Z.ai · Open weights
GLM 5.3 Thinking
GLM-5.3 with higher reasoning enabled for harder long-horizon coding, autonomous agent workflows, and complex engineering tasks.
- Reasoning
- Tools
- Structured output
- Input
- Text
- Output
- Text
- Reasoning effort
- high · max
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.60
- Output
- $1.10
- Cache read
- $0.20
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 131.1K
- Average output (7 days)
- 2.3K tokens
- Longer than 93% of 468 models
Side by side
GLM 5.3 Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GLM 5.3 ThinkingZ.aiThis model | GLM 5.3 TEEZ.aiRemove | |
|---|---|---|
| Provider | Z.ai | Z.ai |
| Discovery tier | Open weights | Open weights |
| Added | Aug 14, 2026 | Aug 31, 2026 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 131.07K | 131.07K |
| Average output | 2.3K tokens | 1.2K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.60Best | $1.40 |
| Output | $1.10Best | $4.40 |
| Cache read | $0.20Best | $0.26 |
| Performance | ||
| Throughput | 123.3 tok/sBest | 38.5 tok/s |
| Time to first token | 7.7 s | 4.4 sBest |
| Request success (24h) | 99.7% | 99.7% |
| Benchmark indexes | ||
| Intelligence Index | 44.8 | 44.8 |
| Coding Index | 74.8 | 74.8 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Structured output | Yes | Yes |
| Parallel tools | No | Yes |
| Reasoning effort | high, max | low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GLM 5.3 Thinking
- GLM 5.3 TEE
Intelligence Index
- GLM 5.3 Thinking44.8
- GLM 5.3 TEE44.8
GLM 5.3 Thinking tested at max reasoning effort
GLM 5.3 Thinking: Better than 90% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GLM 5.3 Thinking: 74.8Top
- GLM 5.3 TEE: 74.8Top
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- GLM 5.3 Thinking: 62.2%Top
- GLM 5.3 TEE: 62.2%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- GLM 5.3 Thinking: 29.4%Top
- GLM 5.3 TEE: 29.4%Top
Harvey LAB-AAv1.1
Legal tasks passed in full, with no material hallucination
- GLM 5.3 Thinking: 0.3%Top
- GLM 5.3 TEE: 0.3%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- GLM 5.3 Thinking: 1,505 EloTop
- GLM 5.3 TEE: 1,505 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- GLM 5.3 Thinking: 1,649 EloTop
- GLM 5.3 TEE: 1,649 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- GLM 5.3 Thinking: 11.2%Top
- GLM 5.3 TEE: 11.2%Top
AA-LCRv1.1
Reasoning across long documents
- GLM 5.3 Thinking: 79.7%Top
- GLM 5.3 TEE: 79.7%Top
MLCR-AA
Long-context reasoning over medical records
- GLM 5.3 Thinking: 17.2%Top
- GLM 5.3 TEE: 17.2%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GLM 5.3 Thinking: 42.3%Top
- GLM 5.3 TEE: 42.3%Top
GPQA Diamond
Graduate-level science questions
- GLM 5.3 Thinking: 91.7%Top
- GLM 5.3 TEE: 91.7%Top
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- GLM 5.3 Thinking: 41.9%Top
- GLM 5.3 TEE: 41.9%Top
SciCode
Scientific computing in Python
- GLM 5.3 Thinking: 59.0%Top
- GLM 5.3 TEE: 59.0%Top
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GLM 5.3 Thinking: 79.7%Top
- GLM 5.3 TEE: 79.7%Top
Scores from Artificial Analysis, updated Oct 26, 2026. GLM 5.3 Thinking was tested at max reasoning effort; compare scores measured at the same effort.Artificial Analysis
- GLM 5.3 Thinking
- GLM 5.3 TEE
| Leaderboard | GLM 5.3 Thinking | GLM 5.3 TEE |
|---|---|---|
| Math | #16 · 5.4% | Not in top 20 |
Ranks among Noumen catalog models on Use cases leaderboards. Only top-20 placements are listed.
- GLM 5.3 Thinking
- GLM 5.3 TEE
| Leaderboard | GLM 5.3 Thinking | GLM 5.3 TEE |
|---|---|---|
| Math | #16 · 1499.8 | Not in top 20 |
| Instruction Following | #18 · 1478.4 | Not in top 20 |
| Hard Prompts | #18 · 1504.5 | Not in top 20 |
| Chinese | #13 · 1538.0 | Not in top 20 |
| English | #16 · 1490.7 | Not in top 20 |
| Expert | #17 · 1517.2 | Not in top 20 |
| French | #9 · 1513.1 | Not in top 20 |
| German | #11 · 1498.6 | Not in top 20 |
| Hard Prompts English | #15 · 1510.7 | Not in top 20 |
| Industry Life And Physical And Social Science | #15 · 1505.7 | Not in top 20 |
| Industry Medicine And Healthcare | #17 · 1500.3 | Not in top 20 |
| Korean | #11 · 1463.0 | Not in top 20 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
- GLM 5.3 Thinking
- GLM 5.3 TEE
| Leaderboard | GLM 5.3 Thinking | GLM 5.3 TEE |
|---|---|---|
| FrontierMath v2 (Tiers 1–3) | #4 · 68.8% | Not in top 20 |
| GPQA Diamond | #7 · 90.9% | Not in top 20 |
Ranks among Noumen catalog models on Epoch AI leaderboards. Only top-20 placements are listed.Epoch AI
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
- 123.3 tok/s
- Faster than 70% of 254 models
- Time to first token
- 7.7 s
- Quicker than 18% of 254 models
- Prompt cache hits
- 46.7%
- About $0.41 per 1M input tokens with caching
- Request success (24h)
- 99.7%
- 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-5.3:thinking",
"reasoning_effort": "high",
"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-5.3:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Z.ai
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