| Signal | GPT-5.2-Codex | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 100 | +17 | |
Pricing | 14 | +14 | |
Context window size | 89 | -6 | |
Recency | 100 | -- | |
Output Capacity | 85 | +5 | |
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 $855.50/month
That's $10266.00/year compared to GPT-5.2-Codex at your current usage level of 100K calls/month.
| Metric | GPT-5.2-Codex | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 85 | 79 | GPT-5.2-Codex |
| Rank | #32 | #80 | GPT-5.2-Codex |
| Quality Rank | #32 | #80 | GPT-5.2-Codex |
| Adoption Rank | #32 | #80 | GPT-5.2-Codex |
| Parameters | -- | -- | -- |
| Context Window | 400K | 1000K | Qwen3.5-Flash |
| Pricing | $1.75/$14.00/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | GPT-5.2-Codex |
| Pricing | 14 | 0 | GPT-5.2-Codex |
| Context window size | 89 | 95 | Qwen3.5-Flash |
| Recency | 100 | 100 | GPT-5.2-Codex |
| Output Capacity | 85 | 80 | GPT-5.2-Codex |
| 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 #32), placing it in the top 89% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
GPT-5.2-Codex 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 98% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $236.25/month with GPT-5.2-Codex - a $231.38 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
GPT-5.2-Codex 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
GPT-5.2-Codex
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
98% lower pricing; better value at scale
Best for Reliability
GPT-5.2-Codex
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.2-Codex
Stronger community support and better developer experience
Best for Production
GPT-5.2-Codex
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-5.2-Codex | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Alibaba
Qwen3.5-Flash saves you $19.52/month
That's 98% cheaper than GPT-5.2-Codex 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-5.2-Codex | Qwen3.5-Flash |
|---|---|---|
| Context Window | 400K | 1M |
| Max Output Tokens | 128,000 | 65,536 |
| Open Source | No | No |
| Created | Jan 14, 2026 | Feb 25, 2026 |
GPT-5.2-Codex scores 85/100 (rank #32) compared to Qwen3.5-Flash's 79/100 (rank #80), giving it a 6-point advantage. GPT-5.2-Codex is the stronger overall choice, though Qwen3.5-Flash may excel in specific areas like cost efficiency.
GPT-5.2-Codex is ranked #32 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 GPT-5.2-Codex's $14.00/M output tokens - 53.8x more expensive. Input token pricing: GPT-5.2-Codex at $1.75/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to GPT-5.2-Codex's 400,000 tokens. A larger context window means the model can process longer documents and conversations.