Moonshot AI vs General Compute

Moonshot makes Kimi K3, a 3T-class open model built for hard coding. General Compute serves other labs' smaller open models at high decode speed.

By The Subconscious Team · Updated October 27, 2026

Moonshot AI vs General Compute: key differences

Kimi K3 is Moonshot's own 2.8 trillion parameter open model with a 1M context, scoring 93.4% on SWE-bench Verified in Vals AI testing. It costs $3 in and $15 out per million tokens and runs around 33 tokens per second. General Compute trains no models. It serves MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B from $0.21 in and $0.38 out, with decode on ASICs from Cerebras, SambaNova and others. In its own benchmark it measured over 1,000 tokens per second on GPT-OSS 120B.

Moonshot is the pick when output quality on large repositories matters more than wait time or price. General Compute fits agent loops and real-time chat where a smaller model is good enough and decode speed sets the pace. Its context tops out at 192K on MiniMax M2.7, so repo-scale prompts that need 1M belong on Kimi.

What Moonshot AI and General Compute do

Moonshot AI

Moonshot AI is the Beijing lab behind the Kimi models. Its flagship Kimi K3 launched July 16, 2026 as a 2.8 trillion parameter mixture-of-experts model that activates 16 of 896 experts per token, with native vision and a 1M token context. It is the first open model in the 3T class, and full weights landed on Hugging Face on July 27. The hosted API costs $3 in and $15 out per million tokens, with cached input at $0.30, and it runs through an OpenAI-compatible endpoint, Kimi Code in the terminal, OpenRouter and Cloudflare Workers AI.

Example models: Kimi K3, Kimi K2.8 Preview

Full Moonshot 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 Moonshot AI or General Compute?

Moonshot AI

Choose Moonshot AI for

Hard coding on huge repositories
1M context
Kimi Code in the terminal

General Compute

Choose General Compute for

Fast decode in agent loops
Low per-token prices
Voice agents and real-time chat

Moonshot AI vs General Compute at a glance

Attribute	Moonshot AI	General Compute
Model access	Open weights, custom license	Open weights
Flagship models	Kimi K3, Kimi K2.8 Preview	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	~33 tok/s on Kimi K3	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	$3 in, $15 out (Kimi K3)	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	Open weights to fine-tune	Custom LoRA and fine-tuned checkpoints
Deployment	API, Kimi Code, OpenRouter	Serverless API, dedicated racks, bare metal
Long context	1M	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Moonshot AI and General Compute?

Moonshot makes Kimi K3, a 3T-class open model built for hard coding. General Compute serves other labs' smaller open models at high decode speed.

When should I choose Moonshot AI over General Compute?

Hard coding on huge repositories; 1M context; Kimi Code in the terminal.

When should I choose General Compute over Moonshot AI?

Fast decode in agent loops; Low per-token prices; Voice agents and real-time chat.

Is Moonshot AI or General Compute cheaper?

Moonshot AI: $3 in, $15 out (Kimi K3). 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, Moonshot AI or General Compute?

Moonshot 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 Moonshot AI Subconscious vs General Compute