Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). On a tight budget at scale, GLM 5.2 is the value pick.
GLM 5.2 (Z.ai) and Kimi K3 (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 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: GLM 5.2 is about 2.1× cheaper on input ($1.4/$4.4 per 1M tokens vs $3/$15 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: Kimi K3 is the newer model by about 44 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5.2
Kimi K3
Provider
Z.ai (China)
Moonshot AI (China)
Released
June 13, 2026
July 27, 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$3/$15 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 — Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Project-level software engineering: GLM 5.2 — An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap — and it runs cheaper at $1.4/$4.4 per 1M tokens.
Tool use across long-running tasks: GLM 5.2 — 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 — GLM 5.2 is comparatively weak here — new release with a limited third-party track record
1M-token context with native vision (text, image and video): Kimi K3 — GLM 5.2 is comparatively weak here — text-only — no native multimodal input
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: 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 is the newer of the two.
Lowest cost at scale: GLM 5.2 — At $1.4/$4.4 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: GLM 5.2 — At $1.4/$4.4 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 coding: GLM 5.2 — 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.
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 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
GLM 5.2 and Kimi K3 overlap enough that the right pick depends on your specific job. GLM 5.2 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable. 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 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, GLM 5.2 leans toward long-horizon agentic coding 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, GLM 5.2 or Kimi K3?
GLM 5.2 is cheaper — $1.4/$4.4 per 1M tokens vs $3/$15 per 1M tokens, roughly 2.1× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GLM 5.2 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Kimi K3 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 K3?
Kimi K3 — released July 27, 2026, about 44 days after GLM 5.2.
GLM 5.2 vs Kimi K3
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 K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). On a tight budget at scale, GLM 5.2 is the value pick.
GLM 5.2 (Z.ai) and Kimi K3 (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 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GLM 5.2 is about 2.1× cheaper on input ($1.4/$4.4 per 1M tokens vs $3/$15 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: Kimi K3 is the newer model by about 44 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5.2
Kimi K3
Provider
Z.ai (China)
Moonshot AI (China)
Released
June 13, 2026
July 27, 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$3/$15 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
Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Project-level software engineering
GLM 5.2
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap — and it runs cheaper at $1.4/$4.4 per 1M tokens.
Tool use across long-running tasks
GLM 5.2
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
GLM 5.2 is comparatively weak here — new release with a limited third-party track record
1M-token context with native vision (text, image and video)
Kimi K3
GLM 5.2 is comparatively weak here — text-only — no native multimodal input
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
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 is the newer of the two.
Lowest cost at scale
GLM 5.2
At $1.4/$4.4 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
→ GLM 5.2
At $1.4/$4.4 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 coding
→ GLM 5.2
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.
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 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
GLM 5.2 and Kimi K3 overlap enough that the right pick depends on your specific job. GLM 5.2 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5.2 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, GLM 5.2 leans toward long-horizon agentic coding 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, GLM 5.2 or Kimi K3?
GLM 5.2 is cheaper — $1.4/$4.4 per 1M tokens vs $3/$15 per 1M tokens, roughly 2.1× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GLM 5.2 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Kimi K3 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 K3?
Kimi K3 — released July 27, 2026, about 44 days after GLM 5.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.