| Signal | Qwen3.5-Flash | Delta | GLM 5.1 |
|---|---|---|---|
Capabilities | 83 | +17 | |
Benchmarks | 67 | -7 | |
Pricing | 100 | +4 | |
Context window size | 95 | +11 | |
Recency | 100 | -- | |
Output Capacity | 80 | +60 | |
| Overall Result | 4 wins | of 6 | 1 wins |
Score History
69.2
current score
GLM 5.1
right now
74.3
current score
Alibaba
Zhipu AI
Qwen3.5-Flash saves you $304.50/month
That's $3654.00/year compared to GLM 5.1 at your current usage level of 100K calls/month.
| Metric | Qwen3.5-Flash | GLM 5.1 | Winner |
|---|---|---|---|
| Overall Score | 69 | 74 | GLM 5.1 |
| Rank | #85 | #50 | GLM 5.1 |
| Quality Rank | #85 | #50 | GLM 5.1 |
| Adoption Rank | #85 | #50 | GLM 5.1 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 203K | Qwen3.5-Flash |
| Pricing | $0.07/$0.26/M | $1.26/$3.96/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 67 | Qwen3.5-Flash |
| Benchmarks | 67 | 74 | GLM 5.1 |
| Pricing | 100 | 96 | Qwen3.5-Flash |
| Context window size | 95 | 84 | Qwen3.5-Flash |
| Recency | 100 | 100 | Qwen3.5-Flash |
| Output Capacity | 80 | 20 | Qwen3.5-Flash |
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 69/100 (rank #85), placing it in the top 71% of all 290 models tracked.
Scores 74/100 (rank #50), placing it in the top 83% of all 290 models tracked.
GLM 5.1 has a 5-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 94% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $78.30/month with GLM 5.1 - a $73.43 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-Flash also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.26/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (74/100) 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 5.1 has a moderate advantage with a 5.099999999999994-point lead in composite score. It wins on more signal dimensions, but Qwen3.5-Flash has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Qwen3.5-Flash
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
94% lower pricing; better value at scale
Best for Reliability
Qwen3.5-Flash
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5-Flash
Stronger community support and better developer experience
Best for Production
Qwen3.5-Flash
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5-Flash | GLM 5.1 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Zhipu AI
Qwen3.5-Flash saves you $6.59/month
That's 94% cheaper than GLM 5.1 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-Flash | GLM 5.1 |
|---|---|---|
| Context Window | 1M | 203K |
| Max Output Tokens | 65,536 | -- |
| Open Source | No | Yes |
| Created | Feb 25, 2026 | Apr 7, 2026 |
GLM 5.1 scores 74/100 (rank #50) compared to Qwen3.5-Flash's 69/100 (rank #85), giving it a 5-point advantage. GLM 5.1 is the stronger overall choice, though Qwen3.5-Flash may excel in specific areas like cost efficiency.
Qwen3.5-Flash is ranked #85 and GLM 5.1 is ranked #50 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-Flash is cheaper at $0.26/M output tokens vs GLM 5.1's $3.96/M output tokens - 15.2x more expensive. Input token pricing: Qwen3.5-Flash at $0.07/M vs GLM 5.1 at $1.26/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to GLM 5.1's 202,752 tokens. A larger context window means the model can process longer documents and conversations.