{"id":1176,"date":"2026-06-19T16:39:10","date_gmt":"2026-06-19T16:39:10","guid":{"rendered":"https:\/\/convly.ai\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/"},"modified":"2026-06-19T16:39:37","modified_gmt":"2026-06-19T16:39:37","slug":"glm-5-2-vs-kimi-k2-7-for-coding-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/","title":{"rendered":"GLM 5.2 contre Kimi K2.7 Code : quel logiciel de programmation open source l'emporte ?"},"content":{"rendered":"<p>Two of China&#8217;s most aggressive AI labs shipped new open-weight coding models within a day of each other this month. Moonshot pushed Kimi K2.7 Code on June 12; Zhipu (Z.ai) answered with GLM 5.2 on June 13. Both are giant Mixture-of-Experts models, both carry permissive licenses, and both are pitched squarely at the same job: long-horizon, agentic coding that doesn&#8217;t cost Claude or GPT money.<\/p>\n<p>The twist is in how each lab handled benchmarks. Moonshot published a stack of first-party numbers for K2.7 Code on day one. Zhipu deployed GLM 5.2 to its Coding Plan tiers first with no benchmark table at all, then released a full benchmark set alongside the API and MIT open weights days later. So as of this writing, both models now have vendor-published coding scores \u2014 but neither has a deep bench of fully independent SWE-bench numbers yet, and Moonshot&#8217;s headline figures sit on proprietary in-house suites that practitioners have already started to question. Here&#8217;s how the two actually stack up, what we can verify, and what&#8217;s still a question mark.<\/p>\n<div class=\"convly-tldr\">\n<h3>Key takeaways<\/h3>\n<ul>\n<li><strong>Different shapes, same target.<\/strong> Kimi K2.7 Code is a 1T-param MoE with 32B active and 256K context; GLM 5.2 is ~744-753B total with ~40B active and a full 1M context.<\/li>\n<li><strong>Both now have vendor benchmarks.<\/strong> Moonshot reports +21.8% on its own Kimi Code Bench v2 (62.0 vs 50.9) plus ~30% fewer reasoning tokens. Zhipu later published GLM 5.2 scores too \u2014 SWE-bench Pro 62.1, Terminal-Bench 2.1 81.0, FrontierSWE 74.4 \u2014 beating GPT-5.5 on several long-horizon suites. Treat both vendors&#8217; numbers with caution until independent runs land.<\/li>\n<li><strong>Pricing favors Kimi per token, GLM per month.<\/strong> Kimi is metered at $0.95 in \/ $4.00 out per million; GLM is metered around $1.40 in \/ $4.40 out, or a flat GLM Coding Plan from $10\/mo (Lite).<\/li>\n<li><strong>Both are genuinely open and commercial-friendly.<\/strong> GLM 5.2 is MIT; Kimi is Modified-MIT (commercial use allowed, with an attribution clause only if you exceed 100M MAU or $20M\/month revenue).<\/li>\n<li><strong>GLM drops into Claude Code cleanly.<\/strong> Z.ai exposes an Anthropic-compatible endpoint, so existing Claude Code \/ Anthropic-SDK agents work with a base-URL and key swap.<\/li>\n<li><strong>Running the weights is not for laptops.<\/strong> 744B+ and 1T parameters mean multi-GPU servers or heavy quantization \u2014 most people will hit the cloud APIs first.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a35fc5bd87d2\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a35fc5bd87d2\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#The_30-second_version\" >The 30-second version<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Architecture_and_active_parameters\" >Architecture and active parameters<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Context_window_1M_vs_256K\" >Context window: 1M vs 256K<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Coding_benchmarks_and_the_honesty_gap\" >Coding benchmarks (and the honesty gap)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Pricing_and_value\" >Pricing and value<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#License_and_openness\" >License and openness<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Agentic_and_tool_use\" >Agentic and tool use<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#How_to_actually_run_each\" >How to actually run each<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#How_they_fit_next_to_DeepSeek_V4_and_Qwen_3x\" >How they fit next to DeepSeek V4 and Qwen 3.x<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Bottom_line\" >Bottom line<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/convly.ai\/fr\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/#Related_articles\" >Related articles<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_30-second_version\"><\/span>The 30-second version<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you want the longest context, the strongest published open-weight coding scores, MIT licensing, a flat monthly bill, and drop-in Claude Code compatibility, GLM 5.2 is the more complete package today. If you want the cheapest per-token rate, the best cache discount for token-heavy agent loops, and measured token-efficiency gains, Kimi K2.7 Code is the leaner buy. Both vendors&#8217; benchmarks are first-party for now, and a single-task head-to-head gave GLM a slight edge \u2014 so anyone crowning a definitive winner this week is leaning on vendor marketing, not independent data.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Architecture_and_active_parameters\"><\/span>Architecture and active parameters<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>These models are built on the same broad idea \u2014 a huge sparse MoE where only a fraction of parameters fire per token \u2014 but they tune it differently.<\/p>\n<p>Kimi K2.7 Code is the bigger model on paper: 1 trillion total parameters with 32B active, drawn from 384 experts (8 routed plus 1 shared per token). That sparse activation is why a trillion-parameter model can serve at a sane price. GLM 5.2 is smaller in total (Z.ai&#8217;s docs cite ~753B, while trackers like vLLM read ~744B) but activates slightly more per token at ~40B, and it leans on a longer context plus a dual thinking-effort system \u2014 a &#8220;High&#8221; mode for routine work and a &#8220;Max&#8221; mode for harder architecture and debugging.<\/p>\n<p>The practical read: Kimi&#8217;s larger expert pool may help with breadth of knowledge, while GLM&#8217;s higher active-parameter count and effort modes are aimed at depth on a single hard problem. The published benchmarks now tilt toward GLM on long-horizon engineering, but those are vendor-run, so treat the architectural story as supporting evidence rather than a verdict.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Context_window_1M_vs_256K\"><\/span>Context window: 1M vs 256K<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the clearest, most verifiable difference. GLM 5.2 ships a genuine 1,000,000-token context (the <code>glm-5.2[1m]<\/code> variant) with output capped around 128K-131K tokens. Kimi K2.7 Code runs a 256K context (262,144 tokens) and a much smaller default output ceiling of 32,768 tokens.<\/p>\n<p>For repo-scale agentic work \u2014 loading a large codebase, long plan-then-execute traces, multi-file refactors in one shot \u2014 GLM&#8217;s 1M window is a real advantage and matches what frontier open models like DeepSeek V4 and Qwen 3.6 Plus now offer. That said, 256K is still large, and in agentic loops most well-built tools retrieve and chunk context rather than stuffing the whole repo in. Bigger context helps; it isn&#8217;t automatically better code.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Coding_benchmarks_and_the_honesty_gap\"><\/span>Coding benchmarks (and the honesty gap)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here&#8217;s where you need to keep your skepticism switched on, because every headline number below is vendor-published.<\/p>\n<p>Moonshot reports that K2.7 Code scores 62.0 on its in-house Kimi Code Bench v2, up 21.8% from K2.6&#8217;s 50.9, alongside gains on Program Bench and MCP-focused agentic suites and a ~30% cut in reasoning-token usage. These are specific claims \u2014 but they run on Moonshot&#8217;s own proprietary benchmarks, and at least one outlet (VentureBeat) has reported practitioners saying the numbers don&#8217;t fully check out in real use. Independent SWE-bench Verified or SWE-bench Pro figures for K2.7 Code were not available at the time of writing.<\/p>\n<p>GLM 5.2 came out the other way around: it launched on Zhipu&#8217;s Coding Plan tiers with no benchmark table, then Z.ai published a full set alongside the API and open weights. Those scores are strong \u2014 SWE-bench Pro 62.1 (vs GPT-5.5&#8217;s 58.6 and GLM 5.1&#8217;s 58.4), Terminal-Bench 2.1 (Terminus-2) 81.0 (vs GPT-5.5&#8217;s 84.0), FrontierSWE 74.4% (vs GPT-5.5&#8217;s 72.6%), plus long-horizon wins on PostTrainBench (34.3 vs 28.4) and SWE-Marathon (13.0 vs 12.0). Several of those were run by outside evaluators (Proximal, the PostTrainBench team, Abundant AI), but they&#8217;re surfaced and curated by Z.ai, so treat them as vendor-published rather than fully independent. The takeaway: GLM 5.2 posts the stronger open-weight coding numbers on paper, while still trailing Claude Opus 4.8 on most of them.<\/p>\n<p>One closer-to-neutral data point exists. An independent-style head-to-head from Kilo gave GLM 5.2 a planning edge \u2014 9.0 vs Kimi&#8217;s 8.1 on a backend feature-flag service task, with GLM passing 15\/15 verification checks to Kimi&#8217;s 14\/15 and both producing near-identical working builds. That&#8217;s a useful signal, but it&#8217;s a single task by one evaluator, not a benchmark suite.<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Spec<\/th>\n<th>GLM 5.2 (Zhipu \/ Z.ai)<\/th>\n<th>Kimi K2.7 Code (Moonshot)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Released<\/td>\n<td>June 13, 2026<\/td>\n<td>June 12, 2026<\/td>\n<\/tr>\n<tr>\n<td>Total \/ active params<\/td>\n<td>~744-753B MoE \/ ~40B<\/td>\n<td>1T MoE \/ 32B (384 experts)<\/td>\n<\/tr>\n<tr>\n<td>Context window<\/td>\n<td>1,000,000 tokens<\/td>\n<td>256K (262,144) tokens<\/td>\n<\/tr>\n<tr>\n<td>Max output<\/td>\n<td>~128-131K tokens<\/td>\n<td>~32K (32,768) tokens<\/td>\n<\/tr>\n<tr>\n<td>Official coding benchmarks<\/td>\n<td>SWE-bench Pro 62.1; Terminal-Bench 2.1 81.0; FrontierSWE 74.4 (vendor-published, some 3rd-party-run)<\/td>\n<td>+21.8% on Kimi Code Bench v2 (62.0 vs 50.9, vendor-reported)<\/td>\n<\/tr>\n<tr>\n<td>Independent SWE-bench<\/td>\n<td>Not yet (public suites)<\/td>\n<td>Not yet<\/td>\n<\/tr>\n<tr>\n<td>API price (per 1M)<\/td>\n<td>~$1.40 in \/ ~$4.40 out; flat plan from $10\/mo<\/td>\n<td>$0.95 in \/ $4.00 out; $0.19 cached<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>MIT<\/td>\n<td>Modified MIT (commercial OK; attribution if >100M MAU or >$20M\/mo)<\/td>\n<\/tr>\n<tr>\n<td>Endpoint compatibility<\/td>\n<td>OpenAI- and Anthropic-compatible<\/td>\n<td>OpenAI-compatible (Moonshot \/ OpenRouter)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Pricing_and_value\"><\/span>Pricing and value<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The pricing models are structured differently, so the &#8220;cheaper&#8221; answer depends on usage.<\/p>\n<p>Kimi K2.7 Code is straightforward metered API: $0.95 per million input tokens, $4.00 per million output, and a notable $0.19 per million for cached input. That cache rate matters for agentic coding, where you re-send a lot of stable context every step. At those rates Kimi is dramatically cheaper than Western frontier models \u2014 by output price alone, more than ten times cheaper than premium-tier options.<\/p>\n<p>GLM 5.2 is metered around $1.40 input \/ $4.40 output per million (live across providers like FriendliAI, Novita, and Z.ai), but Zhipu also pushes the GLM Coding Plan, a flat subscription with Lite, Pro, Max, and Team tiers. Lite starts at $10\/month (roughly 400 prompts\/week), Pro at $30\/month, and Max at $80\/month \u2014 excellent value if you code in it daily and want predictable billing.<\/p>\n<p>If you&#8217;re a solo developer living in an agent all day, GLM&#8217;s flat plan can be the cheaper real-world choice. If you&#8217;re running variable or bursty workloads, or building a product on top, Kimi&#8217;s metered rate plus cheap caching is easier to model. For a broader cost picture across self-hostable options, our roundup of the <a href=\"\/best-local-llm-for-coding-2026\/\">best local LLM for coding in 2026<\/a> puts both in context.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"License_and_openness\"><\/span>License and openness<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Both are legitimately open-weight, which separates them from closed frontier labs \u2014 but the fine print differs.<\/p>\n<p>GLM 5.2 uses a plain MIT license: use it, modify it, ship it commercially, no strings. Kimi K2.7 Code uses a Modified-MIT license that also permits commercial use, but adds one condition: if your product crosses 100 million monthly active users or $20 million in monthly revenue, you must prominently display &#8220;Kimi K2.7 Code&#8221; in the UI. For virtually every team that&#8217;s a non-issue; for a hyperscaler it&#8217;s a real clause. So on pure permissiveness, GLM 5.2&#8217;s MIT edges it.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>GLM 5.2 strengths<\/h4>\n<ul>\n<li>Full 1M-token context for repo-scale work<\/li>\n<li>Strongest published open-weight coding scores of the two<\/li>\n<li>Unrestricted MIT license<\/li>\n<li>Drop-in Anthropic + OpenAI endpoint compatibility<\/li>\n<li>Flat-rate coding plan from $10\/mo<\/li>\n<li>High\/Max thinking-effort control<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>GLM 5.2 caveats<\/h4>\n<ul>\n<li>Benchmarks are vendor-published (some third-party-run); no broad independent SWE-bench suite yet<\/li>\n<li>Per-token API rate slightly higher than Kimi<\/li>\n<li>Smaller total parameter count<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Agentic_and_tool_use\"><\/span>Agentic and tool use<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Both models explicitly target long-horizon coding agents, not just snippet completion, and both expose strong tool-calling.<\/p>\n<p>GLM 5.2&#8217;s standout for agent builders is compatibility: because Z.ai serves an Anthropic-compatible endpoint (alongside an OpenAI-compatible one), you can point Claude Code or an Anthropic-SDK agent at it by swapping the base URL and key \u2014 no rewrite. It also plugs natively into Cline, Cursor, and 20-plus dev tools, and its published long-horizon scores (FrontierSWE, PostTrainBench, SWE-Marathon) are aimed precisely at multi-hour agent workloads. Kimi K2.7 Code leans into measured agentic efficiency: Moonshot&#8217;s reported ~30% reduction in reasoning tokens is aimed directly at the cost and latency of multi-step agent loops, and the model posts gains on MCP-oriented suites. If you&#8217;re choosing an agent harness around either, our guide to the <a href=\"\/best-ai-agent-frameworks-2026\/\">best AI agent frameworks in 2026<\/a> covers the orchestration layer.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_actually_run_each\"><\/span>How to actually run each<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There are two paths, and for most people the answer is the cloud.<\/p>\n<p><strong>Cloud API<\/strong> is the easy route. Kimi K2.7 Code is available through Moonshot&#8217;s API and aggregators like OpenRouter; GLM 5.2 is live on the GLM Coding Plan and via OpenAI-\/Anthropic-compatible endpoints (base URL <code>api.z.ai<\/code>). This is where nearly everyone should start.<\/p>\n<p><strong>Open weights<\/strong> are published \u2014 Kimi K2.7 Code is on Hugging Face with vLLM, SGLang, and KTransformers support, and GLM 5.2&#8217;s MIT weights are downloadable \u2014 but the hardware is serious. A 1T-parameter model (even at 32B active) or a ~750B model needs multi-GPU servers or aggressive GGUF quantization to run locally; these are not single-consumer-card models. If your goal is self-hosting smaller coders on commodity hardware, you&#8217;re better served by the <a href=\"\/best-local-llms-to-run-on-ollama-2026\/\">best local LLMs to run on Ollama in 2026<\/a> than by either of these heavyweights.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_they_fit_next_to_DeepSeek_V4_and_Qwen_3x\"><\/span>How they fit next to DeepSeek V4 and Qwen 3.x<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Neither model exists in a vacuum. DeepSeek V4-Pro (released April 2026) ships 1.6T params with a 1M context and an MIT license, and posts a verified 80.6% on SWE-bench Verified \u2014 currently the strongest open-weight number around. Qwen 3.6 Plus also offers a 1M context and a frontier-competitive 78.8% on SWE-bench Verified. In other words, GLM 5.2 and Kimi K2.7 Code are entering a crowded, fast-moving field where rivals already have published, partly independent benchmarks on the standard public suites. GLM 5.2&#8217;s vendor numbers are competitive, but the gold-standard SWE-bench Verified comparisons still belong to DeepSeek and Qwen for now. For a closer look at that pair, see our <a href=\"\/deepseek-v4-vs-qwen3-2026\/\">DeepSeek V4 vs Qwen3 comparison<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is GLM 5.2 or Kimi K2.7 Code better for coding?<\/h3>\n<p>There&#8217;s no fully independent answer yet, but on published numbers GLM 5.2 looks stronger for long-horizon coding: Zhipu&#8217;s benchmarks put it at SWE-bench Pro 62.1 and FrontierSWE 74.4, ahead of GPT-5.5 on several suites, with a 1M context and Claude Code compatibility. Kimi K2.7 Code is cheaper per token and reports +21.8% on its own coding benchmark. A single-task Kilo head-to-head gave GLM a slight planning edge (9.0 vs 8.1, 15\/15 vs 14\/15 checks). All headline scores are vendor-published, so wait for independent SWE-bench runs before treating any of it as final.<\/p>\n<h3>Does GLM 5.2 have published benchmarks?<\/h3>\n<p>Yes \u2014 but not at launch. Zhipu first deployed GLM 5.2 to its Coding Plan tiers on June 13, 2026 with no benchmark table, then published a full set alongside the API and MIT open weights days later: SWE-bench Pro 62.1, Terminal-Bench 2.1 81.0, FrontierSWE 74.4, PostTrainBench 34.3, and SWE-Marathon 13.0, beating GPT-5.5 on several long-horizon suites while trailing Claude Opus 4.8 on most. Several were run by third-party evaluators but curated by Z.ai, so they&#8217;re vendor-published, not fully independent.<\/p>\n<h3>Can I use GLM 5.2 with Claude Code?<\/h3>\n<p>Yes. Z.ai exposes an Anthropic-compatible endpoint (under <code>api.z.ai<\/code>, e.g. <code>https:\/\/api.z.ai\/api\/anthropic<\/code> or the coding endpoint), so you can point Claude Code or an Anthropic-SDK agent at GLM 5.2 by setting <code>ANTHROPIC_BASE_URL<\/code> and your Z.ai API key, then selecting the <code>glm-5.2<\/code> (or <code>glm-5.2[1m]<\/code>) model \u2014 no code rewrite required. Expect to raise the request timeout, since first-token latency on the 1M context runs longer than Claude&#8217;s default.<\/p>\n<h3>How much does each model cost?<\/h3>\n<p>Kimi K2.7 Code is metered at $0.95 per million input tokens, $4.00 output, and $0.19 cached. GLM 5.2 is metered around $1.40 input \/ $4.40 output per million, or sold through the GLM Coding Plan from $10\/month (Lite), with Pro at $30 and Max at $80.<\/p>\n<h3>Is Kimi K2.7 Code free for commercial use?<\/h3>\n<p>Effectively yes. It uses a Modified-MIT license that permits commercial use; the only added condition is that products exceeding 100 million monthly active users or $20 million in monthly revenue must display &#8220;Kimi K2.7 Code&#8221; in their UI. GLM 5.2&#8217;s plain MIT license has no such clause.<\/p>\n<h3>Can I run these models locally?<\/h3>\n<p>The weights are available \u2014 Kimi K2.7 Code on Hugging Face (vLLM\/SGLang\/KTransformers) and GLM 5.2 under MIT \u2014 but both are very large MoE models. Expect to need multi-GPU servers or heavy quantization; neither runs comfortably on a single consumer GPU.<\/p>\n<h3>Which has the larger context window?<\/h3>\n<p>GLM 5.2, by a wide margin: 1,000,000 tokens versus Kimi K2.7 Code&#8217;s 256K. That makes GLM the better fit for whole-repository context and very long agent traces, though strong agent tooling reduces how often you need the full window.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Bottom line<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>These are two excellent, genuinely open coding models that arrived a day apart, and the honest verdict is that it&#8217;s close \u2014 with GLM 5.2 now holding the on-paper edge. Both vendors have published coding benchmarks, and Zhipu&#8217;s are the stronger of the two (SWE-bench Pro 62.1, FrontierSWE 74.4, ahead of GPT-5.5 on several long-horizon suites), on top of a 1M context, an unrestricted MIT license, predictable flat-rate billing, and effortless Claude Code integration. Kimi K2.7 Code answers with the cheapest per-token price, a strong cache discount, token-efficient agent loops, and its own reported gains.<\/p>\n<p>If you&#8217;re shipping a product or running heavy variable workloads, start with Kimi&#8217;s metered API and its cache discount. If you live inside a coding agent all day and value a 1M window, top published scores, and drop-in Anthropic compatibility, GLM 5.2&#8217;s coding plan is hard to beat. And whichever you pick, remember that every headline number here is vendor-published \u2014 wait for independent SWE-bench Verified results before treating any marketing claim as settled fact. In a field where DeepSeek V4-Pro already posts a verified 80.6% on SWE-bench Verified, the bar for &#8220;best open coder&#8221; is measured by neutral evaluators, not asserted by the labs that built the models.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Related articles<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/deepseek-v4-vs-qwen3-2026\/\">DeepSeek V4 vs Qwen3.7 Max: 2026 Showdown<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/deepseek-vs-chatgpt-2026\/\">DeepSeek vs ChatGPT in 2026: Which AI Should You Actually Use?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/rx-7900-xtx-vs-rtx-4090-for-ai\/\">AMD RX 7900 XTX vs RTX 4090 for AI in 2026: Can ROCm Compete?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/rtx-5090-vs-rtx-5080-for-ai\/\">RTX 5090 vs RTX 5080 for AI in 2026: Which Blackwell Card to Buy?<\/a><\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Two Chinese labs shipped open-weight coding models a day apart. We fact-check the specs, context windows, pricing, licenses and benchmarks of GLM 5.2 and Kimi K2.7 Code to see which open coder actually fits your workflow.<\/p>","protected":false},"author":1,"featured_media":1181,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[246],"tags":[772,424,764,771,456,619,742,765],"class_list":["post-1176","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-ai-coding","tag-chinese-ai-models","tag-glm-5-2","tag-kimi-k2-7","tag-llm-comparison","tag-moonshot-ai","tag-open-weight-llm","tag-zhipu"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1176","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=1176"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1176\/revisions"}],"predecessor-version":[{"id":1188,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1176\/revisions\/1188"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/1181"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1176"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1176"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1176"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}