Moonshot AI vs Inception

Moonshot's Kimi K3 is a top open-weight model that runs slowly. Inception's Mercury runs fast and sits with cost-optimized models. They trade opposite things.

By The Subconscious Team · Updated October 11, 2026

Moonshot AI vs Inception: key differences

Kimi K3 is a 2.8T-parameter open-weight model with 1M context, scoring 93.4% on SWE-bench Verified in Vals AI's testing, at $3 in and $15 out. It always thinks and runs around 33 tokens per second. Inception's Mercury 2.5 is a closed diffusion model with reasoning, tool use and 260K context at $0.20 in and $0.75 out, with quality closer to Claude Haiku 4.5 than the frontier. Inception reports 1,100+ tokens per second; Artificial Analysis measures about 640.

Kimi K3 fits long-horizon coding and research where quality and context matter more than wait time. Mercury fits the fast inner loop: autocomplete, edits, voice and high-volume extraction. Some products could use both, with Kimi planning and Mercury handling quick steps.

What Moonshot AI and Inception 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

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 Moonshot AI or Inception?

Moonshot AI

Choose Moonshot AI for

Hard coding tasks on large repos
1M context
Open weights to fine-tune

Inception

Choose Inception for

Very fast, cheap output
Code autocomplete and edits
Voice agents

Moonshot AI vs Inception at a glance

Attribute	Moonshot AI	Inception
Model access	Open weights, custom license	Closed weights (Mercury dLLMs)
Flagship models	Kimi K3, Kimi K2.8 Preview	Mercury 2.5, Mercury 2
Speed	~33 tok/s on Kimi K3	1,100+ tok/s (vendor); ~640 measured
Price	$3 in, $15 out (Kimi K3)	$0.20 in, $0.75 out (Mercury 2.5)
Customization	Open weights to fine-tune	Fine-tuning on request
Deployment	API, Kimi Code, OpenRouter	Serverless API, Bedrock, Azure, private
Long context	1M	260K

Frequently asked questions

What is the difference between Moonshot AI and Inception?

Moonshot's Kimi K3 is a top open-weight model that runs slowly. Inception's Mercury runs fast and sits with cost-optimized models. They trade opposite things.

When should I choose Moonshot AI over Inception?

Hard coding tasks on large repos; 1M context; Open weights to fine-tune.

When should I choose Inception over Moonshot AI?

Very fast, cheap output; Code autocomplete and edits; Voice agents.

Is Moonshot AI or Inception cheaper?

Moonshot AI: $3 in, $15 out (Kimi K3). Inception: $0.20 in, $0.75 out (Mercury 2.5). The cheaper choice depends on the model and workload.

Which has more context, Moonshot AI or Inception?

Moonshot 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 Moonshot AI Subconscious vs Inception