GLM-5.2 wins on coding (90 vs 88) and writing quality and context window (1M vs 256K). Kimi K2.7 Code wins on price ($0.95 vs $1.4/1M input). For most workflows, GLM-5.2 is the stronger default — top open-weights coder — beats gpt-5.5 at a sixth of the cost.
The safest GLM-5.2 vs Kimi K2.7 Code default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GLM-5.2 is the strongest answer here for GLM-5.2 vs Kimi K2.7 Code — pick it when quality of output matters more than the $1.40/1M/1M input you pay for it.
Kimi K2.7 Code handles the same job for about 15% less per token. Start here and only move up if the output is not good enough.
Kimi K2.7 Code is the fastest of these for GLM-5.2 vs Kimi K2.7 Code — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GLM-5.2 leads on coding with a score of 90 vs 88 for Kimi K2.7 Code.
GLM-5.2 has the larger context window: 1M vs 256K for Kimi K2.7 Code.
Kimi K2.7 Code is cheaper at $0.95/1M input tokens vs $1.4/1M for GLM-5.2.
Choose GLM-5.2 for budget agentic coding at scale. Its coding and budget scores are what carry the recommendation here.
Choose Kimi K2.7 Code when your work is mostly cost-efficient agentic coding — that is the workload it was tuned for.
Kimi K2.7 Code is the more cost-efficient option at $0.95/1M input — GLM-5.2 costs 1x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the GLM-5.2 vs Kimi K2.7 Code answer changes when cost, speed, or long-document depth leads the decision.
Z.ai / Budget / Aug 6, 2026
Top open-weights coder — beats GPT-5.5 at a sixth of the cost.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need frontier reasoning ceiling or launch-day verified benchmarks.
Where the GLM-5.2 vs Kimi K2.7 Code recommendation shifts once you weigh price or latency differently.
Top open-weights coder — beats GPT-5.5 at a sixth of the cost.
Value coding specialist — 1T MoE agentic coder at budget prices.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GLM-5.2Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 90 | 78 | 80 |
| Kimi K2.7 CodeMoonshot | $0.95/1M | $4.00/1M | $18 | 256k tokens | Fast | 88 | 68 | 70 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GLM-5.2 vs Kimi K2.7 Code, what it is genuinely good at, and where we would steer you away from it.
The default answer for GLM-5.2 vs Kimi K2.7 Code — 90/100 on the coding axis, and the model we would start with unless the price below rules it out.
Z.ai's MIT-licensed open-weight flagship — the top open-weights coding model of mid-2026, beating GPT-5.5 on agentic coding benchmarks at roughly a sixth of the cost.
You need frontier reasoning ceiling or launch-day verified benchmarks.
The open-weights coding value king of mid-2026 — GPT-5.5-beating agentic coding at a fraction of the price, with an MIT license. DeepSeek V4-Flash undercuts it on price; GLM-5.2 answers with higher ceiling and two reasoning-effort modes.
Full pricing, benchmark table and release notes on the GLM-5.2 page.
The cost-conscious pick for GLM-5.2 vs Kimi K2.7 Code, about 15% less per token than GLM-5.2 than the top choice while holding 88/100 on coding.
An open-weight 1T-parameter MoE (32B active) coding specialist tuned for long-horizon agentic software engineering with markedly better token efficiency than its predecessor.
Your agent needs big-repo context (256K cap) or frontier general reasoning.
The value pick among coding specialists. K3 superseded it at the frontier a month later, but for pure coding-agent volume at a quarter of K3's input price, K2.7 Code remains the smarter buy.
Full pricing, benchmark table and release notes on the Kimi K2.7 Code page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GLM-5.2 wins on more of the categories we score — coding, budget, reasoning — so it is the better default of the two. Kimi K2.7 Code is the better pick when your work is mostly cost-efficient agentic coding. Neither is universally "better": GLM-5.2 is aimed at budget agentic coding at scale, Kimi K2.7 Code at cost-efficient agentic coding.
Kimi K2.7 Code is cheaper at $0.95/1M input and $4/1M output. GLM-5.2 costs $1.4/1M input and $4.4/1M output.
GLM-5.2 has the larger context window at 1M tokens vs Kimi K2.7 Code's 256K. For large document analysis, GLM-5.2 is the stronger pick.
GLM-5.2 is better for coding with a score of 90 vs Kimi K2.7 Code's 88 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Kimi K2.7 Code is faster with a fast speed rating (score: 4) vs GLM-5.2's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the GLM-5.2 latency penalty is usually invisible.
Clear gap to the closed frontier: AA Index 51 vs Claude Opus 5 (61) and GPT-5.6 Sol (59). Z.ai published no benchmark numbers at launch — buyers depended on third-party evals that arrived weeks later. Avoid it if you need frontier reasoning ceiling or launch-day verified benchmarks. That is the main case for looking at Kimi K2.7 Code instead.
256K context is a quarter of what 2026 rivals offer for large-repo agent work. Headline gains are on Moonshot's own in-house benchmark; general reasoning lags the Western frontier. Avoid it if your agent needs big-repo context (256K cap) or frontier general reasoning. Against GLM-5.2 specifically, the gap shows up most on coding (90 vs 88).
Take a moderate workload of 10M input and 2M output tokens a month. GLM-5.2 runs $22.80 (at $1.4/1M in and $4.4/1M out); Kimi K2.7 Code runs $17.50 (at $0.95/1M in and $4/1M out). That is a $5.30/month difference — Kimi K2.7 Code is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GLM-5.2 for budget agentic coding at scale, with Kimi K2.7 Code handling cost-efficient agentic coding. Routing high-volume, low-stakes calls to Kimi K2.7 Code at $0.95/1M and reserving GLM-5.2 for the hard cases is usually the cheapest arrangement that does not cost you quality.