GLM 4.5 Air
Z.ai · released Jul 25, 2025
GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter...
Specification
- Context window
- 131K
- Max output
- 98K
- Knowledge cutoff
- Dec 31, 2024
- Parameters
- 110.5B
- Licence
- mit
- Serving providers
- 3
- Moderated
- No
- Uptime
- 100.0%
Intelligence
16.7
52th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
42 t/s
Median across providers
Latency
782ms
Time to first token
Cost per task
$0.07
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-Pro81.5%
- AIME 202580.7%
- GPQA Diamond73.3%
- LiveCodeBench68.4%
- τ²-bench (Telecom)46.5%
- AA-LCR (long context)45.7%
- IFBench37.6%
- SciCode30.6%
- Terminal-Bench Hard20.5%
- Humanity's Last Exam7.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 81.5% |
| AIME 2025 | 80.7% |
| GPQA Diamond | 73.3% |
| LiveCodeBench | 68.4% |
| τ²-bench (Telecom) | 46.5% |
| AA-LCR (long context) | 45.7% |
| IFBench | 37.6% |
| SciCode | 30.6% |
| Terminal-Bench Hard | 20.5% |
| Humanity's Last Exam | 7.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Trinity Large ThinkingArcee AI18.6
- Sonar Reasoning ProPerplexity18.0
- GLM 4.5 AirZ.ai16.7
- o3 Mini HighOpenAI15.7
- gpt-oss-20bOpenAI15.2
- gpt-oss-20b (free)OpenAI15.2
- 15.0
- GPT-4.1 MiniOpenAI14.8
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Trinity Large Thinking | 18.6 |
| Sonar Reasoning Pro | 18.0 |
| GLM 4.5 Air | 16.7 |
| o3 Mini High | 15.7 |
| gpt-oss-20b | 15.2 |
| gpt-oss-20b (free) | 15.2 |
| Nemotron 3 Nano Omni (free) | 15.0 |
| GPT-4.1 Mini | 14.8 |
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
- $0.13
- Output / 1M tokens
- $0.85
- Cached input / 1M
- $0.025
- Blended 3:1
- $0.31
One task, estimated
$0.07
- 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).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
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
- 1,218
- Win rate
- 59.2%
- Strongest at
- Data visualisation
- Tournaments
- 218
Elo by category
Dot position on a 1,100–1,225 scale · Elo has no meaningful zero
- 1,218
- 1,178
- 1,169
- 1,158
- 1,135
- 1,118
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Data visualisation | 1218 | 59.2% |
| 3D scenes | 1178 | 54.1% |
| Websites | 1169 | 51.3% |
| UI components | 1158 | 54.5% |
| Game development | 1135 | 48.4% |
| SVG | 1118 | 50.8% |