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 K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, GLM 4.7 is the value pick.
GLM 4.7 (Z.ai) and Kimi K2.7 Code (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 K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: GLM 4.7 is about 1.6× cheaper on input ($0.6/$2.2 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Kimi K2.7 Code 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.
Recency: Kimi K2.7 Code is the newer model by about 6 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
Kimi K2.7 Code
Provider
Z.ai (China)
Moonshot AI (China)
Released
December 22, 2025
June 12, 2026
Context window
200K (~304 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
73.8%
Not published
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 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts Kimi K2.7 Code ($0.95/$4 per 1M tokens), and that gap compounds at volume.
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 K2.7 Code does not.
Long-horizon agentic software engineering: Kimi K2.7 Code — Its 256K window holds about 1.3× more than GLM 4.7's 200K in a single prompt.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it carries the larger 256K context.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it is the newer of the two.
Lowest cost at scale: GLM 4.7 — At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Kimi K2.7 Code — 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?
A cost-sensitive startup shipping high volume: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K2.7 Code — 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 long-horizon agentic software engineering: Kimi K2.7 Code — 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 K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 4.7 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. GLM 4.7 costs less per token; Kimi K2.7 Code 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 K2.7 Code for long-horizon agentic software engineering. 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 K2.7 Code better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or Kimi K2.7 Code?
GLM 4.7 is cheaper — $0.6/$2.2 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
Kimi K2.7 Code — 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 K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, Kimi K2.7 Code 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 K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 6 months after GLM 4.7.
GLM 4.7 vs Kimi K2.7 Code
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 K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, GLM 4.7 is the value pick.
GLM 4.7 (Z.ai) and Kimi K2.7 Code (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 K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GLM 4.7 is about 1.6× cheaper on input ($0.6/$2.2 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Kimi K2.7 Code 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.
▸Recency: Kimi K2.7 Code is the newer model by about 6 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
Kimi K2.7 Code
Provider
Z.ai (China)
Moonshot AI (China)
Released
December 22, 2025
June 12, 2026
Context window
200K (~304 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
73.8%
Not published
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
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts Kimi K2.7 Code ($0.95/$4 per 1M tokens), and that gap compounds at volume.
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 K2.7 Code does not.
Long-horizon agentic software engineering
Kimi K2.7 Code
Its 256K window holds about 1.3× more than GLM 4.7's 200K in a single prompt.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it carries the larger 256K context.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it is the newer of the two.
Lowest cost at scale
GLM 4.7
At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Kimi K2.7 Code
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?
A cost-sensitive startup shipping high volume
→ GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K2.7 Code
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 long-horizon agentic software engineering
→ Kimi K2.7 Code
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 K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 4.7 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. GLM 4.7 costs less per token; Kimi K2.7 Code 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 K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 4.7 and Kimi K2.7 Code 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 K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or Kimi K2.7 Code?
GLM 4.7 is cheaper — $0.6/$2.2 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
Kimi K2.7 Code — 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 K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, Kimi K2.7 Code 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 K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 6 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.