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Model garden Router · warm Dedicated · on request

GLM 5

Instantly via the EU router or as a dedicated GPU deployment. Data stays in Europe.

We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (3...

Start for free ← All models
zai-org/GLM-5
text->text · zai-org · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
754B
Parameters
203K
Context window
1810GB
Minimum VRAM
POST /api/v1/chat/completions 200 OK

Specifications

Parameters 754B
Context window 202,752 tokens
Minimum VRAM 1810 GB
Architecture GlmMoeDsaForCausalLM (vLLM)
License mit
Modality text->text
Released February 2026
Publisher zai-org ↗

Pricing

Shared router · per token
€1.15
Input (per 1M tokens)
€3.68
Output (per 1M tokens)
€0.29 per million cached input tokens
Dedicated GPU · per hour
On request
Dedicated deployment, from 1810 GB of VRAM. Billed per GPU-hour.

Shared EU router, pay-per-token, scale-to-zero. Dedicated GPU deployments are billed hourly, see pricing.

Bench index

Bench is our quality index for every model, 0 to 100. It rests on public measurements (Epoch AI with about 40 benchmarks, LiveBench, LMArena and more) and is refreshed every morning. Where nobody measures, it shows an estimate with a range.

69.8 / 100 · Measured

Measured by Epoch AI across 12 benchmarks, with a range.

Range 67.3 to 72.2 · Rank 81 of 274 at Epoch AI

Sources: Epoch AI ↗ · LiveBench ↗ · LMArena ↗ · Refreshed daily, last on 08-10-2026

LiveBench · 68.9
Reasoning 69.1
Coding 73.6
Agentic coding 55.0
Mathematics 83.5
Data analysis 67.9
Language 77.5
Instruction following 55.3
Every benchmark (12)
GPQA diamond 83.8
OTIS Mock AIME 2024-2025 80.0
SWE-Bench verified 72.1
Terminal Bench 52.4
WeirdML 48.2
ARC-AGI 44.7
SimpleBench 43.8
FrontierMath-2025-02-28-Private 28.8
CL-bench 18.7
Chess Puzzles 5.3
ARC-AGI-2 4.9
FrontierMath-Tier-4-2025-07-01-Private 3.5
LiveBench · All tasks
Reasoning
theory of mind 82.7
zebra puzzle 35.8
spatial 92.0
logic with navigation 66.0
Coding
code generation 69.0
code completion 78.3
Agentic coding
javascript 50.0
typescript 40.0
python 75.0
Mathematics
AMPS Hard 97.0
integrals with game 57.0
math comp 95.1
olympiad 84.8
Data analysis
consecutive events 60.0
tablejoin 45.7
tablereformat 98.0
Language
connections 91.0
plot unscrambling 63.6
typos 78.0
Instruction following
paraphrase 52.4
simplify 52.9
story generation 57.8
summarize 58.3

Call it now

Drop-in replacement for OpenAI: change only the base URL and API key. The Anthropic format (/v1/messages) is supported too.

curl https://hostyourai.com/api/v1/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-5",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run GLM 5 in the EU?

Yes. HostYourAI runs GLM 5 on GPUs in European datacenters via vLLM. Prompts and outputs never leave the EU and there is no US cloud provider in the chain.

Is hosting GLM 5 GDPR-compliant?

Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open-source weights also mean: no training on your data.

How much does GLM 5 cost?

Via the shared EU router you pay €1.15 per million input tokens and €3.68 per million output tokens, with no fixed costs. For high volume or isolation you can also run GLM 5 as a dedicated hourly GPU instance.

Is the API OpenAI-compatible?

Yes. You use the standard OpenAI SDKs with a custom base URL (https://hostyourai.com/api/v1). The Anthropic Messages API is supported as a drop-in as well.

More models from Z.AI

GLM 5.3 Flash

We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

321B 1M context View model →
GLM 5.3

GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:

753B 1M context View model →
GLM 5.2

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: - Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work - Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - Improved Architecture: We propose IndexShare, which reuses the same indexer across every fou

753B 1M context View model →
GLM 5.1

GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

754B 203K context View model →
GLM 4.7 Flash

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

31B 203K context View model →
GLM Image

GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑imag

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