Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents).
GLM 4.7 (Z.ai) and Kimi K2.6 (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Context window: Kimi K2.6 holds 1.3× more — 256K (~393 pages) vs 200K (~304 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Coding: Kimi K2.6 leads SWE-Bench Verified by 6.4 points (73.8% vs 80.2%) — a real edge on hard, real-world software tasks.
Recency: Kimi K2.6 is the newer model by about 4 months (released April 20, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
Kimi K2.6
Provider
Z.ai (China)
Moonshot AI (China)
Released
December 22, 2025
April 20, 2026
Context window
200K (~304 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
80.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; Kimi K2.6 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — GLM 4.7 lists strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch among its strengths; Kimi K2.6 does not.
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; Kimi K2.6 does not.
Open-weight agentic coding and long-horizon tasks: Kimi K2.6 — It scores 80.2% on SWE-Bench Verified against GLM 4.7's 73.8% — a 6.4-point edge on real repository work.
Multi-agent swarms (scales to ~300 sub-agents): Kimi K2.6 — Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host — and it leads SWE-Bench Verified 80.2% to 73.8%.
Self-hosting and data-residency control: Kimi K2.6 — GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
Largest single-prompt input: Kimi K2.6 — Its 256K window is about 1.3× larger than GLM 4.7's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Kimi K2.6 — Larger 256K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — 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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget 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 4.7 and Kimi K2.6 overlap enough that the right pick depends on your specific job. Kimi K2.6 holds the larger context; and each leads in its own area — GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 or Kimi K2.6 better for coding?
On SWE-Bench Verified, GLM 4.7 scores 73.8% and Kimi K2.6 scores 80.2% — Kimi K2.6 has the measurable edge.
Which is cheaper, GLM 4.7 or Kimi K2.6?
They are priced almost identically, so cost will not decide between them.
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 4.7 and Kimi K2.6 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or Kimi K2.6?
Kimi K2.6 — released April 20, 2026, about 4 months after GLM 4.7.
GLM 4.7 vs Kimi K2.6
Z.ai · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents).
GLM 4.7 (Z.ai) and Kimi K2.6 (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Kimi K2.6 holds 1.3× more — 256K (~393 pages) vs 200K (~304 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Coding: Kimi K2.6 leads SWE-Bench Verified by 6.4 points (73.8% vs 80.2%) — a real edge on hard, real-world software tasks.
▸Recency: Kimi K2.6 is the newer model by about 4 months (released April 20, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
Kimi K2.6
Provider
Z.ai (China)
Moonshot AI (China)
Released
December 22, 2025
April 20, 2026
Context window
200K (~304 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
80.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; Kimi K2.6 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
GLM 4.7 lists strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch among its strengths; Kimi K2.6 does not.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; Kimi K2.6 does not.
Open-weight agentic coding and long-horizon tasks
Kimi K2.6
It scores 80.2% on SWE-Bench Verified against GLM 4.7's 73.8% — a 6.4-point edge on real repository work.
Multi-agent swarms (scales to ~300 sub-agents)
Kimi K2.6
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host — and it leads SWE-Bench Verified 80.2% to 73.8%.
Self-hosting and data-residency control
Kimi K2.6
GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
Largest single-prompt input
Kimi K2.6
Its 256K window is about 1.3× larger than GLM 4.7's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Kimi K2.6
Larger 256K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget 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 4.7 and Kimi K2.6 overlap enough that the right pick depends on your specific job. Kimi K2.6 holds the larger context; and each leads in its own area — GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 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.
On SWE-Bench Verified, GLM 4.7 scores 73.8% and Kimi K2.6 scores 80.2% — Kimi K2.6 has the measurable edge.
Which is cheaper, GLM 4.7 or Kimi K2.6?
They are priced almost identically, so cost will not decide between them.
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 4.7 and Kimi K2.6 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or Kimi K2.6?
Kimi K2.6 — released April 20, 2026, about 4 months after GLM 4.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.