Particle.AI vs Inception

Particle.AI serves cheap open Flash-class models with 1M context. Inception serves closed diffusion models built for very fast output.

By The Subconscious Team · Updated October 11, 2026

Particle.AI vs Inception: key differences

Particle serves a small set of open models on Vercel AI Gateway, such as GLM 5.3 Flash at $0.10 in and $0.40 out and DeepSeek V4.1 Flash at $0.25 in and $1 out, each with 1M context. DeepSeek V4.1 Flash runs at about 193 tokens per second with about 0.5 seconds of latency. Inception's Mercury 2.5 is a closed reasoning model with tool use and 260K context at $0.20 in and $0.75 out. Inception reports over 1,100 tokens per second; Artificial Analysis measures about 640.

Particle fits long-context background calls and price-optimized routes inside a gateway. Inception fits user-facing loops where speed matters, like autocomplete, voice and chat. Particle is a very early company with little track record; Inception raised a $50M seed and sells through AWS Bedrock and Azure Foundry.

What Particle.AI and Inception 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

Inception

Inception builds diffusion large language models (dLLMs). Every other LLM on this list generates text one token at a time; Inception's Mercury models start from a rough draft of a whole block and refine it over a few passes, producing many tokens in parallel. That ties speed less to output length and lets the models run fast on ordinary NVIDIA GPUs rather than custom chips. The company was founded by professors from Stanford, UCLA and Cornell behind core work on diffusion, Flash Attention and DPO, and is led by Stanford's Stefano Ermon. It raised a $50M seed in November 2025 led by Menlo Ventures, with Mayfield, M12, NVentures, Snowflake and Databricks, plus Andrew Ng and Andrej Karpathy as angels.

Example models: Mercury 2.5, Mercury 2

Full Inception profile

Should you choose Particle.AI or Inception?

Particle.AI

Choose Particle.AI for

Cheap 1M context calls on Flash models
Fallback routing inside Vercel AI Gateway
Long-document background jobs

Inception

Choose Inception for

Very fast output for real-time loops
Voice agents and code autocomplete
Buying through Bedrock or Azure

Particle.AI vs Inception at a glance

Attribute	Particle.AI	Inception
Model access	Open weights	Closed weights (Mercury dLLMs)
Flagship models	DeepSeek V4.1 Flash, GLM 5.3 Flash	Mercury 2.5, Mercury 2
Speed	~193 tok/s on DeepSeek V4.1 Flash	1,100+ tok/s (vendor); ~640 measured
Price	$0.10 in, $0.40 out (GLM 5.3 Flash)	$0.20 in, $0.75 out (Mercury 2.5)
Customization	Unknown	Fine-tuning on request
Deployment	Via Vercel AI Gateway	Serverless API, Bedrock, Azure, private
Long context	1M	260K

Frequently asked questions

What is the difference between Particle.AI and Inception?

Particle.AI serves cheap open Flash-class models with 1M context. Inception serves closed diffusion models built for very fast output.

When should I choose Particle.AI over Inception?

Cheap 1M context calls on Flash models; Fallback routing inside Vercel AI Gateway; Long-document background jobs.

When should I choose Inception over Particle.AI?

Very fast output for real-time loops; Voice agents and code autocomplete; Buying through Bedrock or Azure.

Is Particle.AI or Inception cheaper?

Particle.AI: $0.10 in, $0.40 out (GLM 5.3 Flash). Inception: $0.20 in, $0.75 out (Mercury 2.5). The cheaper choice depends on the model and workload.

Which has more context, Particle.AI or Inception?

Particle.AI: 1M. Inception: 260K.

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 Inception