vs
Morph vs Particle.AI
Particle AI serves cheap Flash-class open models with 1M context. Morph applies code edits fast. One is the model, the other is a tool around it.
By The Subconscious Team · Updated
Morph vs Particle.AI: key differences
Particle AI and Morph are not alternatives. Particle is an early startup serving a few Flash-class open models through its own API and Vercel AI Gateway, with its own API listing GLM 5.3 Flash at $0.15 in and $0.50 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. Morph builds small specialist models for coding agents, led by Fast Apply, which merges a main model's edits into full files at 10,500+ tokens per second with up to 98% accuracy.
A budget coding agent could combine them: a cheap Flash model on Particle writes diffs, and Morph applies them, with WarpGrep handling repo search and Compact trimming context. Particle's weak spots are a tiny catalog, little track record and slow latency on some listings, like about 1.9 seconds to first token on Particle-1 via Vercel AI Gateway, which Morph's fast apply step cannot fix. Morph's weak spot is that it only covers part of the job, and its 2 to 4% merge error rate calls for tests before edits ship.
What Morph and Particle.AI do
Morph
Morph builds small, very fast specialist models that sit beside a big coding model inside an agent. Its flagship is Fast Apply. The frontier model writes only the changed lines with // ... existing code ... markers, and Morph merges them into the full file at 10,500+ tokens per second with up to 98% accuracy. It is the same idea behind Cursor's instant apply, offered as an OpenAI compatible API.
Example models: morph-v3-fast, morph-v3-large
Full Morph 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. Mixpanel founder Suhail Doshi started it, with backing from Y Combinator, 8VC, Caffeinated Capital and angels including Patrick Collison, Adam D'Angelo and Aaron Levie.
Example models: Particle-1, DeepSeek V4.1 Flash, GLM 5.3 Flash
Full Particle.AI profileShould you choose Morph or Particle.AI?
Morph
Choose Morph for
- Fast, accurate file edits in coding agents
- Fewer output tokens than full rewrites
- Agentic repo search with WarpGrep
Particle.AI
Choose Particle.AI for
- A cheap main model with 1M context
- High-volume calls on DeepSeek and GLM Flash
- A fallback route inside Vercel AI Gateway
Morph vs Particle.AI at a glance
| Attribute | Morph |  Particle.AI |
|---|---|---|
| Model access | Specialist models | Open weights plus its own Particle-1 tune |
| Flagship models | morph-v3-fast, morph-v3-large | Particle-1, DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | 10,500+ tok/s Fast Apply | ~110 tok/s on Particle-1 (Vercel AI Gateway) |
| Price | $0.80 in, $1.20 out (morph-v3-fast) | $0.15 in, $0.50 out (GLM 5.3 Flash); $0.25 in, $1 out (Particle-1) |
| Customization | Fine-tuning offered | Unknown |
| Deployment | OpenAI compatible API; self-hosted on Enterprise | Serverless API, Vercel AI Gateway |
| Long context | Unknown | 1M |
Frequently asked questions
What is the difference between Morph and Particle.AI?
Particle AI serves cheap Flash-class open models with 1M context. Morph applies code edits fast. One is the model, the other is a tool around it.
When should I choose Morph over Particle.AI?
Fast, accurate file edits in coding agents; Fewer output tokens than full rewrites; Agentic repo search with WarpGrep.
When should I choose Particle.AI over Morph?
A cheap main model with 1M context; High-volume calls on DeepSeek and GLM Flash; A fallback route inside Vercel AI Gateway.
Is Morph or Particle.AI cheaper?
Morph: $0.80 in, $1.20 out (morph-v3-fast). Particle.AI: $0.15 in, $0.50 out (GLM 5.3 Flash); $0.25 in, $1 out (Particle-1). The cheaper choice depends on the model and workload.
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.