GLM 5 vs Kimi K2.7 Code
Z.ai · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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 (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 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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: nearly identical — $1/$3.2 per 1M tokens vs $0.95/$4 per 1M tokens. Cost will not be the deciding factor here.
- ▸Context window: Kimi K2.7 Code holds 1.3× more — 256K (~393 pages) vs 200K (~300 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 K2.7 Code is the newer model by about 4 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GLM 5 | Kimi K2.7 Code |
|---|---|---|
| Provider | Z.ai (China) | Moonshot AI (China) |
| Released | February 12, 2026 | June 12, 2026 |
| Context window | 200K (~300 pages) | 256K (~393 pages) |
| Price (in/out) | $1/$3.2 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 | 77.8% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; Kimi K2.7 Code does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; Kimi K2.7 Code does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Kimi K2.7 Code does not.
Long-horizon agentic software engineering
Kimi K2.7 Code
Its 256K window holds about 1.3× more than GLM 5's 200K in a single prompt.
Token-efficient reasoning (~30% fewer than K2.6)
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.
Open-weight 1T MoE, self-hostable
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 carries the larger 256K context.
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 K2.7 Code
Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 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, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K2.7 Code
Larger 256K window fits more in one prompt.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 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.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 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
GLM 5 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. Kimi K2.7 Code costs less per token; Kimi K2.7 Code holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, 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 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.
See pricingFrequently asked questions
Is GLM 5 or Kimi K2.7 Code better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows 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 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper — $1/$3.2 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
Kimi K2.7 Code — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 4 months after GLM 5.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.