Z.ai · Open weights
GLM 4.6 Turbo (Thinking)
GLM 4.6 Turbo with thinking mode enabled for enhanced reasoning; shows internal reasoning and supports long context.
- Reasoning
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $1.00
- Output
- $3.00
Reference prices as published by the model provider. API example
Specifications
- Context window
- 204.8K
- Max output
- 131.1K
- Average output (7 days)
- 1.9K tokens
- Longer than 91% of 465 models
Side by side
GLM 4.6 Turbo (Thinking) compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GLM 4.6 Turbo (Thinking)Z.aiThis model | DeepSeek V4 Flash 0423DeepSeekRemove | |
|---|---|---|
| Provider | Z.ai | DeepSeek |
| Discovery tier | Open weights | Open weights |
| Added | Oct 2, 2025 | Oct 7, 2026 |
| Limits | ||
| Context window | 204.8K | 1.05MBest |
| Max output | 131.07K | — |
| Average output | 1.9K tokens | 245 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $1.00 | $0.14Best |
| Output | $3.00 | $0.28Best |
| Cache read | — | $0.028 |
| Performance | ||
| Throughput | — | 131.1 tok/s |
| Time to first token | — | 2.0 s |
| Request success (24h) | — | 98.4% |
| Benchmark indexes | ||
| Intelligence Index | 18.5 | 18.9Best |
| Coding Index | 45.8 | — |
| Math Index | 86.0 | — |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GLM 4.6 Turbo (Thinking)
- DeepSeek V4 Flash 0423
Intelligence Index
- GLM 4.6 Turbo (Thinking)18.5
- DeepSeek V4 Flash 042318.9
GLM 4.6 Turbo (Thinking): Better than 47% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GLM 4.6 Turbo (Thinking): 45.8
- DeepSeek V4 Flash 0423: —
Math Index
Composite of the math evaluations
- GLM 4.6 Turbo (Thinking): 86.0
- DeepSeek V4 Flash 0423: —
Agentic work
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- GLM 4.6 Turbo (Thinking): 756 Elo
- DeepSeek V4 Flash 0423: —
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- GLM 4.6 Turbo (Thinking): 54.0%Top
- DeepSeek V4 Flash 0423: 41.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GLM 4.6 Turbo (Thinking): 14.5%Top
- DeepSeek V4 Flash 0423: 7.8%
GPQA Diamond
Graduate-level science questions
- GLM 4.6 Turbo (Thinking): 78.0%Top
- DeepSeek V4 Flash 0423: 71.6%
MMLU-Pro
Multi-subject knowledge and reasoning
- GLM 4.6 Turbo (Thinking): 82.9%
- DeepSeek V4 Flash 0423: —
IFBench
Following precise output instructions
- GLM 4.6 Turbo (Thinking): 43.4%
- DeepSeek V4 Flash 0423: 47.2%Top
Coding
LiveCodeBench
Recent competitive programming problems
- GLM 4.6 Turbo (Thinking): 69.5%
- DeepSeek V4 Flash 0423: —
Math
AIME 2025
Competition mathematics, 2025 problems
- GLM 4.6 Turbo (Thinking): 86.0%
- DeepSeek V4 Flash 0423: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- GLM 4.6 Turbo (Thinking): 25.0%
- DeepSeek V4 Flash 0423: 34.1%Top
τ²-Bench Telecom
Tool use in a customer-support simulation
- GLM 4.6 Turbo (Thinking): 70.5%
- DeepSeek V4 Flash 0423: 94.4%Top
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GLM 4.6 Turbo (Thinking): 54.0%Top
- DeepSeek V4 Flash 0423: 41.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
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-4.6-turbo:thinking",
"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-4.6-turbo:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Z.ai
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