Both are Moonshot AI models. Kimi K2.7 Code is the newer, generally stronger default; reach for Kimi K2.5 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Kimi K2.5 and Kimi K2.7 Code are both Moonshot AI models, so the real question is not which lab to trust but which tier fits your workload and budget. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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. 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 K2.5 is about 1.6× cheaper on input ($0.6/$2.5 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Kimi K2.7 Code is the newer model by about 5 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Kimi K2.5
Kimi K2.7 Code
Provider
Moonshot AI (China)
Moonshot AI (China)
Released
January 27, 2026
June 12, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.5 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native multimodal reasoning and visual coding: Kimi K2.5 — Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7 — and it runs cheaper at $0.6/$2.5 per 1M tokens.
Agentic tool-calling and self-directed multi-step work: Kimi K2.5 — Kimi K2.5 lists agentic tool-calling and self-directed multi-step work among its strengths; Kimi K2.7 Code does not.
Open-weight (Modified-MIT) — self-hostable at 256K context: Kimi K2.5 — Kimi K2.5 lists open-weight (Modified-MIT) — self-hostable at 256K context among its strengths; Kimi K2.7 Code does not.
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 is the newer of the two.
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 K2.5 does not.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — Kimi K2.7 Code lists open-weight 1T MoE, self-hostable among its strengths; Kimi K2.5 does not.
Lowest cost at scale: Kimi K2.5 — At $0.6/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume: Kimi K2.5 — At $0.6/$2.5 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is native multimodal reasoning and visual coding: Kimi K2.5 — It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering: Kimi K2.7 Code — That is its strongest area.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.6 in / $2.5 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
Because Kimi K2.5 and Kimi K2.7 Code come from the same lab (Moonshot AI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Kimi K2.7 Code 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 K2.7 Code and drop down only with a concrete reason.
Frequently asked questions
Is Kimi K2.5 or Kimi K2.7 Code 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 K2.5 leans toward native multimodal reasoning and visual coding while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.5 or Kimi K2.7 Code?
Kimi K2.5 is cheaper — $0.6/$2.5 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Kimi K2.5 to Kimi K2.7 Code?
Since both are Moonshot AI models, the newer one (Kimi K2.7 Code) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Kimi K2.5 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 5 months after Kimi K2.5.
Kimi K2.5 vs Kimi K2.7 Code
Moonshot AI · China | Moonshot AI · China · Updated June 2026
Quick verdict
Both are Moonshot AI models. Kimi K2.7 Code is the newer, generally stronger default; reach for Kimi K2.5 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Kimi K2.5 and Kimi K2.7 Code are both Moonshot AI models, so the real question is not which lab to trust but which tier fits your workload and budget. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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. 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 K2.5 is about 1.6× cheaper on input ($0.6/$2.5 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Kimi K2.7 Code is the newer model by about 5 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Kimi K2.5
Kimi K2.7 Code
Provider
Moonshot AI (China)
Moonshot AI (China)
Released
January 27, 2026
June 12, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.5 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native multimodal reasoning and visual coding
Kimi K2.5
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7 — and it runs cheaper at $0.6/$2.5 per 1M tokens.
Agentic tool-calling and self-directed multi-step work
Kimi K2.5
Kimi K2.5 lists agentic tool-calling and self-directed multi-step work among its strengths; Kimi K2.7 Code does not.
Open-weight (Modified-MIT) — self-hostable at 256K context
Kimi K2.5
Kimi K2.5 lists open-weight (Modified-MIT) — self-hostable at 256K context among its strengths; Kimi K2.7 Code does not.
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 is the newer of the two.
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 K2.5 does not.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
Kimi K2.7 Code lists open-weight 1T MoE, self-hostable among its strengths; Kimi K2.5 does not.
Lowest cost at scale
Kimi K2.5
At $0.6/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Kimi K2.5
At $0.6/$2.5 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is native multimodal reasoning and visual coding
→ Kimi K2.5
It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering
→ Kimi K2.7 Code
That is its strongest area.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.6 in / $2.5 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
Because Kimi K2.5 and Kimi K2.7 Code come from the same lab (Moonshot AI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Kimi K2.7 Code 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 K2.7 Code and drop down only with a concrete reason.
Want both Kimi K2.5 and Kimi K2.7 Code 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 K2.5 leans toward native multimodal reasoning and visual coding while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.5 or Kimi K2.7 Code?
Kimi K2.5 is cheaper — $0.6/$2.5 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Kimi K2.5 to Kimi K2.7 Code?
Since both are Moonshot AI models, the newer one (Kimi K2.7 Code) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Kimi K2.5 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 5 months after Kimi K2.5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.