Z.ai · Open weights
GLM 4.5 Air
GLM-4.5-Air is a 106B total / 12B active parameter model designed to unify frontier reasoning, coding, and agentic capabilities. On the SWE-bench Verified benchmark, it delivers the best performance at its scale with a competitive performance-to-cost ratio.
- Tools
- Structured output
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.13
- Output
- $0.85
- Cache read
- $0.025
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 98.3K
- Average output (7 days)
- 59 tokens
- Longer than 5% of 465 models
Side by side
GLM 4.5 Air compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GLM 4.5 AirZ.aiThis model | GLM 4 Air 0111Z.aiRemove | |
|---|---|---|
| Provider | Z.ai | Z.ai |
| Discovery tier | Open weights | Efficient |
| Added | Apr 15, 2025 | Jan 11, 2025 |
| Limits | ||
| Context window | 128K | 128K |
| Max output | 98.3KBest | 4.1K |
| Average output | 59 tokens | 69 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.13Best | $0.14 |
| Output | $0.85 | $0.14Best |
| Cache read | $0.025 | — |
| Performance | ||
| Throughput | 89.9 tok/s | — |
| Time to first token | 1.6 s | — |
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | 11.1 | — |
| Math Index | 80.7 | — |
| Capabilities | ||
| Tools | Yes | No |
| Structured output | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GLM 4.5 Air
- GLM 4 Air 0111
Intelligence Index
- GLM 4.5 Air11.1
- GLM 4 Air 0111—
GLM 4.5 Air: Better than 23% of 326 scored models
Indexes
Math Index
Composite of the math evaluations
- GLM 4.5 Air: 80.7
- GLM 4 Air 0111: —
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- GLM 4.5 Air: 46.7%
- GLM 4 Air 0111: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GLM 4.5 Air: 7.0%
- GLM 4 Air 0111: —
GPQA Diamond
Graduate-level science questions
- GLM 4.5 Air: 73.3%
- GLM 4 Air 0111: —
MMLU-Pro
Multi-subject knowledge and reasoning
- GLM 4.5 Air: 81.5%
- GLM 4 Air 0111: —
IFBench
Following precise output instructions
- GLM 4.5 Air: 37.6%
- GLM 4 Air 0111: —
Coding
LiveCodeBench
Recent competitive programming problems
- GLM 4.5 Air: 68.4%
- GLM 4 Air 0111: —
Math
AIME 2025
Competition mathematics, 2025 problems
- GLM 4.5 Air: 80.7%
- GLM 4 Air 0111: —
AIME
Competition mathematics
- GLM 4.5 Air: 67.3%
- GLM 4 Air 0111: —
MATH-500
Competition math problems
- GLM 4.5 Air: 96.5%
- GLM 4 Air 0111: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- GLM 4.5 Air: 20.5%
- GLM 4 Air 0111: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- GLM 4.5 Air: 46.5%
- GLM 4 Air 0111: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GLM 4.5 Air: 46.7%
- GLM 4 Air 0111: —
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 89.9 tok/s
- Faster than 57% of 251 models
- Time to first token
- 1.6 s
- Quicker than 85% of 251 models
- Prompt cache hits
- 40.6%
- About $0.0873 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "z-ai/GLM-4.5-Air",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="z-ai/GLM-4.5-Air",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Z.ai
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