| Signal | Qwen3.5-27B | Delta | GLM 4.5 |
|---|---|---|---|
Capabilities | 83 | +17 | |
Benchmarks | 68 | +68 | |
Pricing | 2 | -1 | |
Context window size | 86 | +5 | |
Recency | 100 | +12 | |
Output Capacity | 80 | -3 | |
| Overall Result | 4 wins | of 6 | 2 wins |
11
days higher
5
days
14
days higher
Alibaba
Zhipu AI
Qwen3.5-27B saves you $72.50/month
That's $870.00/year compared to GLM 4.5 at your current usage level of 100K calls/month.
| Metric | Qwen3.5-27B | GLM 4.5 | Winner |
|---|---|---|---|
| Overall Score | 79 | 78 | Qwen3.5-27B |
| Rank | #86 | #88 | Qwen3.5-27B |
| Quality Rank | #86 | #88 | Qwen3.5-27B |
| Adoption Rank | #86 | #88 | Qwen3.5-27B |
| Parameters | 27B | -- | -- |
| Context Window | 262K | 131K | Qwen3.5-27B |
| Pricing | $0.20/$1.56/M | $0.60/$2.20/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 67 | Qwen3.5-27B |
| Benchmarks | 68 | -- | Qwen3.5-27B |
| Pricing | 2 | 2 | GLM 4.5 |
| Context window size | 86 | 81 | Qwen3.5-27B |
| Recency | 100 | 88 | Qwen3.5-27B |
| Output Capacity | 80 | 83 | GLM 4.5 |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 79/100 (rank #86), placing it in the top 71% of all 290 models tracked.
Scores 78/100 (rank #88), placing it in the top 70% of all 290 models tracked.
With only a 1-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.
Qwen3.5-27B offers 37% better value per quality point. At 1M tokens/day, you'd spend $26.33/month with Qwen3.5-27B vs $42.00/month with GLM 4.5 - a $15.67 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. Qwen3.5-27B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.56/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Qwen3.5-27B and GLM 4.5 are extremely close in overall performance (only 0.6999999999999886 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Qwen3.5-27B
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-27B
37% lower pricing; better value at scale
Best for Reliability
Qwen3.5-27B
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5-27B
Stronger community support and better developer experience
Best for Production
Qwen3.5-27B
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5-27B | GLM 4.5 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Zhipu AI
Qwen3.5-27B saves you $1.50/month
That's 40% cheaper than GLM 4.5 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 | Qwen3.5-27B | GLM 4.5 |
|---|---|---|
| Context Window | 262K | 131K |
| Max Output Tokens | 65,536 | 98,304 |
| Open Source | Yes | Yes |
| Created | Feb 25, 2026 | Jul 25, 2025 |
Qwen3.5-27B scores 79/100 (rank #86) compared to GLM 4.5's 78/100 (rank #88), giving it a 1-point advantage. Qwen3.5-27B is the stronger overall choice, though GLM 4.5 may excel in specific areas like certain benchmarks.
Qwen3.5-27B is ranked #86 and GLM 4.5 is ranked #88 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Qwen3.5-27B is cheaper at $1.56/M output tokens vs GLM 4.5's $2.20/M output tokens - 1.4x more expensive. Input token pricing: Qwen3.5-27B at $0.20/M vs GLM 4.5 at $0.60/M.
Qwen3.5-27B has a larger context window of 262,144 tokens compared to GLM 4.5's 131,072 tokens. A larger context window means the model can process longer documents and conversations.