Z.ai · Open weights
GLM 4.5
GLM-4.5 is a Mixture-of-Experts model built for agent-based applications, with 355B total / 32B active parameters and up to 128k context. It supports reasoning, code generation, and agent workflows.
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.30
- Output
- $1.30
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 65.5K
- Average output (7 days)
- 696 tokens
- Longer than 62% of 468 models
Side by side
GLM 4.5 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GLM 4.5Z.aiThis model | GLM 5.2Z.aiRemove | |
|---|---|---|
| Provider | Z.ai | Z.ai |
| Discovery tier | Open weights | Open weights |
| Added | Apr 15, 2025 | Jun 15, 2026 |
| Limits | ||
| Context window | 128K | 1.05MBest |
| Max output | 65.54K | 131.07KBest |
| Average output | 696 tokens | 839 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.30Best | $0.42 |
| Output | $1.30Best | $1.32 |
| Cache read | — | $0.078 |
| Performance | ||
| Throughput | 35.7 tok/s | 96.8 tok/sBest |
| Time to first token | 9.6 s | 1.6 sBest |
| Request success (24h) | 100.0%Best | 99.6% |
| Benchmark indexes | ||
| Intelligence Index | 12.8 | 22.4Best |
| Coding Index | — | 46.5 |
| Math Index | 73.7 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GLM 4.5
- GLM 5.2
Intelligence Index
- GLM 4.512.8
- GLM 5.222.4
GLM 4.5: Better than 29% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GLM 4.5: —
- GLM 5.2: 46.5
Math Index
Composite of the math evaluations
- GLM 4.5: 73.7
- GLM 5.2: —
Agentic work
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- GLM 4.5: —
- GLM 5.2: 1,244 Elo
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- GLM 4.5: 52.7%Top
- GLM 5.2: 42.3%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GLM 4.5: 13.0%Top
- GLM 5.2: 9.8%
GPQA Diamond
Graduate-level science questions
- GLM 4.5: 78.2%Top
- GLM 5.2: 68.6%
MMLU-Pro
Multi-subject knowledge and reasoning
- GLM 4.5: 83.5%
- GLM 5.2: —
IFBench
Following precise output instructions
- GLM 4.5: 44.1%
- GLM 5.2: —
Coding
LiveCodeBench
Recent competitive programming problems
- GLM 4.5: 73.8%
- GLM 5.2: —
Math
AIME 2025
Competition mathematics, 2025 problems
- GLM 4.5: 73.7%
- GLM 5.2: —
AIME
Competition mathematics
- GLM 4.5: 87.3%
- GLM 5.2: —
MATH-500
Competition math problems
- GLM 4.5: 97.9%
- GLM 5.2: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- GLM 4.5: 22.0%
- GLM 5.2: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- GLM 4.5: 43.0%
- GLM 5.2: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GLM 4.5: 52.7%Top
- GLM 5.2: 42.3%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- GLM 4.5
- GLM 5.2
| Leaderboard | GLM 4.5 | GLM 5.2 |
|---|---|---|
| EQ-Bench 4 | Not in top 20 | #17 · 1211 |
| EQ-Bench 3 | Not in top 20 | #6 · 1563 |
Ranks among Noumen catalog models on EQ-Bench leaderboards. Only top-20 placements are listed.EQ-Bench
- GLM 4.5
- GLM 5.2
| Leaderboard | GLM 4.5 | GLM 5.2 |
|---|---|---|
| UI Component | Not in top 20 | #20 · 1291 |
| Data Visualization | Not in top 20 | #19 · 1285 |
| Agentic Game Dev | Not in top 20 | #8 · 1170 |
| Web Apps | Not in top 20 | #15 · 1223 |
| Full Stack | Not in top 20 | #11 · 1241 |
| Native Apps | Not in top 20 | #14 · 1181 |
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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
- 35.7 tok/s
- Faster than 25% of 254 models
- Time to first token
- 9.6 s
- Quicker than 11% of 254 models
- Prompt cache hits
- 45.5%
- 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",
"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",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Z.ai
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