Kimi K3 is Moonshot's best model for research — it scores 93/100 vs 70/100 for Kimi K2.7 Code, at $3/1M input tokens. Across all providers, GPT-6 Astra still leads research at 100/100 — worth considering if you're not committed to Moonshot.
The safest moonshot model for research default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Kimi K3 is the strongest answer here for moonshot model for research — pick it when quality of output matters more than the $3.00/1M/1M input you pay for it.
Kimi K2.7 Code handles the same job for about 73% less per token. Start here and only move up if the output is not good enough.
Kimi K3 carries 1M tokens of context, so it is the pick for moonshot model for research when whole documents, transcripts, or repositories go in at once.
Kimi K3 leads Moonshot's lineup for research at 93/100 ($3/1M input, 1M context).
Kimi K2.7 Code is the value pick at $0.95/1M input with a research score of 70/100.
GPT-6 Astra (OpenAI) is the overall research leader at 100/100 if provider choice is open.
Choose Kimi K3 when research quality is the priority and you're staying on Moonshot.
Choose Kimi K2.7 Code when token volume matters more than peak quality.
Teams open to other providers should also evaluate GPT-6 Astra before committing.
Switch the scoring lens to see whether the moonshot model for research 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 for research recommendation shifts once you weigh price or latency differently.
Closest Chinese challenger to the frontier — #4 overall on intelligence.
Value coding specialist — 1T MoE agentic coder at budget prices.
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 K3Moonshot | $3.00/1M | $15.00/1M | $60 | 1M tokens | Deliberate | 96 | 90 | 93 |
| Kimi K2.7 CodeMoonshot | $0.95/1M | $4.00/1M | $18 | 256k tokens | Fast | 88 | 68 | 70 |
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 for research, what it is genuinely good at, and where we would steer you away from it.
Our pick for moonshot model for research. It scores 38/100 on the budget axis we weight this page by, and nothing else in this shortlist matches it on output quality.
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.
The value option for moonshot model for research: about 73% less per token than Kimi K3, at 85/100 on budget. Worth starting here and moving up only if the output disappoints.
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.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Newsletter
We email when the moonshot model for research pick changes, when one of these models moves on price, or when something new displaces the current leader.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
Kimi K3 — it scores 93/100 on research in this directory, ahead of Kimi K2.7 Code at 70/100. Closest Chinese challenger to the frontier — #4 overall on intelligence.
Not overall. GPT-6 Astra (OpenAI) leads the directory for research at 100/100 vs Kimi K3's 93/100. Kimi K3 is the best pick if you're staying within Moonshot's ecosystem.
Kimi K2.7 Code at $0.95/1M input tokens (research score: 70/100). Use it for volume work and reserve Kimi K3 for the tasks where quality matters most.
$3/1M input tokens and $15/1M output tokens via the API, or through Kimi Moderato at $19/mo for chat use. Context window: 1M tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $60.00, against $17.50 for Kimi K2.7 Code.
Most expensive Chinese-lab model ever ($3/$15) with always-on thinking driving high output-token burn and slow responses. 2.8T size makes self-hosting impractical despite open weights; consumer signups were paused July 19 over GPU capacity. Concretely, avoid it if you need fast responses or predictable output costs — always-on thinking burns tokens. If none of that is negotiable, GPT-6 Astra (OpenAI) is the cross-provider leader at 100/100.
hardest reasoning tasks — #4 of all models on AA Intelligence Index v4.1 (57.1), ahead of Claude Opus 4.8, agentic coding at 81.2 FrontierSWE and 88.3 Terminal-Bench 2.0 (Moonshot-reported), and 1M-context research synthesis with always-on extended thinking. Its 1M-token context window is the practical limit on how much you can hand it in one go.
Kimi K3 scores 93/100 on research against 70/100 for Kimi K2.7 Code, at 3x the input price. That premium is worth it on work where a wrong answer costs real time or money, and hard to justify on high-volume, low-stakes calls. Most teams run both and route by task rather than picking one.