What Is GLM-5? Architecture, Speed & API Access

I’m Dora. Recently, GLM-5 kept showing up in threads and benchmarks while I was trying to get through a normal week of drafts, specs, and a few small data pulls. I paused the third time I saw it mentioned next to “reasoning” and “agentic.” Not because I needed a new model, but because my current mix sometimes drags on longer tasks. If a swap could lighten the load a bit, I wanted to feel that for myself.

So I spent a few evenings in early February 2026 running GLM-5 against the kind of work that actually happens on my desk: messy prompts, half-finished outlines, and scripts that never stay the same for long. Here’s what stood out, calmly, without fireworks.

GLM-5 in context — Zhipu’s fifth-generation model

Zhipu AI has been shipping GLM models for a while. If you’ve used GLM-3 or GLM-4, you already know the vibe: solid multilingual reasoning, good coding instincts, and a practical streak, you can get work done without massaging every prompt.

GLM-5 is their next step. I’m sticking to what I could observe and what Zhipu shares in public materials. If you want the vendor’s wording, the official docs are a good anchor point: Zhipu AI (GLM) docs and the broader Zhipu site.

745B total / 44B active (MoE architecture)

The headline detail is architecture. GLM-5 uses a Mixture-of-Experts (MoE) setup: a large pool of “experts” (reportedly around 745B total parameters), but only a slice activates per token, roughly 44B on average. In practice, this means two things I felt day to day:

First-token latency felt closer to a 30–70B dense model than a 700B giant. My prompts didn’t hang at the start the way some oversized models do.
Long-form stability was better than I expected. MoE can sometimes wander: GLM-5 mostly stayed on track in multi-step outlines and code refactors, which I didn’t take for granted.

I care less about the number and more about what it buys: the active compute is big enough to carry nuance, but the routing keeps cost and speed in a workable band. According to Hugging Face’s MoE explainer, sparse activation allows models to “scale to billions or even trillions of parameters” while maintaining reasonable inference costs. On a few long reasoning chains (multi-hop analysis over ~3–5 paragraphs), I noticed fewer “forgetful” jumps compared to smaller dense models.

Key upgrades: reasoning, coding, agentic, creative writing

What changed for me versus earlier GLMs:

Reasoning: Chain-of-thought style structure (even without asking) appeared more often. I didn’t always want it verbatim, but the internal logic felt steadier. When I asked it to critique its own plan, it adjusted without getting defensive or looping.
Coding: It handled incremental edits better than full rewrites. When I asked for a diff-style change in a script, it preserved context instead of reprinting everything. This shaved off minutes, small, but real.
Agentic behavior: Tool-call style tasks (describe steps, identify missing inputs, propose retries) came out clearer. I wouldn’t hand it unattended access to critical systems, but as a planning partner it was competent.
Creative writing: Voice control improved. If I set a tone (“plain, slow, and kind”), it kept that line for a few pages. It still stumbles when the brief mixes too many constraints, but the drift was mild.

None of this felt magical. It did, but, reduce the mental overhead my prompts usually require. That matters on a Tuesday afternoon when attention is scarce.

Inference speed profile — what to expect

I tested GLM-5 through a shared inference layer rather than Zhipu’s own console, so hardware likely varied under the hood. Still, a pattern showed up across three sessions (Feb 6–9, 2026):

First-token latency: Generally under a second on short prompts: 1–2 seconds on heavier, tool-like requests with multi-part instructions. That’s the range where I don’t lose my train of thought.
Sustained throughput: For long answers, I saw steady streaming that felt in the 30–60 tokens/second band. It didn’t stall mid-paragraph the way some MoE models do under load.
Stability under context: At ~8–16k tokens, outputs stayed coherent. I didn’t push to the max window in these runs because my real tasks rarely need it. More on window size in the FAQ.

Latency vs throughput vs cost tradeoffs

The MoE design means you’re trading dense-model simplicity for a routing layer that (ideally) pays for itself in speed/cost at the same quality level. In practice:

If you care about snappy back-and-forth (product specs, email drafts, refactors), GLM-5 feels responsive enough to stay in flow.
If you batch large jobs, throughput holds up. I’d still chunk very long documents to avoid retries.
Cost is provider-dependent. The active 44B suggests pricing in the “large but not giant” tier. If your current stack uses small dense models for quick tasks and a single expensive model for tough ones, GLM-5 might cover more middle ground with fewer switches.

One note from the field: I didn’t see big speed differences between “reasoning-heavy” and “creative” prompts. Some models slow down when they decide to think out loud. GLM-5 kept a steady pace either way.

How to access GLM-5 via WaveSpeed API

I used GLM-5 through WaveSpeed, which wraps multiple providers behind an OpenAI-compatible interface. No code here, just the steps I followed, in plain language.

Model ID, endpoint, auth setup

Model ID: I selected the model listed as “glm-5” in the WaveSpeed model catalog. Some providers append size or routing tags: I stuck with the default.
Endpoint style: The interface mirrored the familiar chat.completions pattern. If you’ve integrated anything OpenAI-like, the swap is usually changing the base URL and the model string.
Auth: A single API key in the standard Authorization header worked. I set a per-project key to keep logs tidy. Rate limits showed up in headers, handy when you’re tuning concurrency.

Two practical notes from my setup:

Temperature and top_p behaved predictably, but I got better stability by lowering temperature slightly (0.5–0.7) on complex prompts. It reduced meandering without flattening tone.
Maximum output tokens: the default cap was conservative. If your answers get clipped, raise it early. It saves re-runs.

GLM-5 in the landscape (GPT-5, Claude 4.5, DeepSeek)

Comparisons get noisy fast, so I’ll keep this to practical feel, not leaderboard theater.

Versus GPT line: The GPT family still wins on ecosystem gravity, plugins, examples, community snippets. In head-down writing and stepwise reasoning, GLM-5 held its own. It made fewer formatting oddities in long outlines than some GPT variants I’ve used lately, and it handled incremental code edits with less overreach.
Versus Claude line: Claude models tend to be careful, good at restraint and summary. GLM-5 matched that restraint on factual rewrites and was slightly more willing to propose next steps without being asked. If you love Claude for tone and safety scaffolding, you may still prefer it for sensitive content.
Versus DeepSeek: DeepSeek models I’ve tried feel nimble and cost-efficient, great for high-volume tasks. GLM-5 felt heavier per call but steadier on multi-hop analysis. If you hammer a model with lots of small queries, DeepSeek might edge it on cost-performance: for fewer, deeper calls, GLM-5 made sense to me.

None of these are right or wrong, just different defaults. If you’re already embedded in one ecosystem, the case to switch is thinner. If you’re mixing models per task, GLM-5 is a strong candidate for the “thinking work” slot.

FAQ — availability, pricing, context window

Availability: GLM-5 is accessible through Zhipu’s platform and some aggregators. If you’re outside China, latency and access can differ by provider. I used WaveSpeed during the week of Feb 6–9, 2026.
Pricing: It varies. Aggregators set their own rates, and vendors adjust over time. I avoid quoting numbers that will age poorly. Check your provider’s pricing page right before you roll anything to production.
Context window: I didn’t hit the ceiling in my tests. Working ranges around 8–16k tokens were stable. If your workflow leans on very long contexts (full PDFs, transcripts), confirm the hard limits in the docs and watch for truncation.
Safety and moderation: I saw standard guardrails. It refused a few ambiguous requests until I clarified use. If your domain has strict compliance needs, run a small policy audit first.
Who it’s for: If you need fewer models and steadier outputs on planning, analysis, and revision-heavy writing, GLM-5 fits. If you optimize for ultra-cheap, ultra-fast micro-tasks, a smaller dense model or DeepSeek-style option might serve you better.

A small closing note from my desk: the part I appreciated wasn’t raw power, it was not having to babysit it. That’s not a headline, but it’s the kind of quiet improvement that stacks up over a week.