| Signal | Qwen2.5 Coder 32B Instruct | Delta | GLM 4 32B |
|---|---|---|---|
Capabilities | 17 | -17 | |
Pricing | 99 | -1 | |
Context window size | 72 | -9 | |
Recency | 40 | -46 | |
Output Capacity | 20 | -- | |
Benchmarks | 0 | -44 | |
| Overall Result | 0 wins | of 6 | 5 wins |
Score History
36.7
current score
Qwen2.5 Coder 32B Instruct
right now
35.6
current score
Alibaba
Zhipu AI
GLM 4 32B saves you $101.00/month
That's $1212.00/year compared to Qwen2.5 Coder 32B Instruct at your current usage level of 100K calls/month.
| Metric | Qwen2.5 Coder 32B Instruct | GLM 4 32B | Winner |
|---|---|---|---|
| Overall Score | 37 | 36 | Qwen2.5 Coder 32B Instruct |
| Rank | #297 | #298 | Qwen2.5 Coder 32B Instruct |
| Quality Rank | #297 | #298 | Qwen2.5 Coder 32B Instruct |
| Adoption Rank | #297 | #298 | Qwen2.5 Coder 32B Instruct |
| Parameters | 32B | 32B | -- |
| Context Window | 33K | 128K | GLM 4 32B |
| Pricing | $0.66/$1.00/M | $0.10/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 33 | GLM 4 32B |
| Pricing | 99 | 100 | GLM 4 32B |
| Context window size | 72 | 81 | GLM 4 32B |
| Recency | 40 | 86 | GLM 4 32B |
| Output Capacity | 20 | 20 | Qwen2.5 Coder 32B Instruct |
| Benchmarks | -- | 44 | GLM 4 32B |
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 37/100 (rank #297), placing it in the top -2% of all 290 models tracked.
Scores 36/100 (rank #298), placing it in the top -2% 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.
GLM 4 32B offers 88% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with GLM 4 32B vs $24.90/month with Qwen2.5 Coder 32B Instruct - a $21.90 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 32B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (37/100) correlates with better nuance, coherence, and style in long-form content
Qwen2.5 Coder 32B Instruct and GLM 4 32B are extremely close in overall performance (only 1.1000000000000014 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Qwen2.5 Coder 32B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4 32B
88% lower pricing; better value at scale
Best for Reliability
Qwen2.5 Coder 32B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Qwen2.5 Coder 32B Instruct
Stronger community support and better developer experience
Best for Production
Qwen2.5 Coder 32B Instruct
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen2.5 Coder 32B Instruct | GLM 4 32B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Zhipu AI
GLM 4 32B saves you $2.09/month
That's 87% cheaper than Qwen2.5 Coder 32B Instruct 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 | Qwen2.5 Coder 32B Instruct | GLM 4 32B |
|---|---|---|
| Context Window | 33K | 128K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | No |
| Created | Nov 11, 2024 | Jul 24, 2025 |
Qwen2.5 Coder 32B Instruct scores 37/100 (rank #297) compared to GLM 4 32B 's 36/100 (rank #298), giving it a 1-point advantage. Qwen2.5 Coder 32B Instruct is the stronger overall choice, though GLM 4 32B may excel in specific areas like cost efficiency.
Qwen2.5 Coder 32B Instruct is ranked #297 and GLM 4 32B is ranked #298 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 32B is cheaper at $0.10/M output tokens vs Qwen2.5 Coder 32B Instruct's $1.00/M output tokens - 10.0x more expensive. Input token pricing: Qwen2.5 Coder 32B Instruct at $0.66/M vs GLM 4 32B at $0.10/M.
GLM 4 32B has a larger context window of 128,000 tokens compared to Qwen2.5 Coder 32B Instruct's 32,768 tokens. A larger context window means the model can process longer documents and conversations.