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xAI vs Particle.AI
A closed lab with a 1M window on Grok 4.20 against an early startup serving cheap Flash-class open models with 1M context through Vercel AI Gateway.
By The Subconscious Team · Updated
xAI vs Particle.AI: key differences
Both offer 1M context at low prices, from very different companies. xAI's Grok 4.20 and 4.3 keep a 1M window at $1.25 in and $2.50 out, while the flagship Grok 4.6 has 500K at $2 in and $6 out. Particle AI serves a small set of open Flash-class models through Vercel AI Gateway, like 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. Particle is much cheaper per token. xAI brings closed models, X Search and an established API.
Long prompts deserve a closer look. xAI bills the whole request at double once a prompt hits 200K tokens, so filling that 1M window gets expensive. Particle's per-token prices are low enough that even long prompts stay cheap, but it is a very early company with a tiny catalog and little track record, and some listings are slow, like 3.5 seconds of latency on DeepSeek V4.1 Flash. Particle fits as a cheap route inside a multi-provider gateway. xAI fits when quality, live data and a known vendor matter more than price.
What xAI and Particle.AI do
xAI
xAI sells the Grok models through its own API. Grok 4.6 is the current flagship and xAI tells developers to use it for everything outside audio, image and video, code included. 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 4.6, Grok 4.20
Full xAI 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 xAI or Particle.AI?
xAI
Choose xAI for
- Agents that need live X and web data
- Closed-model quality from an established API
- First-party image, video and audio APIs
Particle.AI
Choose Particle.AI for
- Very cheap high-volume calls on Flash-class models
- A price-optimized route inside Vercel AI Gateway
- Cache reads at $0.03 per million on every listing
xAI vs Particle.AI at a glance
| Attribute | xAI |  Particle.AI |
|---|---|---|
| Model access | Closed | Open weights |
| Flagship models | Grok 4.6, Grok 4.20, grok-build | DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | ~54 tok/s on Grok 4.6 | ~157 tok/s on DeepSeek V4.1 Flash |
| Price | $2 in, $6 out (Grok 4.6); 2x past 200K | $0.10 in, $0.40 out (GLM 5.3 Flash) |
| Customization | Unknown | Unknown |
| Deployment | First-party API | Via Vercel AI Gateway |
| Long context | 500K (4.6), 1M (4.20, 4.3) | 1M |
Frequently asked questions
What is the difference between xAI and Particle.AI?
A closed lab with a 1M window on Grok 4.20 against an early startup serving cheap Flash-class open models with 1M context through Vercel AI Gateway.
When should I choose xAI over Particle.AI?
Agents that need live X and web data; Closed-model quality from an established API; First-party image, video and audio APIs.
When should I choose Particle.AI over xAI?
Very cheap high-volume calls on Flash-class models; A price-optimized route inside Vercel AI Gateway; Cache reads at $0.03 per million on every listing.
Is xAI or Particle.AI cheaper?
xAI: $2 in, $6 out (Grok 4.6); 2x past 200K. 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, xAI or Particle.AI?
xAI: 500K (4.6), 1M (4.20, 4.3). Particle.AI: 1M.
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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.