Kimi K2.7 Code is Moonshot's cheapest model at $0.95/1M input tokens — 68% less than the flagship Kimi K3. It is also the best capability-per-dollar pick in the lineup.
The safest moonshot model worth using default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Kimi K2.7 Code is the strongest answer here for moonshot model worth using — pick it when quality of output matters more than the $0.95/1M/1M input you pay for it.
GPT-5.1-Codex-Max is the cheaper way in for moonshot model worth using, at $1.25/1M/1M input against Kimi K2.7 Code's $0.95/1M/1M.
Kimi K3 is the fastest of these for moonshot model worth using — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Kimi K2.7 Code is the lowest-cost Moonshot model: $0.95/1M input, $4/1M output.
Kimi K2.7 Code is the best capability-per-dollar pick (budget score 85/100).
Kimi K3 costs 3x more on input — reserve it for work where quality is the bottleneck.
Choose Kimi K2.7 Code for high-volume, low-stakes tasks like classification, extraction, and drafts.
Choose Kimi K2.7 Code as the everyday default if you want one budget model.
Route only the hardest tasks to Kimi K3 — a two-tier setup usually cuts spend 60–80%.
Switch the scoring lens to see whether the moonshot model worth using answer changes when cost, speed, or long-document depth leads the decision.
Moonshot / Premium / Aug 6, 2026
Closest Chinese challenger to the frontier — #4 overall on intelligence.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need fast responses or predictable output costs — always-on thinking burns tokens.
Where the moonshot model worth using recommendation shifts once you weigh price or latency differently.
Value coding specialist — 1T MoE agentic coder at budget prices.
Closest Chinese challenger to the frontier — #4 overall on intelligence.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Kimi K2.7 CodeMoonshot | $0.95/1M | $4.00/1M | $18 | 256k tokens | Fast | 88 | 68 | 70 |
| Kimi K3Moonshot | $3.00/1M | $15.00/1M | $60 | 1M tokens | Deliberate | 96 | 90 | 93 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for moonshot model worth using, what it is genuinely good at, and where we would steer you away from it.
The default answer for moonshot model worth using — 88/100 on the coding axis, and the model we would start with unless the price below rules it out.
An open-weight 1T-parameter MoE (32B active) coding specialist tuned for long-horizon agentic software engineering with markedly better token efficiency than its predecessor.
Your agent needs big-repo context (256K cap) or frontier general reasoning.
The value pick among coding specialists. K3 superseded it at the frontier a month later, but for pure coding-agent volume at a quarter of K3's input price, K2.7 Code remains the smarter buy.
Full pricing, benchmark table and release notes on the Kimi K2.7 Code page.
Here for latency: it answers fastest of anything listed for moonshot model worth using, at 96/100 on coding.
Moonshot's 2.8-trillion-parameter multimodal reasoning flagship with always-on thinking — the largest open-weight model ever released and the closest Chinese challenger to the Western frontier.
You need fast responses or predictable output costs — always-on thinking burns tokens.
The first Chinese model to genuinely crowd the Western frontier — #4 on aggregate intelligence ahead of Opus 4.8. The always-on thinking makes it slow and output-heavy, so cost per task runs above the sticker price. A serious Opus-class alternative if latency isn't critical.
Full pricing, benchmark table and release notes on the Kimi K3 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Kimi K2.7 Code at $0.95/1M input and $4/1M output tokens. Value coding specialist — 1T MoE agentic coder at budget prices.
Kimi K2.7 Code is the best capability-per-dollar pick in Moonshot's lineup (budget score 85/100). It handles cost-efficient agentic coding well — step up to Kimi K3 only where quality visibly falls short.
Kimi K2.7 Code costs $0.95/1M input vs $3/1M for Kimi K3 — a 68% saving on input tokens.
Kimi K3 — 1M tokens at $3/1M input. Context is where budget models are least compromised: you usually lose reasoning depth before you lose window size, so a cheap model is often a perfectly good choice for summarising or extracting from long documents.
256K context is a quarter of what 2026 rivals offer for large-repo agent work. Headline gains are on Moonshot's own in-house benchmark; general reasoning lags the Western frontier. Avoid it if your agent needs big-repo context (256K cap) or frontier general reasoning.
On a moderate workload of 10M input and 2M output tokens, Kimi K2.7 Code runs about $17.50 against $60.00 for Kimi K3 — a difference of $42.50 a month at the same volume. Output tokens dominate the bill on both, so the length of the responses you generate matters far more than the length of your prompts.
Mixing is almost always cheaper for the same quality. Route high-volume, low-stakes work — classification, extraction, first drafts, routine agent steps — to Kimi K2.7 Code, and reserve Kimi K3 for the calls where a wrong answer costs real time. Teams that split this way typically cut spend substantially without a quality drop anyone notices, because most tokens in a real workload are not hard problems.