Last refreshed 2026-06-30. Next refresh: weekly.
Why use GLM-5 on Fireworks AI?
Fireworks AI offers GLM-5 with pay-as-you-go pricing at $1.00/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare GLM-5 across 7 providers to find the best fit for your use caseInput / 1M
$1.00
Output / 1M
$3.20
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install openaiAuth
export FIREWORKS_API_KEY=...Call
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],Model ID
accounts/fireworks/models/glm-5Request example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="accounts/fireworks/models/glm-5",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/glm-5", not the LLMReference slug "glm-5".
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_API_KEY; rename it only if your application config maps the new variable.
Compare GLM-5 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | $1.00 | $3.20 |
| OpenRouter | $0.60 | $2.08 |
| Together AI | $1.00 | $3.20 |
| GCP Vertex AI | $1.00 | $3.20 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.00 |
| Output tokens | $3.20 |
Capabilities
ReasoningJSON / Tool useStructured OutputsPrompt Caching
About GLM-5
Flagship open-weight foundation model from Zhipu AI with 744B parameters (40B active per token) in Mixture of Experts architecture. Trained on 28.5T tokens using DeepSeek Sparse Attention on Huawei Ascend hardware. Achieves state-of-the-art performance on coding and agentic benchmarks (SWE-bench Verified: 77.8%). Supports autonomous planning, multi-step tool use, and self-correction.
Get Started
Model Specs
Released2026-02-11
Parameters744B total, 40B active
Context200k
ArchitectureMixture of Experts
Knowledge cutoff2025-11