GLM 4.6V
Z.ai · released Dec 8, 2025
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts...
Specification
- Context window
- 131K
- Max output
- 33K
- Knowledge cutoff
- Not stated
- Parameters
- 107.7B
- Licence
- mit
- Serving providers
- 2
- Moderated
- No
- Uptime
- 99.5%
Intelligence
10.9
38th percentile
Coding
—
Coding Index
Agentic
18.6
Agentic Index
Output speed
20 t/s
Median across providers
Latency
1.66s
Time to first token
Cost per task
$0.09
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro75.2%
- GPQA Diamond56.6%
- LiveCodeBench41.1%
- τ²-bench (Telecom)30.7%
- IFBench27.9%
- SciCode27.2%
- AIME 202526.3%
- AA-LCR (long context)14.3%
- Humanity's Last Exam3.7%
- Terminal-Bench Hard3.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 75.2% |
| GPQA Diamond | 56.6% |
| LiveCodeBench | 41.1% |
| τ²-bench (Telecom) | 30.7% |
| IFBench | 27.9% |
| SciCode | 27.2% |
| AIME 2025 | 26.3% |
| AA-LCR (long context) | 14.3% |
| Humanity's Last Exam | 3.7% |
| Terminal-Bench Hard | 3.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- GPT-4o (2024-11-20)OpenAI11.1
- GPT-4oOpenAI11.1
- 11.0
- GLM 4.6VZ.ai10.9
- Llama 4 ScoutMeta10.3
- 9.9
- R1 Distill Llama 70BDeepSeek9.8
- GPT-4.1 NanoOpenAI9.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| GPT-4o (2024-11-20) | 11.1 |
| GPT-4o | 11.1 |
| Qwen3 VL 32B Instruct | 11.0 |
| GLM 4.6V | 10.9 |
| Llama 4 Scout | 10.3 |
| Qwen3 VL 30B A3B Instruct | 9.9 |
| R1 Distill Llama 70B | 9.8 |
| GPT-4.1 Nano | 9.6 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.3
- Output / 1M tokens
- $0.9
- Cached input / 1M
- $0.055
- Blended 3:1
- $0.45
One task, estimated
$0.09
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,197
- Win rate
- 54.4%
- Strongest at
- Websites
- Tournaments
- 12,417
Elo by category
Dot position on a 1,050–1,200 scale · Elo has no meaningful zero
- 1,197
- 1,193
- 1,191
- 1,190
- 1,183
- 1,156
- 1,155
- 1,072
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Websites | 1197 | 54.4% |
| UI components | 1193 | 53.9% |
| Game development | 1191 | 54.7% |
| Data visualisation | 1190 | 52.6% |
| 3D scenes | 1183 | 54.0% |
| SVG | 1156 | 52.1% |
| Mobile apps | 1155 | 46.8% |
| Full-stack apps | 1072 | 42.3% |