| Signal | Qwen3 235B A22B Thinking 2507 | Delta | GLM 4 32B |
|---|---|---|---|
Capabilities | 67 | +33 | |
Pricing | 2 | +1 | |
Context window size | 81 | +0 | |
Recency | 87 | +0 | |
Output Capacity | 20 | -- | |
| Overall Result | 4 wins | of 5 | 0 wins |
9
days higher
3
days
18
days higher
Alibaba
Zhipu AI
GLM 4 32B saves you $74.70/month
That's $896.40/year compared to Qwen3 235B A22B Thinking 2507 at your current usage level of 100K calls/month.
| Metric | Qwen3 235B A22B Thinking 2507 | GLM 4 32B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #217 | #218 | Qwen3 235B A22B Thinking 2507 |
| Quality Rank | #217 | #218 | Qwen3 235B A22B Thinking 2507 |
| Adoption Rank | #217 | #218 | Qwen3 235B A22B Thinking 2507 |
| Parameters | 235B | 32B | -- |
| Context Window | 131K | 128K | Qwen3 235B A22B Thinking 2507 |
| Pricing | $0.15/$1.50/M | $0.10/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 33 | Qwen3 235B A22B Thinking 2507 |
| Pricing | 2 | 0 | Qwen3 235B A22B Thinking 2507 |
| Context window size | 81 | 81 | Qwen3 235B A22B Thinking 2507 |
| Recency | 87 | 87 | Qwen3 235B A22B Thinking 2507 |
| Output Capacity | 20 | 20 | Qwen3 235B A22B Thinking 2507 |
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 40/100 (rank #217), placing it in the top 26% of all 290 models tracked.
Scores 40/100 (rank #218), placing it in the top 25% 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 32B offers 88% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with GLM 4 32B vs $24.67/month with Qwen3 235B A22B Thinking 2507 - a $21.67 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. GLM 4 32B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K 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 (40/100) correlates with better nuance, coherence, and style in long-form content
Qwen3 235B A22B Thinking 2507 and GLM 4 32B 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
Qwen3 235B A22B Thinking 2507
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4 32B
88% lower pricing; better value at scale
Best for Reliability
Qwen3 235B A22B Thinking 2507
Higher uptime and faster response speeds
Best for Prototyping
Qwen3 235B A22B Thinking 2507
Stronger community support and better developer experience
Best for Production
Qwen3 235B A22B Thinking 2507
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3 235B A22B Thinking 2507 | GLM 4 32B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Alibaba
Zhipu AI
GLM 4 32B saves you $1.76/month
That's 85% cheaper than Qwen3 235B A22B Thinking 2507 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 235B A22B Thinking 2507 | GLM 4 32B |
|---|---|---|
| Context Window | 131K | 128K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | No |
| Created | Jul 25, 2025 | Jul 24, 2025 |
Both Qwen3 235B A22B Thinking 2507 and GLM 4 32B score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Qwen3 235B A22B Thinking 2507 is ranked #217 and GLM 4 32B is ranked #218 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 Qwen3 235B A22B Thinking 2507's $1.50/M output tokens - 15.0x more expensive. Input token pricing: Qwen3 235B A22B Thinking 2507 at $0.15/M vs GLM 4 32B at $0.10/M.
Qwen3 235B A22B Thinking 2507 has a larger context window of 131,072 tokens compared to GLM 4 32B 's 128,000 tokens. A larger context window means the model can process longer documents and conversations.