades on Nasdaq and serves OpenAI under a deal worth over $10B, while Meta's API is still in preview. Choose Meta for cost-sensitive coding agents and multimodal assistants that need image or speech on the same bill. Choose Cerebras when output speed is the product.
What Cerebras and Meta do
Cerebras
Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.
Example models: GPT-OSS 120B, Gemma 4 31B
Full Cerebras profileMeta
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 profileShould you choose Cerebras or Meta?
Cerebras
Choose Cerebras for
- Real-time generation where speed is the product
- Fast long outputs on GPT-OSS 120B
- Buying through AWS Marketplace or OpenRouter
Meta
Choose Meta for
- Agentic coding with a 1M-context model at mid-tier prices
- Image generation and transcription on one key
- Near-free prototyping when data sharing is acceptable
Cerebras vs Meta at a glance
| Attribute | Cerebras | Meta |
|---|---|---|
| Model access | Open weights | Closed API; open Muse Glimmer |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | Muse Spark 1.3, Muse Glimmer |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~145–233 tok/s on Muse Spark 1.3 |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | $1.25 in, $4.25 out; Contributor tier cheaper |
| Customization | Unknown | Open Muse Glimmer weights to fine-tune |
| Deployment | Shared API, dedicated, partners | Meta Model API (preview) |
| Long context | Unknown | 1M |
Frequently asked questions
What is the difference between Cerebras and Meta?
Meta's Muse API bundles a 1M-context agentic model with cheap image and speech. Cerebras sells extreme speed on a narrow open catalog.
When should I choose Cerebras over Meta?
Real-time generation where speed is the product; Fast long outputs on GPT-OSS 120B; Buying through AWS Marketplace or OpenRouter.
When should I choose Meta over Cerebras?
Agentic coding with a 1M-context model at mid-tier prices; Image generation and transcription on one key; Near-free prototyping when data sharing is acceptable.
Is Cerebras or Meta cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Meta: $1.25 in, $4.25 out; Contributor tier cheaper. The cheaper choice depends on the model and workload.
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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.