Inception vs General Compute

Both sell fast output. Inception uses diffusion models on standard GPUs, and General Compute runs open models with decode on ASICs.

By The Subconscious Team · Updated October 27, 2026

Inception vs General Compute: key differences

Inception's closed Mercury models refine a block of tokens in parallel. Mercury 2.5 is a reasoning model with tool use and 260K context at $0.20 in and $0.75 out per million tokens; Inception reports over 1,100 tokens per second, and Artificial Analysis measures about 640. It is also on AWS Bedrock and Azure Foundry. General Compute serves open models with GPUs on prefill and Cerebras, SambaNova and other ASICs on decode. In its own benchmark it measured over 1,000 tokens per second on GPT-OSS 120B, which it sells at $0.21 in and $0.79 out.

Pick Inception for cheap, fast reasoning with long context, or to buy through Bedrock or Azure. Pick General Compute for a choice of open model families, private fine-tuned checkpoints, or dedicated racks with a tokens-per-second SLA. Both suit voice agents and real-time chat.

What Inception and General Compute do

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

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 Inception or General Compute?

Inception

Choose Inception for

Fast reasoning with 260K context
Speed without specialized silicon
Buying through Bedrock or Azure

General Compute

Choose General Compute for

Fast decode on open models
Private LoRA and fine-tuned models
Dedicated ASIC racks

Inception vs General Compute at a glance

Attribute	Inception	General Compute
Model access	Closed weights (Mercury dLLMs)	Open weights
Flagship models	Mercury 2.5, Mercury 2	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	1,100+ tok/s (vendor); ~640 measured	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	$0.20 in, $0.75 out (Mercury 2.5)	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Fine-tuning on request	Custom LoRA and fine-tuned checkpoints
Deployment	Serverless API, Bedrock, Azure, private	Serverless API, dedicated racks, bare metal
Long context	260K	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Inception and General Compute?

Both sell fast output. Inception uses diffusion models on standard GPUs, and General Compute runs open models with decode on ASICs.

When should I choose Inception over General Compute?

Fast reasoning with 260K context; Speed without specialized silicon; Buying through Bedrock or Azure.

When should I choose General Compute over Inception?

Fast decode on open models; Private LoRA and fine-tuned models; Dedicated ASIC racks.

Is Inception or General Compute cheaper?

Inception: $0.20 in, $0.75 out (Mercury 2.5). 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, Inception or General Compute?

Inception: 260K. 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 Inception Subconscious vs General Compute