Particle.AI vs General Compute

Particle.AI serves cheap Flash-class open models with 1M context. General Compute serves older open models with fast ASIC decode.

By The Subconscious Team · Updated October 27, 2026

Particle.AI vs General Compute: key differences

Particle.AI sells mostly through Vercel AI Gateway, with DeepSeek V4.1 Flash at $0.25 in and $1 out per million tokens at about 193 tokens per second, and GLM 5.3 Flash at $0.10 in and $0.40 out, all with 1M context and $0.03 cache reads. General Compute sells its own OpenAI-compatible API in us-west-2. DeepSeek V3.2 costs $0.25 in and $0.38 out with 32K context, and GPT-OSS 120B costs $0.21 in and $0.79 out with 128K. In its own benchmark it measured over 1,000 tokens per second on GPT-OSS 120B.

Pick Particle.AI for newer Flash models with 1M context, or a cheap route inside a gateway you already use. Pick General Compute when output speed matters more than context length, or when you want plans with higher rate limits, private fine-tuned models or dedicated capacity.

What Particle.AI and General Compute do

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: DeepSeek V4.1 Flash, GLM 5.3 Flash

Full Particle.AI 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 profile

Should you choose Particle.AI or General Compute?

Particle.AI

Choose Particle.AI for

Newer Flash-class models
1M token context
Trying it through Vercel AI Gateway

General Compute

Choose General Compute for

Fast output on GPT-OSS 120B
Private fine-tuned model IDs
Dedicated capacity under contract

Particle.AI vs General Compute at a glance

Attribute	Particle.AI	General Compute
Model access	Open weights	Open weights
Flagship models	DeepSeek V4.1 Flash, GLM 5.3 Flash	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	~193 tok/s on DeepSeek V4.1 Flash	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	$0.10 in, $0.40 out (GLM 5.3 Flash)	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Unknown	Custom LoRA and fine-tuned checkpoints
Deployment	Via Vercel AI Gateway	Serverless API, dedicated racks, bare metal
Long context	1M	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Particle.AI and General Compute?

Particle.AI serves cheap Flash-class open models with 1M context. General Compute serves older open models with fast ASIC decode.

When should I choose Particle.AI over General Compute?

Newer Flash-class models; 1M token context; Trying it through Vercel AI Gateway.

When should I choose General Compute over Particle.AI?

Fast output on GPT-OSS 120B; Private fine-tuned model IDs; Dedicated capacity under contract.

Is Particle.AI or General Compute cheaper?

Particle.AI: $0.10 in, $0.40 out (GLM 5.3 Flash). 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, Particle.AI or General Compute?

Particle.AI: 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.

Subconscious vs Particle.AI Subconscious vs General Compute