| Signal | GPT-4.1 | Delta | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|
Capabilities | 83 | -- | |
Benchmarks | 75 | +9 | |
Pricing | 8 | +5 | |
Context window size | 96 | +14 | |
Recency | 69 | -29 | |
Output Capacity | 75 | -- | |
| Overall Result | 3 wins | of 6 | 1 wins |
10
days higher
4
days
16
days higher
OpenAI
Alibaba
Qwen3 VL 235B A22B Thinking saves you $444.00/month
That's $5328.00/year compared to GPT-4.1 at your current usage level of 100K calls/month.
| Metric | GPT-4.1 | Qwen3 VL 235B A22B Thinking | Winner |
|---|---|---|---|
| Overall Score | 77 | 77 | Qwen3 VL 235B A22B Thinking |
| Rank | #101 | #99 | Qwen3 VL 235B A22B Thinking |
| Quality Rank | #101 | #99 | Qwen3 VL 235B A22B Thinking |
| Adoption Rank | #101 | #99 | Qwen3 VL 235B A22B Thinking |
| Parameters | -- | 235B | -- |
| Context Window | 1048K | 131K | GPT-4.1 |
| Pricing | $2.00/$8.00/M | $0.26/$2.60/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 83 | GPT-4.1 |
| Benchmarks | 75 | 66 | GPT-4.1 |
| Pricing | 8 | 3 | GPT-4.1 |
| Context window size | 96 | 81 | GPT-4.1 |
| Recency | 69 | 99 | Qwen3 VL 235B A22B Thinking |
| Output Capacity | 75 | 75 | GPT-4.1 |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 77/100 (rank #101), placing it in the top 66% of all 290 models tracked.
Scores 77/100 (rank #99), placing it in the top 66% 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.
Qwen3 VL 235B A22B Thinking offers 71% better value per quality point. At 1M tokens/day, you'd spend $42.90/month with Qwen3 VL 235B A22B Thinking vs $150.00/month with GPT-4.1 - a $107.10 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 VL 235B A22B Thinking also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1048K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($2.60/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (77/100) 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
GPT-4.1 and Qwen3 VL 235B A22B Thinking are extremely close in overall performance (only 0.10000000000000853 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4.1
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 VL 235B A22B Thinking
71% lower pricing; better value at scale
Best for Reliability
GPT-4.1
Higher uptime and faster response speeds
Best for Prototyping
GPT-4.1
Stronger community support and better developer experience
Best for Production
GPT-4.1
Wider enterprise adoption and proven at scale
by OpenAI
by Alibaba
| Capability | GPT-4.1 | Qwen3 VL 235B A22B Thinking |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Alibaba
Qwen3 VL 235B A22B Thinking saves you $9.61/month
That's 73% cheaper than GPT-4.1 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 | GPT-4.1 | Qwen3 VL 235B A22B Thinking |
|---|---|---|
| Context Window | 1.0M | 131K |
| Max Output Tokens | 32,768 | 32,768 |
| Open Source | No | Yes |
| Created | Apr 14, 2025 | Sep 23, 2025 |
Qwen3 VL 235B A22B Thinking scores 77/100 (rank #99) compared to GPT-4.1's 77/100 (rank #101), giving it a 0-point advantage. Qwen3 VL 235B A22B Thinking is the stronger overall choice, though GPT-4.1 may excel in specific areas like certain benchmarks.
GPT-4.1 is ranked #101 and Qwen3 VL 235B A22B Thinking is ranked #99 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Qwen3 VL 235B A22B Thinking is cheaper at $2.60/M output tokens vs GPT-4.1's $8.00/M output tokens - 3.1x more expensive. Input token pricing: GPT-4.1 at $2.00/M vs Qwen3 VL 235B A22B Thinking at $0.26/M.
GPT-4.1 has a larger context window of 1,047,576 tokens compared to Qwen3 VL 235B A22B Thinking's 131,072 tokens. A larger context window means the model can process longer documents and conversations.