vs
Meta vs Particle.AI
Both offer 1M context at low prices. Meta sells its own closed Muse Spark; Particle AI serves cheap Flash-class open models through Vercel AI Gateway.
By The Subconscious Team · Updated
Meta vs Particle.AI: key differences
On price, Particle AI undercuts Meta's standard tier. Its Vercel AI Gateway listings include GLM 5.3 Flash at $0.10 in and $0.40 out and DeepSeek V4.1 Flash at $0.25 in and $1 out, all with 1M context and cache reads at $0.03 per million. Meta's Muse Spark 1.3 costs $1.25 in and $4.25 out with 1M context, and cached input at $0.15. Meta's Contributor tier, at $0.10 in and $0.20 out, matches Particle's floor, but only if Meta can train on your traffic.
The bigger difference is what each company is. Particle is a very early startup, still hiring its founding team, with a tiny catalog, little public track record and slow latency on some listings, like 3.5 seconds on DeepSeek V4.1 Flash. Meta is a giant, but its Model API is a preview with a short record of its own. Meta brings a stronger agentic model plus image and speech APIs. Particle works best as a cheap fallback route inside a multi-provider gateway.
What Meta and Particle.AI 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
Particle.AI
Particle AI is an early San Francisco infrastructure startup with a mission to make intelligence as cheap and abundant as electricity. The team works on post-training, inference optimization and distributed systems, all aimed at pushing down cost per unit of intelligence. It is still hiring its founding team and works fully in person. Public detail about funding and founders is thin as of this writing.
Example models: DeepSeek V4.1 Flash, GLM 5.3 Flash
Full Particle.AI profileShould you choose Meta or Particle.AI?
Meta
Choose Meta for
- A single agentic model for coding and computer use
- Images and transcription next to text
- Near-free prototyping when data sharing is acceptable
Particle.AI
Choose Particle.AI for
- Low list prices on DeepSeek and GLM Flash models
- A price-optimized route in Vercel AI Gateway
- High-volume calls with 1M context
Meta vs Particle.AI at a glance
| Attribute | Meta |  Particle.AI |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Open weights |
| Flagship models | Muse Spark 1.3, Muse Glimmer | DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | ~157 tok/s on DeepSeek V4.1 Flash |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | $0.10 in, $0.40 out (GLM 5.3 Flash) |
| Customization | Open Muse Glimmer weights to fine-tune | Unknown |
| Deployment | Meta Model API (preview) | Via Vercel AI Gateway |
| Long context | 1M | 1M |
Frequently asked questions
What is the difference between Meta and Particle.AI?
Both offer 1M context at low prices. Meta sells its own closed Muse Spark; Particle AI serves cheap Flash-class open models through Vercel AI Gateway.
When should I choose Meta over Particle.AI?
A single agentic model for coding and computer use; Images and transcription next to text; Near-free prototyping when data sharing is acceptable.
When should I choose Particle.AI over Meta?
Low list prices on DeepSeek and GLM Flash models; A price-optimized route in Vercel AI Gateway; High-volume calls with 1M context.
Is Meta or Particle.AI cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. Particle.AI: $0.10 in, $0.40 out (GLM 5.3 Flash). The cheaper choice depends on the model and workload.
Which has more context, Meta or Particle.AI?
Meta: 1M. Particle.AI: 1M.
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.