| Signal | o4 Mini Deep Research | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 100 | +17 | |
Pricing | 8 | +8 | |
Context window size | 84 | -11 | |
Recency | 100 | -- | |
Output Capacity | 83 | +3 | |
Benchmarks | 0 | -67 | |
| Overall Result | 3 wins | of 6 | 2 wins |
29
days ranked higher
0
days
1
days ranked higher
OpenAI
Alibaba
Qwen3.5-Flash saves you $580.50/month
That's $6966.00/year compared to o4 Mini Deep Research at your current usage level of 100K calls/month.
| Metric | o4 Mini Deep Research | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 85 | 79 | o4 Mini Deep Research |
| Rank | #41 | #80 | o4 Mini Deep Research |
| Quality Rank | #41 | #80 | o4 Mini Deep Research |
| Adoption Rank | #41 | #80 | o4 Mini Deep Research |
| Parameters | -- | -- | -- |
| Context Window | 200K | 1000K | Qwen3.5-Flash |
| Pricing | $2.00/$8.00/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | o4 Mini Deep Research |
| Pricing | 8 | 0 | o4 Mini Deep Research |
| Context window size | 84 | 95 | Qwen3.5-Flash |
| Recency | 100 | 100 | o4 Mini Deep Research |
| Output Capacity | 83 | 80 | o4 Mini Deep Research |
| Benchmarks | -- | 67 | Qwen3.5-Flash |
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 85/100 (rank #41), placing it in the top 86% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
o4 Mini Deep Research has a 6-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 97% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $150.00/month with o4 Mini Deep Research - a $145.13 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 (85/100) 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
o4 Mini Deep Research has a moderate advantage with a 5.599999999999994-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
o4 Mini Deep Research
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
97% lower pricing; better value at scale
Best for Reliability
o4 Mini Deep Research
Higher uptime and faster response speeds
Best for Prototyping
o4 Mini Deep Research
Stronger community support and better developer experience
Best for Production
o4 Mini Deep Research
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | o4 Mini 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 $12.77/month
That's 97% cheaper than o4 Mini 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 | o4 Mini 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 |
o4 Mini Deep Research scores 85/100 (rank #41) compared to Qwen3.5-Flash's 79/100 (rank #80), giving it a 6-point advantage. o4 Mini Deep Research is the stronger overall choice, though Qwen3.5-Flash may excel in specific areas like cost efficiency.
o4 Mini Deep Research is ranked #41 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 o4 Mini Deep Research's $8.00/M output tokens - 30.8x more expensive. Input token pricing: o4 Mini Deep Research at $2.00/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to o4 Mini Deep Research's 200,000 tokens. A larger context window means the model can process longer documents and conversations.