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 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 5.1 is the value pick.
GLM 5.1 (Z.ai) and Kimi K3 (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 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 5.1 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: Kimi K3 holds 5.2× more — 1M (~1,573 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 K3 is the newer model by about 4 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5.1
Kimi K3
Provider
Z.ai (China)
Moonshot AI (China)
Released
April 7, 2026
July 27, 2026
Context window
200K (~300 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 autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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 — and it runs cheaper at $1.4/$4.4 per 1M tokens.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Sustained tool use across thousands of calls: GLM 5.1 — GLM 5.1 lists sustained tool use across thousands of calls among its strengths; Kimi K3 does not.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: 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 carries the larger 1M context.
1M-token context with native vision (text, image and video): Kimi K3 — Its 1M window holds about 5.2× more than GLM 5.1's 200K in a single prompt.
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 5.1 — At $1.4/$4.4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Kimi K3 — Its 1M window is about 5.2× larger than GLM 5.1's 200K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 5.1 — 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 autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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 5.1: 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 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 5.1 and Kimi K3 overlap enough that the right pick depends on your specific job. GLM 5.1 costs less per token; Kimi K3 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 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 5.1 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 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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 5.1 or Kimi K3?
GLM 5.1 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?
Kimi K3 — 1M vs 200K, about 5.2× 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 K3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or Kimi K3?
Kimi K3 — released July 27, 2026, about 4 months after GLM 5.1.
GLM 5.1 vs Kimi K3
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 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 5.1 is the value pick.
GLM 5.1 (Z.ai) and Kimi K3 (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 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 5.1 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: Kimi K3 holds 5.2× more — 1M (~1,573 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 K3 is the newer model by about 4 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5.1
Kimi K3
Provider
Z.ai (China)
Moonshot AI (China)
Released
April 7, 2026
July 27, 2026
Context window
200K (~300 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 autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
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 — and it runs cheaper at $1.4/$4.4 per 1M tokens.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Sustained tool use across thousands of calls
GLM 5.1
GLM 5.1 lists sustained tool use across thousands of calls among its strengths; Kimi K3 does not.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
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 carries the larger 1M context.
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 5.2× more than GLM 5.1's 200K in a single prompt.
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 5.1
At $1.4/$4.4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Kimi K3
Its 1M window is about 5.2× larger than GLM 5.1's 200K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 5.1
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 autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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 5.1: 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 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 5.1 and Kimi K3 overlap enough that the right pick depends on your specific job. GLM 5.1 costs less per token; Kimi K3 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 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 5.1 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 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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 5.1 or Kimi K3?
GLM 5.1 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?
Kimi K3 — 1M vs 200K, about 5.2× 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 K3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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 5.1 or Kimi K3?
Kimi K3 — released July 27, 2026, about 4 months 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.