| Signal | Qwen3.5-35B-A3B | Delta | GLM 4.7 |
|---|---|---|---|
Capabilities | 83 | +17 | |
Benchmarks | 67 | -6 | |
Pricing | 1 | 0 | |
Context window size | 86 | +2 | |
Recency | 100 | -- | |
Output Capacity | 80 | -- | |
| Overall Result | 2 wins | of 6 | 2 wins |
3
days higher
0
days
27
days higher
Alibaba
Zhipu AI
Qwen3.5-35B-A3B saves you $45.25/month
That's $543.00/year compared to GLM 4.7 at your current usage level of 100K calls/month.
| Metric | Qwen3.5-35B-A3B | GLM 4.7 | Winner |
|---|---|---|---|
| Overall Score | 69 | 73 | GLM 4.7 |
| Rank | #67 | #53 | GLM 4.7 |
| Quality Rank | #67 | #53 | GLM 4.7 |
| Adoption Rank | #67 | #53 | GLM 4.7 |
| Parameters | 35B | -- | -- |
| Context Window | 262K | 203K | Qwen3.5-35B-A3B |
| Pricing | $0.16/$1.30/M | $0.39/$1.75/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 67 | Qwen3.5-35B-A3B |
| Benchmarks | 67 | 72 | GLM 4.7 |
| Pricing | 1 | 2 | GLM 4.7 |
| Context window size | 86 | 84 | Qwen3.5-35B-A3B |
| Recency | 100 | 100 | Qwen3.5-35B-A3B |
| Output Capacity | 80 | 80 | Qwen3.5-35B-A3B |
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 69/100 (rank #67), placing it in the top 77% of all 290 models tracked.
Scores 73/100 (rank #53), placing it in the top 82% of all 290 models tracked.
With only a 4-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.
Qwen3.5-35B-A3B offers 32% better value per quality point. At 1M tokens/day, you'd spend $21.94/month with Qwen3.5-35B-A3B vs $32.10/month with GLM 4.7 - a $10.16 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. Qwen3.5-35B-A3B 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 ($1.30/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
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
GLM 4.7 has a moderate advantage with a 4.1000000000000085-point lead in composite score. It wins on more signal dimensions, but Qwen3.5-35B-A3B has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Qwen3.5-35B-A3B
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-35B-A3B
32% lower pricing; better value at scale
Best for Reliability
Qwen3.5-35B-A3B
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5-35B-A3B
Stronger community support and better developer experience
Best for Production
Qwen3.5-35B-A3B
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5-35B-A3B | GLM 4.7 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Zhipu AI
Qwen3.5-35B-A3B saves you $0.9495/month
That's 34% cheaper than GLM 4.7 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 | Qwen3.5-35B-A3B | GLM 4.7 |
|---|---|---|
| Context Window | 262K | 203K |
| Max Output Tokens | 65,536 | 65,535 |
| Open Source | Yes | Yes |
| Created | Feb 25, 2026 | Dec 22, 2025 |
GLM 4.7 scores 73/100 (rank #53) compared to Qwen3.5-35B-A3B's 69/100 (rank #67), giving it a 4-point advantage. GLM 4.7 is the stronger overall choice, though Qwen3.5-35B-A3B may excel in specific areas like cost efficiency.
Qwen3.5-35B-A3B is ranked #67 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.
Qwen3.5-35B-A3B is cheaper at $1.30/M output tokens vs GLM 4.7's $1.75/M output tokens - 1.3x more expensive. Input token pricing: Qwen3.5-35B-A3B at $0.16/M vs GLM 4.7 at $0.39/M.
Qwen3.5-35B-A3B has a larger context window of 262,144 tokens compared to GLM 4.7's 202,752 tokens. A larger context window means the model can process longer documents and conversations.