| Signal | o3 Deep Research | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 100 | +17 | |
Benchmarks | 88 | +21 | |
Pricing | 40 | +40 | |
Context window size | 84 | -11 | |
Recency | 100 | -- | |
Output Capacity | 83 | +3 | |
| Overall Result | 4 wins | of 6 | 1 wins |
30
days ranked higher
0
days
0
days ranked higher
OpenAI
Alibaba
Qwen3.5-Flash saves you $2980.50/month
That's $35766.00/year compared to o3 Deep Research at your current usage level of 100K calls/month.
| Metric | o3 Deep Research | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 92 | 79 | o3 Deep Research |
| Rank | #8 | #80 | o3 Deep Research |
| Quality Rank | #8 | #80 | o3 Deep Research |
| Adoption Rank | #8 | #80 | o3 Deep Research |
| Parameters | -- | -- | -- |
| Context Window | 200K | 1000K | Qwen3.5-Flash |
| Pricing | $10.00/$40.00/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | o3 Deep Research |
| Benchmarks | 88 | 67 | o3 Deep Research |
| Pricing | 40 | 0 | o3 Deep Research |
| Context window size | 84 | 95 | Qwen3.5-Flash |
| Recency | 100 | 100 | o3 Deep Research |
| Output Capacity | 83 | 80 | o3 Deep Research |
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 92/100 (rank #8), placing it in the top 98% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
o3 Deep Research has a 12-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 99% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $750.00/month with o3 Deep Research - a $745.13 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 (92/100) 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
o3 Deep Research clearly outperforms Qwen3.5-Flash with a significant 12.099999999999994-point lead. For most general use cases, o3 Deep Research is the stronger choice. However, Qwen3.5-Flash may still excel in niche scenarios.
Best for Quality
o3 Deep Research
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
99% lower pricing; better value at scale
Best for Reliability
o3 Deep Research
Higher uptime and faster response speeds
Best for Prototyping
o3 Deep Research
Stronger community support and better developer experience
Best for Production
o3 Deep Research
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | o3 Deep Research | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Alibaba
Qwen3.5-Flash saves you $65.57/month
That's 99% cheaper than o3 Deep Research 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 | o3 Deep Research | Qwen3.5-Flash |
|---|---|---|
| Context Window | 200K | 1M |
| Max Output Tokens | 100,000 | 65,536 |
| Open Source | No | No |
| Created | Oct 10, 2025 | Feb 25, 2026 |
o3 Deep Research scores 92/100 (rank #8) compared to Qwen3.5-Flash's 79/100 (rank #80), giving it a 12-point advantage. o3 Deep Research is the stronger overall choice, though Qwen3.5-Flash may excel in specific areas like cost efficiency.
o3 Deep Research is ranked #8 and Qwen3.5-Flash is ranked #80 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.
Qwen3.5-Flash is cheaper at $0.26/M output tokens vs o3 Deep Research's $40.00/M output tokens - 153.8x more expensive. Input token pricing: o3 Deep Research at $10.00/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to o3 Deep Research's 200,000 tokens. A larger context window means the model can process longer documents and conversations.