Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. On a tight budget at scale, Kimi K2.5 is the value pick.
GLM 5 (Z.ai) and Kimi K2.5 (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 K2.5 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open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Kimi K2.5 is about 1.7× cheaper on input ($0.6/$2.5 per 1M tokens vs $1/$3.2 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Kimi K2.5 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: GLM 5 is the newer model by about 15 days (released February 11, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5
Kimi K2.5
Provider
Z.ai (China)
Moonshot AI (China)
Released
February 11, 2026
January 27, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, 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 — Kimi K2.5 is comparatively weak here — its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol
Complex systems design and backend reasoning: GLM 5 — Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it is the newer of the two.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Kimi K2.5 does not.
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open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7 — and it runs cheaper at $0.6/$2.5 per 1M tokens.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 — 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 carries the larger 256K context.
Open-weight (Modified-MIT) — self-hostable at 256K context: Kimi K2.5 — Its 256K window holds about 1.3× more than GLM 5's 200K in a single prompt.
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.
Largest single-prompt input: Kimi K2.5 — 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 K2.5 — At $0.6/$2.5 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 K2.5 — 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 native multimodal reasoning and visual coding: Kimi K2.5 — 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 11, 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 K2.5: 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: 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.
The bottom line for this matchup
GLM 5 and Kimi K2.5 overlap enough that the right pick depends on your specific job. Kimi K2.5 costs less per token; Kimi K2.5 holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Kimi K2.5 for native multimodal reasoning and visual coding. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5 or Kimi K2.5 better for coding?
Public SWE-Bench figures are not available for Kimi K2.5, 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 K2.5 leans toward native multimodal reasoning and visual coding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Kimi K2.5?
Kimi K2.5 is cheaper — $1/$3.2 per 1M tokens vs $0.6/$2.5 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
Kimi K2.5 — 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 K2.5 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Kimi K2.5 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 K2.5?
GLM 5 — released February 11, 2026, about 15 days after Kimi K2.5.
GLM 5 vs Kimi K2.5
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 K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. On a tight budget at scale, Kimi K2.5 is the value pick.
GLM 5 (Z.ai) and Kimi K2.5 (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 K2.5 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Kimi K2.5 is about 1.7× cheaper on input ($0.6/$2.5 per 1M tokens vs $1/$3.2 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Kimi K2.5 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: GLM 5 is the newer model by about 15 days (released February 11, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5
Kimi K2.5
Provider
Z.ai (China)
Moonshot AI (China)
Released
February 11, 2026
January 27, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, 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
Kimi K2.5 is comparatively weak here — its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol
Complex systems design and backend reasoning
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it is the newer of the two.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Kimi K2.5 does not.
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
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 carries the larger 256K context.
Open-weight (Modified-MIT) — self-hostable at 256K context
Kimi K2.5
Its 256K window holds about 1.3× more than GLM 5's 200K in a single prompt.
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.
Largest single-prompt input
Kimi K2.5
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 K2.5
At $0.6/$2.5 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 K2.5
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 native multimodal reasoning and visual coding
→ Kimi K2.5
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 11, 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 K2.5: 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: 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.
The bottom line for this matchup
GLM 5 and Kimi K2.5 overlap enough that the right pick depends on your specific job. Kimi K2.5 costs less per token; Kimi K2.5 holds the larger context; and each leads in its own area — GLM 5 for agentic planning and long-horizon coding workflows, Kimi K2.5 for native multimodal reasoning and visual coding. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5 and Kimi K2.5 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 Kimi K2.5, 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 K2.5 leans toward native multimodal reasoning and visual coding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Kimi K2.5?
Kimi K2.5 is cheaper — $1/$3.2 per 1M tokens vs $0.6/$2.5 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
Kimi K2.5 — 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 K2.5 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Kimi K2.5 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 K2.5?
GLM 5 — released February 11, 2026, about 15 days 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.