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Models

GLM-5-FP8

By zai-org. 753.9 billion parameters, a context window of 202,752 tokens and the licence mit.

Facts

Released
2026-02-11 the day the repository was first published on Hugging Face Hugging Face, read
Licence
mit the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
753.9 billion counted from the safetensors weight files Hugging Face, read
Active parameters
51.8 billion estimate estimated from the expert counts and sizes in the configuration model configuration, read
Knowledge cutoff
Unknown
Context window
  • 202,752 tokens the maximum position embeddings in the model configuration model configuration, read
Longest output
Unknown
Tool calling
Unknown
Structured output
Unknown
Reasoning controls
Unknown
Inputs and outputs
text in, text out Hugging Face, read
Good for
No source names a use.

Prices

Prices in US dollars per million tokens, as each source lists them
ProviderInputOutputContextAs ofSource
GMI Cloud direct0.600 USD1.92 USD202,752 older than 30 days; check the providermodels.dev, read

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
F16 estimate1,404 GiB1,476 GiB1,478 GiB
Q8_0 estimate746 GiB785 GiB787 GiB
Q4_0 estimate395 GiB416 GiB418 GiB

Start it with a local runtime

  • vLLM
    vllm serve zai-org/GLM-5-FP8
  • SGLang
    sglang serve --model-path zai-org/GLM-5-FP8 --port 30000

Use it from your harness

Set up for GMI Cloud with the model zai-org/GLM-5-FP8. Each endpoint page has the same setup for its own address.

OpenCode

Put this in opencode.json in your project folder:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "gmicloud": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "GMI Cloud",
      "options": {
        "baseURL": "https://api.gmi-serving.com/v1",
        "apiKey": "{env:GMICLOUD_API_KEY}"
      },
      "models": {
        "zai-org/GLM-5-FP8": {
          "name": "GLM-5-FP8"
        }
      }
    }
  }
}
  • OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.

From OpenCode documentation, read .

Pi

Put this in ~/.pi/agent/models.json:

{
  "providers": {
    "gmicloud": {
      "baseUrl": "https://api.gmi-serving.com/v1",
      "api": "openai-completions",
      "apiKey": "$GMICLOUD_API_KEY",
      "models": [
        {
          "id": "zai-org/GLM-5-FP8"
        }
      ]
    }
  }
}
  • The apiKey field can name an environment variable as $NAME.

From Pi documentation, read .

Codex

Codex speaks only the Responses API, and GMI Cloud documents no Responses address. A gateway that offers one can sit in between.

  • Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.

From Codex documentation, read .

Claude Code

Claude Code sends Anthropic Messages requests, and GMI Cloud documents no such address. A gateway that translates to that API can sit in between.

  • Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.

From Claude Code documentation, read .

Published results

These are results other people published. Baltor did not run them and does not rank models by them.