Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, Kimi K2.7 Code is the value pick.
GLM 5.2 (Z.ai) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Kimi K2.7 Code is about 1.5× cheaper on input ($0.95/$4 per 1M tokens vs $1.4/$4.4 per 1M tokens) — modest, but it adds up at steady volume.
Context window: GLM 5.2 holds 3.8× more — 1M (~1,500 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.
Specifications
Spec
GLM 5.2
Kimi K2.7 Code
Provider
Z.ai (China)
Moonshot AI (China)
Released
June 13, 2026
June 12, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, 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 coding: GLM 5.2 — A core design strength of GLM 5.2.
Project-level software engineering: GLM 5.2 — A core design strength of GLM 5.2.
Tool use across long-running tasks: GLM 5.2 — A core design strength of GLM 5.2.
Long-horizon agentic software engineering: Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
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: GLM 5.2 — Its 1M window is about 3.8× larger, fitting roughly 1,500 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 GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 5.2 — Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic coding: GLM 5.2 — It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering: Kimi K2.7 Code — That is its strongest area.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 13, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs are real: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid 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
GLM 5.2 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. Kimi K2.7 Code costs less per token; GLM 5.2 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5.2 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, GLM 5.2 leans toward long-horizon agentic 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, GLM 5.2 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper — $1.4/$4.4 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.5× apart on input.
Which has the bigger context window?
GLM 5.2 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.2 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Kimi K2.7 Code and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or Kimi K2.7 Code?
GLM 5.2 — released June 13, 2026, about 1 days after Kimi K2.7 Code.
GLM 5.2 vs Kimi K2.7 Code
Z.ai · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, Kimi K2.7 Code is the value pick.
GLM 5.2 (Z.ai) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Kimi K2.7 Code is about 1.5× cheaper on input ($0.95/$4 per 1M tokens vs $1.4/$4.4 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: GLM 5.2 holds 3.8× more — 1M (~1,500 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.
Side-by-side specs
Spec
GLM 5.2
Kimi K2.7 Code
Provider
Z.ai (China)
Moonshot AI (China)
Released
June 13, 2026
June 12, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, 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 coding
GLM 5.2
A core design strength of GLM 5.2.
Project-level software engineering
GLM 5.2
A core design strength of GLM 5.2.
Tool use across long-running tasks
GLM 5.2
A core design strength of GLM 5.2.
Long-horizon agentic software engineering
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
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
GLM 5.2
Its 1M window is about 3.8× larger, fitting roughly 1,500 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 GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 5.2
Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering
→ Kimi K2.7 Code
That is its strongest area.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 13, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs are real: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid 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
GLM 5.2 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. Kimi K2.7 Code costs less per token; GLM 5.2 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5.2 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, GLM 5.2 leans toward long-horizon agentic 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, GLM 5.2 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper — $1.4/$4.4 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.5× apart on input.
Which has the bigger context window?
GLM 5.2 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.2 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Kimi K2.7 Code and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or Kimi K2.7 Code?
GLM 5.2 — released June 13, 2026, about 1 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.