Z.ai · Open weights
GLM 4.6V Original
GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Integrates native Function Calling capabilities, bridging 'visual perception' and 'executable action' for multimodal agents. Direct via Z-AI (Zhipu).
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.60
- Output
- $0.90
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 24K
- Average output (7 days)
- 258 tokens
- Longer than 27% of 468 models
Side by side
GLM 4.6V Original compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GLM 4.6V OriginalZ.aiThis model | LFM2.5 2.6BliquidRemove | |
|---|---|---|
| Provider | Z.ai | liquid |
| Discovery tier | Open weights | Open weights |
| Added | Dec 8, 2025 | Aug 12, 2026 |
| Limits | ||
| Context window | 128K | 128K |
| Max output | 24K | 32.77KBest |
| Average output | 258 tokens | 459 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.60 | $0.10Best |
| Output | $0.90 | $0.20Best |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 8.4 | 8.4 |
| Coding Index | — | 7.7 |
| Math Index | 26.3 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | Yes | No |
| Structured output | No | Yes |
| Reasoning effort | — | minimal, low, medium, high, xhigh |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GLM 4.6V Original
- LFM2.5 2.6B
Intelligence Index
- GLM 4.6V Original8.4
- LFM2.5 2.6B8.4
GLM 4.6V Original: Better than 13% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GLM 4.6V Original: —
- LFM2.5 2.6B: 7.7
Math Index
Composite of the math evaluations
- GLM 4.6V Original: 26.3
- LFM2.5 2.6B: —
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- GLM 4.6V Original: 17.0%Top
- LFM2.5 2.6B: 5.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GLM 4.6V Original: 3.7%
- LFM2.5 2.6B: 6.2%Top
GPQA Diamond
Graduate-level science questions
- GLM 4.6V Original: 56.6%Top
- LFM2.5 2.6B: 55.8%
MMLU-Pro
Multi-subject knowledge and reasoning
- GLM 4.6V Original: 75.2%
- LFM2.5 2.6B: —
IFBench
Following precise output instructions
- GLM 4.6V Original: 27.9%
- LFM2.5 2.6B: —
Coding
SciCode
Scientific computing in Python
- GLM 4.6V Original: —
- LFM2.5 2.6B: 14.4%
LiveCodeBench
Recent competitive programming problems
- GLM 4.6V Original: 41.1%
- LFM2.5 2.6B: —
Math
AIME 2025
Competition mathematics, 2025 problems
- GLM 4.6V Original: 26.3%
- LFM2.5 2.6B: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- GLM 4.6V Original: 3.0%
- LFM2.5 2.6B: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- GLM 4.6V Original: 30.7%
- LFM2.5 2.6B: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GLM 4.6V Original: 17.0%Top
- LFM2.5 2.6B: 5.7%
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-4.6v-original",
"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-4.6v-original",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Z.ai
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