Quick Verdict
GLM-5.2, released by Z.ai (formerly Zhipu AI) on June 16, 2026, is the most credible open-weight challenger to closed frontier models we've seen for coding-heavy workloads. It ships under an MIT license, supports a 1,048,576-token context window, and — according to Z.ai's own launch benchmarks plus independent tracking on OpenRouter — scores higher than GPT-5.5 on SWE-bench Pro while costing a fraction as much per token through the official API.
This isn't a hands-on usage review — GLM-5.2 only became publicly available a week before this article was written. What follows is based on Z.ai's published specs, OpenRouter's listed pricing and benchmark data, and independent reporting from outlets like VentureBeat and Artificial Analysis. We'll update this page with first-hand testing notes once we've put the model through real workloads.
Our early take: 8.6/10 — exceptional price-to-performance for an open-weight model, with the usual caveats that come with a brand-new release: a less mature tooling ecosystem than OpenAI or Anthropic, and a few specs (like exact parameter count) that Z.ai hasn't officially confirmed.
What Is GLM-5.2?
GLM-5.2 is a large-scale reasoning model built by Z.ai, the company formerly known as Zhipu AI. It's part of the GLM (General Language Model) family, and this release is positioned specifically around long-horizon agentic work — project-level software engineering, multi-step automation, and tasks that require a model to hold context and follow conventions across an entire workflow, not just answer a single prompt.
The headline differentiator is that GLM-5.2 ships with open weights under the MIT license, available on Hugging Face, alongside a standard pay-per-token API from Z.ai and third-party routing through aggregators like OpenRouter. Z.ai has not published an exact parameter count; third-party estimates place it somewhere in the 740–755 billion range as a Mixture-of-Experts (MoE) architecture, but treat that figure as an approximation rather than a confirmed spec.
GLM-5.2 Pricing: API Costs and Coding Plans
GLM-5.2 is priced per token through the API, with a separate flat-rate subscription option (the "GLM Coding Plan") aimed at developers who'd rather pay a fixed monthly fee than meter usage.
| Access method | Price | Notes |
|---|---|---|
| Z.ai API (direct) | $1.40 / 1M input tokens, $4.40 / 1M output tokens | Listed rate as of launch; confirmed via OpenRouter's model page |
| OpenRouter (aggregator) | Same listed rate, routed across 8 providers | Adds redundancy/uptime; effective price can be lower with prompt caching |
| GLM Coding Plan — Lite | ~$3–6/month | Publicly listed rate for light, individual use; verify current pricing at z.ai/subscribe |
| GLM Coding Plan — Pro | ~$15–19/month | Mid-tier for regular coding workloads |
| GLM Coding Plan — Max | ~$80/month | Heaviest individual tier; a Team plan also exists for organizations |
For context, GPT-5.5's API pricing is reported to be roughly six times more expensive per token than GLM-5.2's direct API rate on equivalent coding workloads — which is the core of Z.ai's pitch: comparable or better coding performance at a meaningfully lower cost.
Try GLM-5.2's open weights directly
View GLM-5.2 on Hugging Face →GLM-5.2 vs GPT-5.5: How It Stacks Up on Benchmarks
The benchmark numbers below come from Z.ai's own launch announcement, cross-referenced against independent tracking from Artificial Analysis (the benchmark source OpenRouter cites directly on its GLM-5.2 listing) and reporting from VentureBeat. As with any vendor-published benchmark, treat these as a starting point for evaluation, not a substitute for testing on your own workload.
| Benchmark | GLM-5.2 | GPT-5.5 | GLM-5.1 (predecessor) |
|---|---|---|---|
| SWE-bench Pro (real-world coding) | 62.1 | 58.6 | 58.4 |
| Terminal-Bench 2.1 (agentic terminal tasks) | ~81.0 (82.7 with best harness) | Not independently confirmed at time of writing | — |
| Context window | 1,048,576 tokens (~1M) | Varies by tier | 200,000 tokens |
| Max output tokens | 262,144 | Varies by tier | — |
| License | MIT (open weights) | Closed/proprietary | MIT (open weights) |
The SWE-bench Pro result is the figure getting the most attention, since it's a benchmark built around realistic, multi-file software engineering tasks rather than isolated coding puzzles. GLM-5.2 also reportedly posted strong results on AIME 2026 (math reasoning) and GPQA Diamond (graduate-level science Q&A), though those figures are less consistently corroborated across independent sources as of this writing, so we're not citing exact numbers here.
Key Features
- 1M-token context window: At 1,048,576 tokens, GLM-5.2 can hold an entire mid-size codebase, a long document set, or an extended multi-turn agent session in context at once — roughly 5x the context of its GLM-5.1 predecessor.
- Two reasoning effort levels: GLM-5.2 supports a
highand anxhighreasoning mode (the latter mapping to maximum reasoning depth). Higher effort trades speed for more thorough planning — useful for complex refactors, less necessary for quick autocomplete-style requests. - Open weights, MIT license: Unlike GPT-5.5 or Claude, you can download GLM-5.2's weights and self-host it, which matters for teams with data residency or compliance constraints.
- Built for long-horizon agent workflows: Z.ai designed this release specifically around maintaining consistency across a full development workflow — from requirements to multi-platform deployment — in a single extended task, rather than optimizing purely for single-shot answers.
How to Access GLM-5.2
GLM-5.2 is available through several channels depending on how much control you want over hosting and cost:
- Hugging Face: Download the open weights directly and self-host (
zai-org/GLM-5.2). - Z.ai API: Pay-per-token access at the rates above; check z.ai for current pricing, since rates on new model releases tend to shift in the first few months.
- OpenRouter: A unified API endpoint that routes requests across multiple GLM-5.2 hosting providers, useful if you want a single integration point across several models.
- Third-party coding environments: Reported to be integrated into more than 20 third-party coding tools and IDE plugins shortly after launch.
Compare pricing and providers for GLM-5.2 in one place
See GLM-5.2 on OpenRouter →Pros and Cons
| Pros | Cons |
|---|---|
| Strong SWE-bench Pro score relative to GPT-5.5 on Z.ai's own benchmarks | Released June 16, 2026 — no long-term track record yet, no independent hands-on review from us at time of writing |
| Open weights under MIT license — can be self-hosted | Exact parameter count not officially confirmed by Z.ai |
| 1M-token context window, useful for large codebases | Using the hosted Z.ai API (rather than self-hosting) routes data through a China-based provider, which may matter for data-residency-sensitive teams |
| Meaningfully cheaper per-token than GPT-5.5 on comparable coding tasks | Tooling/IDE ecosystem is newer and less mature than OpenAI's or Anthropic's |
| Multiple access paths (self-host, direct API, OpenRouter, subscription plans) | Some headline benchmark figures circulating online vary slightly between sources |
Who Should Use GLM-5.2?
GLM-5.2 is worth evaluating if you are:
- A developer or team doing heavy agentic coding work — multi-file refactors, long-running automation, or project-level engineering tasks where the 1M-token context window pays off.
- Cost-conscious about API spend — if you're running high-volume coding workloads through GPT-5.5 or a similarly priced closed model, GLM-5.2's per-token cost is worth benchmarking against your actual usage.
- Working under data residency or self-hosting requirements — the open MIT-licensed weights mean you can run it on your own infrastructure instead of relying on a hosted API.
GLM-5.2 is probably not the right fit yet if you:
- Need a deeply mature plugin/IDE ecosystem today — OpenAI and Anthropic's tooling has a multi-year head start.
- Have strict requirements against routing any data through China-based infrastructure and aren't able to self-host the open weights.
- Want a model with a longer public track record before committing production workloads to it — GLM-5.2 is, at the time of writing, about a week old.
Frequently Asked Questions
Is GLM-5.2 free?
The open weights are free to download and self-host under the MIT license, but you'll need your own compute to run a 700+ billion parameter model. The hosted API is pay-per-token ($1.40/$4.40 per million input/output tokens as of launch), and Z.ai also offers flat-rate "GLM Coding Plan" subscriptions starting at a few dollars a month for lighter use.
Is GLM-5.2 actually better than GPT-5.5?
On SWE-bench Pro, the benchmark Z.ai highlighted at launch, GLM-5.2 scored higher (62.1 vs 58.6) at a fraction of the per-token cost. Whether that translates to "better" for your specific workload depends on the task — benchmark wins on one suite don't guarantee an across-the-board advantage, and we haven't yet run our own side-by-side testing.
Can I self-host GLM-5.2?
Yes. The weights are published on Hugging Face under the MIT license, which permits self-hosting and commercial use. You'll need substantial GPU resources given the model's scale.
How big is GLM-5.2's context window?
1,048,576 tokens (roughly 1 million), with a maximum output of 262,144 tokens. That's about 5x the 200,000-token context window of its predecessor, GLM-5.1.
Where can I check current pricing?
Pricing on new model releases tends to shift in the first few months. Check z.ai directly or OpenRouter's GLM-5.2 listing for current rates rather than relying solely on figures in this article.
Final Verdict
GLM-5.2 is a genuinely significant open-weight release — not because it's flawless, but because it closes a real gap between what you can self-host under a permissive license and what you'd otherwise have to pay a premium for through a closed API. The SWE-bench Pro result against GPT-5.5 is the kind of benchmark win that's hard to dismiss, especially paired with a meaningfully lower cost per token.
The honest caveat: this article is based on launch-week specs and benchmarks, not our own extended testing, because the model has only been public for about a week. We'll revisit this review with hands-on notes once we've run it against real workloads.
Early rating: 8.6/10 — a strong open-source option for coding-heavy, cost-sensitive teams, with the normal new-release caveats attached.