| Signal | GPT-4 Turbo | Delta | Qwen3.5-9B |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 69 | +3 | |
Pricing | 70 | -30 | |
Context window size | 81 | -5 | |
Recency | 0 | -100 | |
Output Capacity | 60 | -15 | |
| Overall Result | 1 wins | of 6 | 5 wins |
Score History
66.7
current score
GPT-4 Turbo
right now
66.5
current score
OpenAI
Alibaba
Qwen3.5-9B saves you $2487.50/month
That's $29850.00/year compared to GPT-4 Turbo at your current usage level of 100K calls/month.
| Metric | GPT-4 Turbo | Qwen3.5-9B | Winner |
|---|---|---|---|
| Overall Score | 67 | 67 | GPT-4 Turbo |
| Rank | #112 | #113 | GPT-4 Turbo |
| Quality Rank | #112 | #113 | GPT-4 Turbo |
| Adoption Rank | #112 | #113 | GPT-4 Turbo |
| Parameters | -- | 9B | -- |
| Context Window | 128K | 256K | Qwen3.5-9B |
| Pricing | $10.00/$30.00/M | $0.05/$0.15/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-9B |
| Benchmarks | 69 | 66 | GPT-4 Turbo |
| Pricing | 70 | 100 | Qwen3.5-9B |
| Context window size | 81 | 86 | Qwen3.5-9B |
| Recency | 0 | 100 | Qwen3.5-9B |
| Output Capacity | 60 | 75 | Qwen3.5-9B |
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 67/100 (rank #112), placing it in the top 62% of all 290 models tracked.
Scores 67/100 (rank #113), placing it in the top 61% 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.
Qwen3.5-9B offers 99% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Qwen3.5-9B vs $600.00/month with GPT-4 Turbo - a $597.00 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. Qwen3.5-9B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (256K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.15/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (67/100) 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 Turbo and Qwen3.5-9B are extremely close in overall performance (only 0.20000000000000284 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4 Turbo
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-9B
99% lower pricing; better value at scale
Best for Reliability
GPT-4 Turbo
Higher uptime and faster response speeds
Best for Prototyping
GPT-4 Turbo
Stronger community support and better developer experience
Best for Production
GPT-4 Turbo
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4 Turbo | Qwen3.5-9B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
Alibaba
Qwen3.5-9B saves you $53.73/month
That's 100% cheaper than GPT-4 Turbo 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 Turbo | Qwen3.5-9B |
|---|---|---|
| Context Window | 128K | 256K |
| Max Output Tokens | 4,096 | 32,768 |
| Open Source | No | Yes |
| Created | Apr 9, 2024 | Mar 10, 2026 |