| Signal | GPT-5.2-Codex | Delta | Qwen3.5-35B-A3B |
|---|---|---|---|
Capabilities | 100 | +17 | |
Pricing | 14 | +13 | |
Context window size | 89 | +3 | |
Recency | 100 | -- | |
Output Capacity | 85 | +5 | |
| Overall Result | 4 wins | of 5 | 0 wins |
30
days ranked higher
0
days
0
days ranked higher
OpenAI
Alibaba
Qwen3.5-35B-A3B saves you $793.75/month
That's $9525.00/year compared to GPT-5.2-Codex at your current usage level of 100K calls/month.
| Metric | GPT-5.2-Codex | Qwen3.5-35B-A3B | Winner |
|---|---|---|---|
| Overall Score | 96 | 87 | GPT-5.2-Codex |
| Rank | #9 | #42 | GPT-5.2-Codex |
| Quality Rank | #9 | #42 | GPT-5.2-Codex |
| Adoption Rank | #9 | #42 | GPT-5.2-Codex |
| Parameters | -- | 35B | -- |
| Context Window | 400K | 262K | GPT-5.2-Codex |
| Pricing | $1.75/$14.00/M | $0.16/$1.30/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | GPT-5.2-Codex |
| Pricing | 14 | 1 | GPT-5.2-Codex |
| Context window size | 89 | 86 | GPT-5.2-Codex |
| Recency | 100 | 100 | GPT-5.2-Codex |
| Output Capacity | 85 | 80 | GPT-5.2-Codex |
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 96/100 (rank #9), placing it in the top 97% of all 290 models tracked.
Scores 87/100 (rank #42), placing it in the top 86% of all 290 models tracked.
GPT-5.2-Codex has a 9-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-35B-A3B offers 91% better value per quality point. At 1M tokens/day, you'd spend $21.94/month with Qwen3.5-35B-A3B vs $236.25/month with GPT-5.2-Codex — a $214.31 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. Qwen3.5-35B-A3B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (400K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (96/100) 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 8.799999999999997-point lead in composite score. It wins on more signal dimensions, but Qwen3.5-35B-A3B 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
Qwen3.5-35B-A3B
91% 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 | Qwen3.5-35B-A3B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Alibaba
Qwen3.5-35B-A3B saves you $18.10/month
That's 91% 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 | Qwen3.5-35B-A3B |
|---|---|---|
| Context Window | 400K | 262K |
| Max Output Tokens | 128,000 | 65,536 |
| Open Source | No | Yes |
| Created | Jan 14, 2026 | Feb 25, 2026 |
GPT-5.2-Codex scores 96/100 (rank #9) compared to Qwen3.5-35B-A3B's 87/100 (rank #42), giving it a 9-point advantage. GPT-5.2-Codex is the stronger overall choice, though Qwen3.5-35B-A3B may excel in specific areas like cost efficiency.
GPT-5.2-Codex is ranked #9 and Qwen3.5-35B-A3B is ranked #42 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.
Qwen3.5-35B-A3B is cheaper at $1.30/M output tokens vs GPT-5.2-Codex's $14.00/M output tokens — 10.8x more expensive. Input token pricing: GPT-5.2-Codex at $1.75/M vs Qwen3.5-35B-A3B at $0.16/M.
GPT-5.2-Codex has a larger context window of 400,000 tokens compared to Qwen3.5-35B-A3B's 262,144 tokens. A larger context window means the model can process longer documents and conversations.