GLM 5.2 Inference

GLM 5.2, priced to win.
Tuned to maximum performance.

Z.AI's frontier open model on dedicated, sovereign serving with a learning loop that keeps cache hit rates high, lowering effective token costs while keeping performance at peak.

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50%
Cheaper than Fireworks
See the full comparison ↓
744B MoE, 40B active
1M Token context
MIT Open weights
ProviderInput $/1MOutput $/1MCache hit rate
PRIMALABS$0.133$2.2090%
Z.AI$0.346$4.4092.4%
Fireworks$0.421$4.4077.7%
Together AI$0.534$4.4075.9%
PROVIDER PRICING: OPENROUTER EFFECTIVE PRICING, JULY 2026 · CACHE HIT RATE IS THE HIDDEN MULTIPLIER ON EVERY BILL
Why PrimaLabs

Static stacks tune once. Ours learns every workload.

Customization

Tuned to the workload, not the benchmark

The learning loop observes live prompts, context lengths, and load, then re-tunes batching, scheduling, and cache strategy for that exact traffic. GLM 5.2 serving gets faster and cheaper the longer it runs.

Cache

Hit rate is where the bill is won

Cached input tokens bill at a fraction of list price. One warm, dedicated stack keeps hit rates high where routed serverless traffic fragments them. Effective cost per token drops below any sticker price.

Sovereign

Dedicated capacity, fully controlled

Reserved throughput on one sovereign stack, in the PrimaLabs cloud, a customer VPC, or on customer GPUs, NVIDIA or AMD. Known data path, predictable latency, cost that falls with scale.

Where it fits

The open-model move that pays for itself

Displacement

Moving off closed-model APIs

Teams cutting closed-model spend keep quality with GLM 5.2 and gain a serving layer that improves over time. The migration pays back in effective cost per token.

Agents

Agentic and coding workloads

Strong open-model coding performance with faster first tokens on every sequential call, and cache economics that reward repeated context.

Scale

Inference as core COGS

When tokens are the business model, capacity per GPU is the margin. The loop keeps raising it under real load, not benchmark load.

Consistency

One stack, every request

No provider roulette. Every request hits the same warm, sovereign infrastructure, so latency and cache behavior stay predictable at scale.

FAQ

Common questions

How is PrimaLabs pricing structured?
Per million tokens on dedicated capacity, with volume tiers that step down as sustained throughput rises. Cache-aware pricing means the effective rate depends on hit rate, which the learning loop actively manages upward.
Is GLM 5.2 served at full context length?
Yes. GLM 5.2 runs at its published context window, with the serving layer tuned for long-context memory efficiency under real traffic.
How hard is migration?
The API is OpenAI compatible. Change the base URL and the API key, keep the rest of the code. Most teams are serving production traffic the same week.
What does customization mean in practice?
Every deployment is tuned to its own traffic. Prompt shapes, context length distribution, request rate, and cache behavior all feed the loop, so two customers running the same model get two differently optimized stacks.

Get GLM 5.2 pricing for the workload

Share the traffic profile. Get dedicated pricing with a cache-aware cost model, and a benchmark on real prompts within days.

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