Both are Moonshot AI models. Kimi K3 is the newer, generally stronger default; reach for Kimi K2.7 Code when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Kimi K2.7 Code and Kimi K3 are both Moonshot AI models, so the real question is not which lab to trust but which tier fits your workload and budget. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Price: Kimi K2.7 Code is about 3.2× cheaper on input ($0.95/$4 per 1M tokens vs $3/$15 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Kimi K3 holds 4× more — 1M (~1,573 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Kimi K3 is the newer model by about 45 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Kimi K2.7 Code
Kimi K3
Provider
Moonshot AI (China)
Moonshot AI (China)
Released
June 12, 2026
July 27, 2026
Context window
256K (~393 pages)
1M (~1,573 pages)
Price (in/out)
$0.95/$4 per 1M tokens
$3/$15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic software engineering: Kimi K2.7 Code — Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it runs cheaper at $0.95/$4 per 1M tokens.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — Kimi K2.7 Code lists token-efficient reasoning (~30% fewer than K2.6) among its strengths; Kimi K3 does not.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: Kimi K3 — Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video): Kimi K3 — Its 1M window holds about 4× more than Kimi K2.7 Code's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: Kimi K3 — Kimi K2.7 Code is comparatively weak here — only self-reported benchmarks; no SWE-Bench Verified
Lowest cost at scale: Kimi K2.7 Code — At $0.95/$4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Kimi K3 — Its 1M window is about 4× larger than Kimi K2.7 Code's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Kimi K2.7 Code — At $0.95/$4 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K3 — Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic software engineering: Kimi K2.7 Code — It is specifically built for that.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable: Kimi K3 — That is its strongest area.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs are real: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Because Kimi K2.7 Code and Kimi K3 come from the same lab (Moonshot AI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Kimi K3 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Kimi K3 and drop down only with a concrete reason.
Frequently asked questions
Is Kimi K2.7 Code or Kimi K3 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Kimi K2.7 Code leans toward long-horizon agentic software engineering while Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.7 Code or Kimi K3?
Kimi K2.7 Code is cheaper — $0.95/$4 per 1M tokens vs $3/$15 per 1M tokens, roughly 3.2× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Kimi K2.7 Code to Kimi K3?
Since both are Moonshot AI models, the newer one (Kimi K3) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Kimi K2.7 Code or Kimi K3?
Kimi K3 — released July 27, 2026, about 45 days after Kimi K2.7 Code.
Kimi K2.7 Code vs Kimi K3
Moonshot AI · China | Moonshot AI · China · Updated June 2026
Quick verdict
Both are Moonshot AI models. Kimi K3 is the newer, generally stronger default; reach for Kimi K2.7 Code when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Kimi K2.7 Code and Kimi K3 are both Moonshot AI models, so the real question is not which lab to trust but which tier fits your workload and budget. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Price: Kimi K2.7 Code is about 3.2× cheaper on input ($0.95/$4 per 1M tokens vs $3/$15 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Kimi K3 holds 4× more — 1M (~1,573 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Kimi K3 is the newer model by about 45 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Kimi K2.7 Code
Kimi K3
Provider
Moonshot AI (China)
Moonshot AI (China)
Released
June 12, 2026
July 27, 2026
Context window
256K (~393 pages)
1M (~1,573 pages)
Price (in/out)
$0.95/$4 per 1M tokens
$3/$15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic software engineering
Kimi K2.7 Code
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it runs cheaper at $0.95/$4 per 1M tokens.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
Kimi K2.7 Code lists token-efficient reasoning (~30% fewer than K2.6) among its strengths; Kimi K3 does not.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 4× more than Kimi K2.7 Code's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Kimi K2.7 Code is comparatively weak here — only self-reported benchmarks; no SWE-Bench Verified
Lowest cost at scale
Kimi K2.7 Code
At $0.95/$4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Kimi K3
Its 1M window is about 4× larger than Kimi K2.7 Code's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Kimi K2.7 Code
At $0.95/$4 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic software engineering
→ Kimi K2.7 Code
It is specifically built for that.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
That is its strongest area.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs are real: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Because Kimi K2.7 Code and Kimi K3 come from the same lab (Moonshot AI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Kimi K3 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Kimi K3 and drop down only with a concrete reason.
Want both Kimi K2.7 Code and Kimi K3 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Kimi K2.7 Code leans toward long-horizon agentic software engineering while Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.7 Code or Kimi K3?
Kimi K2.7 Code is cheaper — $0.95/$4 per 1M tokens vs $3/$15 per 1M tokens, roughly 3.2× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Kimi K2.7 Code to Kimi K3?
Since both are Moonshot AI models, the newer one (Kimi K3) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Kimi K2.7 Code or Kimi K3?
Kimi K3 — released July 27, 2026, about 45 days after Kimi K2.7 Code.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.