| Signal | GLM 4.6 | Delta | GLM 4.7 |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 70 | -2 | |
Pricing | 2 | +0 | |
Context window size | 84 | +0 | |
Recency | 99 | -1 | |
Output Capacity | 89 | +8 | |
| Overall Result | 3 wins | of 6 | 2 wins |
6
days higher
0
days
24
days higher
Zhipu AI
Zhipu AI
GLM 4.7 saves you $7.50/month
That's $90.00/year compared to GLM 4.6 at your current usage level of 100K calls/month.
| Metric | GLM 4.6 | GLM 4.7 | Winner |
|---|---|---|---|
| Overall Score | 71 | 73 | GLM 4.7 |
| Rank | #55 | #53 | GLM 4.7 |
| Quality Rank | #55 | #53 | GLM 4.7 |
| Adoption Rank | #55 | #53 | GLM 4.7 |
| Parameters | -- | -- | -- |
| Context Window | 205K | 203K | GLM 4.6 |
| Pricing | $0.39/$1.90/M | $0.39/$1.75/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | GLM 4.6 |
| Benchmarks | 70 | 72 | GLM 4.7 |
| Pricing | 2 | 2 | GLM 4.6 |
| Context window size | 84 | 84 | GLM 4.6 |
| Recency | 99 | 100 | GLM 4.7 |
| Output Capacity | 89 | 80 | 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%). Here's what the scores mean for these two models:
Scores 71/100 (rank #55), placing it in the top 81% of all 290 models tracked.
Scores 73/100 (rank #53), placing it in the top 82% of all 290 models tracked.
With only a 2-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 4.7 offers 7% better value per quality point. At 1M tokens/day, you'd spend $32.10/month with GLM 4.7 vs $34.35/month with GLM 4.6 - a $2.25 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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. GLM 4.7 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 ($1.75/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (73/100) correlates with better nuance, coherence, and style in long-form content
GLM 4.6 and GLM 4.7 are extremely close in overall performance (only 2 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GLM 4.6
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4.7
7% lower pricing; better value at scale
Best for Reliability
GLM 4.6
Higher uptime and faster response speeds
Best for Prototyping
GLM 4.6
Stronger community support and better developer experience
Best for Production
GLM 4.6
Wider enterprise adoption and proven at scale
by Zhipu AI
| Capability | GLM 4.6 | GLM 4.7 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Zhipu AI
Zhipu AI
GLM 4.7 saves you $0.1800/month
That's 6% 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 4.6 | GLM 4.7 |
|---|---|---|
| Context Window | 205K | 203K |
| Max Output Tokens | 204,800 | 65,535 |
| Open Source | Yes | Yes |
| Created | Sep 30, 2025 | Dec 22, 2025 |
GLM 4.7 scores 73/100 (rank #53) compared to GLM 4.6's 71/100 (rank #55), giving it a 2-point advantage. GLM 4.7 is the stronger overall choice, though GLM 4.6 may excel in specific areas like certain benchmarks.
GLM 4.6 is ranked #55 and GLM 4.7 is ranked #53 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
GLM 4.7 is cheaper at $1.75/M output tokens vs GLM 4.6's $1.90/M output tokens - 1.1x more expensive. Input token pricing: GLM 4.6 at $0.39/M vs GLM 4.7 at $0.39/M.
GLM 4.6 has a larger context window of 204,800 tokens compared to GLM 4.7's 202,752 tokens. A larger context window means the model can process longer documents and conversations.