Moonshot AI

Moonshot AI — Kimi models — open-weight agentic performance at low cost. Moonshot's Kimi K2 line is the open-weight model most often cited as competitive with closed frontier models on agentic and tool-use benchmarks, released under a permissive modified-MIT licence. The first-party platform is inexpensive and OpenAI-compatible; the weights are also hosted by most routers on this page, which is the practical route for anyone who cannot send data to a mainland-China endpoint. Founded 2023. Open source under Modified MIT.

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it wins.

• $/M output — $2.5 /M tok, 3rd of 8.
• $/M input — $0.6 /M tok, 2nd of 8.

Each of those is ranked against the whole roster on its category page, not against a hand-picked subset, so a first place here means first of everything we list.

Where it gives ground.

• Zero retention — No. Amazon Bedrock records Yes.
• Image input — No. Anthropic records Yes.
• Context window — 256,000 tokens, 7th of 10. Anthropic records 1,000,000 tokens.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 22 of 27 tracked fields carry a value for Moonshot AI, and 2 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Qwen. Moonshot AI is ahead on Open weights (Yes against Partial) and $/M output ($2.5 /M tok against $6 /M tok). Qwen takes MCP support (Partial against No) and Image input (Partial against No). That pattern repeats across the rest of the roster — see Moonshot AI alternatives for the other rivals, each compared the same way.

Founded

2023

Licence

Modified MIT (open source)

Fields we track

22 of 27

Last verified

2026-01-15

Kimi K2 is the strongest argument that open weights have caught up on agentic work specifically — it holds up in tool loops where other open models fall apart. Reach it through Groq, Fireworks or Together rather than the first-party endpoint, which is slower and has weaker data terms.

$/M output

$2.5 /M tok3rd of 8

Inferredverified 2026-01-15

$/M input

$0.6 /M tok2nd of 8

Inferredverified 2026-01-15

Zero retention

○No10th of 12

Inferredverified 2026-01-15

Image input

○No16th of 18

Inferredverified 2026-01-15

$/M cache read

$0.15 /M tok3rd of 5

Inferredverified 2026-01-15

Context window

256,000 tokens7th of 10

Inferredverified 2026-01-15

Tool use

●Yes1st of 18

Community-reportedverified 2026-01-15

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

Moonshot AI is the right call in these situations, each one drawn from a field we actually record:

• Agent loops on an open-weight budget.
• Self-hosting a tool-use-capable model under a permissive licence.
• Replacing a frontier model on the cheaper half of a mixed workload.
• $/M output is your binding constraint. Moonshot AI records $2.5 /M tok, 3rd of 8 in the LLM APIs roster. We define that field as pay-as-you-go list price per million output tokens for the named flagship model, including reasoning or thinking tokens where those are billed as output.
• $/M input is your binding constraint. Moonshot AI records $0.6 /M tok, 2nd of 8 in the LLM APIs roster. We define that field as pay-as-you-go list price per million input tokens for the named flagship model, uncached, at the standard context tier.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• Zero retention. Moonshot AI records No on the LLM APIs table. Amazon Bedrock records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.
• Image input. Moonshot AI records No on the LLM APIs table. Anthropic records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.
• Context window. Moonshot AI records 256,000 tokens, 7th of 10 in the LLM APIs roster. Anthropic records 1,000,000 tokens on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 5 of the 27 fields we track for Moonshot AI are still blank: Max output, TTFT p50, Output tok/s, Batch discount and Notice period. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.