Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents). On a tight budget at scale, Kimi K2.6 is the value pick.
GLM 5.1 (Z.ai) and Kimi K2.6 (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Kimi K2.6 is moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Kimi K2.6 is about 2.3× cheaper on input ($0.6/$2.5 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Kimi K2.6 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.
Specifications
Spec
GLM 5.1
Kimi K2.6
Provider
Z.ai (China)
Moonshot AI (China)
Released
April 7, 2026
April 20, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
Not published
80.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — A core design strength of GLM 5.1.
Sustained tool use across thousands of calls: GLM 5.1 — A core design strength of GLM 5.1.
Open-weight agentic coding and long-horizon tasks: Kimi K2.6 — A core design strength of Kimi K2.6.
Multi-agent swarms (scales to ~300 sub-agents): Kimi K2.6 — A core design strength of Kimi K2.6.
Self-hosting and data-residency control: Kimi K2.6 — A core design strength of Kimi K2.6.
Lowest cost at scale: Kimi K2.6 — 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 K2.6 — Its 256K window is about 1.3× larger, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Kimi K2.6 — At $0.6/$2.5 per 1M tokens it undercuts GLM 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K2.6 — Larger 256K window fits more in one prompt.
Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — It is specifically built for that.
Anyone whose priority is open-weight agentic coding and long-horizon tasks: Kimi K2.6 — That is its strongest area.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
Kimi K2.6: where it fits
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. Released April 20, 2026 by Moonshot AI, it is built for open-weight agentic coding and long-horizon tasks, multi-agent swarms (scales to ~300 sub-agents), self-hosting and data-residency control, and strong price-to-performance across many API providers.
Its trade-offs: 256K context trails the 1M Claude and Gemini flagships, weaker on single-turn vision and grounded multimodal tasks, and chinese-jurisdiction data and newer vendor track record. At $0.6 in / $2.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 5.1 and Kimi K2.6 overlap enough that the right pick depends on your specific job. Kimi K2.6 costs less per token; Kimi K2.6 holds the larger context; and each leads in its own area — GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), Kimi K2.6 for open-weight agentic coding and long-horizon tasks. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5.1 or Kimi K2.6 better for coding?
Public SWE-Bench figures are not available for GLM 5.1, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) while Kimi K2.6 leans toward open-weight agentic coding and long-horizon tasks, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Kimi K2.6?
Kimi K2.6 is cheaper — $1.4/$4.4 per 1M tokens vs $0.6/$2.5 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
Kimi K2.6 — 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 5.1 and Kimi K2.6 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Kimi K2.6 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 5.1 or Kimi K2.6?
Kimi K2.6 — released April 20, 2026, about 13 days after GLM 5.1.
GLM 5.1 vs Kimi K2.6
Z.ai · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents). On a tight budget at scale, Kimi K2.6 is the value pick.
GLM 5.1 (Z.ai) and Kimi K2.6 (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Kimi K2.6 is moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Kimi K2.6 is about 2.3× cheaper on input ($0.6/$2.5 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Kimi K2.6 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.
Side-by-side specs
Spec
GLM 5.1
Kimi K2.6
Provider
Z.ai (China)
Moonshot AI (China)
Released
April 7, 2026
April 20, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
Not published
80.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
A core design strength of GLM 5.1.
Sustained tool use across thousands of calls
GLM 5.1
A core design strength of GLM 5.1.
Open-weight agentic coding and long-horizon tasks
Kimi K2.6
A core design strength of Kimi K2.6.
Multi-agent swarms (scales to ~300 sub-agents)
Kimi K2.6
A core design strength of Kimi K2.6.
Self-hosting and data-residency control
Kimi K2.6
A core design strength of Kimi K2.6.
Lowest cost at scale
Kimi K2.6
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 K2.6
Its 256K window is about 1.3× larger, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Kimi K2.6
At $0.6/$2.5 per 1M tokens it undercuts GLM 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K2.6
Larger 256K window fits more in one prompt.
Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
It is specifically built for that.
Anyone whose priority is open-weight agentic coding and long-horizon tasks
→ Kimi K2.6
That is its strongest area.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
Kimi K2.6: where it fits
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. Released April 20, 2026 by Moonshot AI, it is built for open-weight agentic coding and long-horizon tasks, multi-agent swarms (scales to ~300 sub-agents), self-hosting and data-residency control, and strong price-to-performance across many API providers.
Its trade-offs: 256K context trails the 1M Claude and Gemini flagships, weaker on single-turn vision and grounded multimodal tasks, and chinese-jurisdiction data and newer vendor track record. At $0.6 in / $2.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 5.1 and Kimi K2.6 overlap enough that the right pick depends on your specific job. Kimi K2.6 costs less per token; Kimi K2.6 holds the larger context; and each leads in its own area — GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), Kimi K2.6 for open-weight agentic coding and long-horizon tasks. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5.1 and Kimi K2.6 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 GLM 5.1, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) while Kimi K2.6 leans toward open-weight agentic coding and long-horizon tasks, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.1 or Kimi K2.6?
Kimi K2.6 is cheaper — $1.4/$4.4 per 1M tokens vs $0.6/$2.5 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
Kimi K2.6 — 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 5.1 and Kimi K2.6 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, Kimi K2.6 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 5.1 or Kimi K2.6?
Kimi K2.6 — released April 20, 2026, about 13 days after GLM 5.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.