Qwen3 235B A22B Thinking 2507 vs GLM 4.6V
| Signal | Qwen3 235B A22B Thinking 2507 | Delta | GLM 4.6V |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 64 | +1 | |
Pricing | 97 | -2 | |
Context window size | 86 | +5 | |
Recency | 66 | -25 | |
Output Capacity | 75 | -- | |
| Overall Result | 2 wins | of 6 | 3 wins |
Score History
65.5
current score
Tied
right now
65.5
current score
Qwen3 235B A22B Thinking 2507
Alibaba
GLM 4.6V
Zhipu AI
GLM 4.6V saves you $105.00/month
That's $1260.00/year compared to Qwen3 235B A22B Thinking 2507 at your current usage level of 100K calls/month.
| Metric | Qwen3 235B A22B Thinking 2507 | GLM 4.6V | Winner |
|---|---|---|---|
| Overall Score | 66 | 66 | -- |
| Rank | #141 | #140 | GLM 4.6V |
| Quality Rank | #141 | #140 | GLM 4.6V |
| Adoption Rank | #141 | #140 | GLM 4.6V |
| Parameters | 235B | -- | -- |
| Context Window | 262K | 131K | Qwen3 235B A22B Thinking 2507 |
| Pricing | $0.30/$3.00/M | $0.30/$0.90/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | GLM 4.6V |
| Benchmarks | 64 | 64 | Qwen3 235B A22B Thinking 2507 |
| Pricing | 97 | 99 | GLM 4.6V |
| Context window size | 86 | 81 | Qwen3 235B A22B Thinking 2507 |
| Recency | 66 | 91 | GLM 4.6V |
| Output Capacity | 75 | 75 | 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%). Learn more about our methodology.
Scores 66/100 (rank #141), placing it in the top 52% of all 290 models tracked.
Scores 66/100 (rank #140), placing it in the top 52% 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.
Choose Qwen3 235B A22B Thinking 2507 when you need:
- Processing long documents or large codebases (262K token context)
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
Choose GLM 4.6V when you need:
- High-volume production workloads where API costs must be minimized
- Multimodal workflows that require image understanding
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
GLM 4.6V offers 64% better value per quality point. At 1M tokens/day, you'd spend $18.00/month with GLM 4.6V vs $49.50/month with Qwen3 235B A22B Thinking 2507 - a $31.50 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. GLM 4.6V 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 ($0.90/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (66/100) 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
Qwen3 235B A22B Thinking 2507 and GLM 4.6V 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.
By Use Case
Best for Quality
Qwen3 235B A22B Thinking 2507
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 4.6V
64% 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
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Qwen3 235B A22B Thinking 2507 | GLM 4.6V |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Qwen3 235B A22B Thinking 2507
Alibaba
GLM 4.6V
Zhipu AI
GLM 4.6V saves you $2.52/month
That's 61% 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 4.6V |
|---|---|---|
| Context Window | 262K | 131K |
| Max Output Tokens | 32,768 | 32,768 |
| Open Source | Yes | Yes |
| Created | Jul 25, 2025 | Dec 8, 2025 |