| Signal | GLM 4.5 Air | Delta | GLM 4.6 |
|---|---|---|---|
Capabilities | 50 | -17 | |
Benchmarks | 72 | +2 | |
Pricing | 99 | +1 | |
Context window size | 81 | -3 | |
Recency | 80 | -12 | |
Output Capacity | 83 | -5 | |
| Overall Result | 2 wins | of 6 | 4 wins |
Score History
70.7
current score
Tied
right now
70.7
current score
Zhipu AI
Zhipu AI
GLM 4.5 Air saves you $78.50/month
That's $942.00/year compared to GLM 4.6 at your current usage level of 100K calls/month.
| Metric | GLM 4.5 Air | GLM 4.6 | Winner |
|---|---|---|---|
| Overall Score | 71 | 71 | -- |
| Rank | #94 | #92 | GLM 4.6 |
| Quality Rank | #94 | #92 | GLM 4.6 |
| Adoption Rank | #94 | #92 | GLM 4.6 |
| Parameters | -- | -- | -- |
| Context Window | 131K | 205K | GLM 4.6 |
| Pricing | $0.13/$0.85/M | $0.39/$1.90/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 67 | GLM 4.6 |
| Benchmarks | 72 | 70 | GLM 4.5 Air |
| Pricing | 99 | 98 | GLM 4.5 Air |
| Context window size | 81 | 84 | GLM 4.6 |
| Recency | 80 | 92 | GLM 4.6 |
| Output Capacity | 83 | 89 | GLM 4.6 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 71/100 (rank #94), placing it in the top 68% of all 290 models tracked.
Scores 71/100 (rank #92), placing it in the top 69% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
GLM 4.5 Air offers 57% better value per quality point. At 1M tokens/day, you'd spend $14.70/month with GLM 4.5 Air vs $34.35/month with GLM 4.6 - a $19.65 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. GLM 4.5 Air also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (205K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.85/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (71/100) correlates with better nuance, coherence, and style in long-form content
GLM 4.5 Air and GLM 4.6 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GLM 4.5 Air
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4.5 Air
57% lower pricing; better value at scale
Best for Reliability
GLM 4.5 Air
Higher uptime and faster response speeds
Best for Prototyping
GLM 4.5 Air
Stronger community support and better developer experience
Best for Production
GLM 4.5 Air
Wider enterprise adoption and proven at scale
by Zhipu AI
| Capability | GLM 4.5 Air | GLM 4.6 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Zhipu AI
Zhipu AI
GLM 4.5 Air saves you $1.73/month
That's 58% cheaper than GLM 4.6 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | GLM 4.5 Air | GLM 4.6 |
|---|---|---|
| Context Window | 131K | 205K |
| Max Output Tokens | 98,304 | 204,800 |
| Open Source | Yes | Yes |
| Created | Jul 25, 2025 | Sep 30, 2025 |