| Signal | GPT-3.5 Turbo Instruct | Delta | GLM 4 32B |
|---|---|---|---|
Capabilities | 33 | -- | |
Pricing | 98 | -2 | |
Context window size | 57 | -24 | |
Recency | 0 | -86 | |
Output Capacity | 60 | +40 | |
Benchmarks | 0 | -44 | |
| Overall Result | 1 wins | of 6 | 4 wins |
Score History
35
current score
GLM 4 32B
right now
35.6
current score
OpenAI
Zhipu AI
GLM 4 32B saves you $235.00/month
That's $2820.00/year compared to GPT-3.5 Turbo Instruct at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo Instruct | GLM 4 32B | Winner |
|---|---|---|---|
| Overall Score | 35 | 36 | GLM 4 32B |
| Rank | #300 | #298 | GLM 4 32B |
| Quality Rank | #300 | #298 | GLM 4 32B |
| Adoption Rank | #300 | #298 | GLM 4 32B |
| Parameters | -- | 32B | -- |
| Context Window | 4K | 128K | GLM 4 32B |
| Pricing | $1.50/$2.00/M | $0.10/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 33 | GPT-3.5 Turbo Instruct |
| Pricing | 98 | 100 | GLM 4 32B |
| Context window size | 57 | 81 | GLM 4 32B |
| Recency | 0 | 86 | GLM 4 32B |
| Output Capacity | 60 | 20 | GPT-3.5 Turbo 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 35/100 (rank #300), placing it in the top -3% 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 94% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with GLM 4 32B vs $52.50/month with GPT-3.5 Turbo Instruct - a $49.50 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 (36/100) correlates with better nuance, coherence, and style in long-form content
GPT-3.5 Turbo Instruct and GLM 4 32B are extremely close in overall performance (only 0.6000000000000014 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-3.5 Turbo Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4 32B
94% lower pricing; better value at scale
Best for Reliability
GPT-3.5 Turbo Instruct
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo Instruct
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo Instruct
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo Instruct | GLM 4 32B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Zhipu AI
GLM 4 32B saves you $4.80/month
That's 94% cheaper than GPT-3.5 Turbo 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 | GPT-3.5 Turbo Instruct | GLM 4 32B |
|---|---|---|
| Context Window | 4K | 128K |
| Max Output Tokens | 4,096 | -- |
| Open Source | No | No |
| Created | Sep 28, 2023 | Jul 24, 2025 |
GLM 4 32B scores 36/100 (rank #298) compared to GPT-3.5 Turbo Instruct's 35/100 (rank #300), giving it a 1-point advantage. GLM 4 32B is the stronger overall choice, though GPT-3.5 Turbo Instruct may excel in specific areas like certain benchmarks.
GPT-3.5 Turbo Instruct is ranked #300 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 GPT-3.5 Turbo Instruct's $2.00/M output tokens - 20.0x more expensive. Input token pricing: GPT-3.5 Turbo Instruct at $1.50/M vs GLM 4 32B at $0.10/M.
GLM 4 32B has a larger context window of 128,000 tokens compared to GPT-3.5 Turbo Instruct's 4,095 tokens. A larger context window means the model can process longer documents and conversations.