| Signal | GPT-5.3-Codex | Delta | Qwen3.5 397B A17B |
|---|---|---|---|
Capabilities | 100 | +17 | |
Pricing | 14 | +12 | |
Context window size | 89 | +3 | |
Recency | 100 | -- | |
Output Capacity | 85 | +5 | |
Benchmarks | 0 | -73 | |
| Overall Result | 4 wins | of 6 | 1 wins |
20
days ranked higher
3
days
7
days ranked higher
OpenAI
Alibaba
Qwen3.5 397B A17B saves you $719.00/month
That's $8628.00/year compared to GPT-5.3-Codex at your current usage level of 100K calls/month.
| Metric | GPT-5.3-Codex | Qwen3.5 397B A17B | Winner |
|---|---|---|---|
| Overall Score | 85 | 82 | GPT-5.3-Codex |
| Rank | #29 | #65 | GPT-5.3-Codex |
| Quality Rank | #29 | #65 | GPT-5.3-Codex |
| Adoption Rank | #29 | #65 | GPT-5.3-Codex |
| Parameters | -- | 397B | -- |
| Context Window | 400K | 262K | GPT-5.3-Codex |
| Pricing | $1.75/$14.00/M | $0.39/$2.34/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | GPT-5.3-Codex |
| Pricing | 14 | 2 | GPT-5.3-Codex |
| Context window size | 89 | 86 | GPT-5.3-Codex |
| Recency | 100 | 100 | GPT-5.3-Codex |
| Output Capacity | 85 | 80 | GPT-5.3-Codex |
| Benchmarks | -- | 73 | Qwen3.5 397B A17B |
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 #29), placing it in the top 90% of all 290 models tracked.
Scores 82/100 (rank #65), placing it in the top 78% of all 290 models tracked.
With only a 3-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 397B A17B offers 83% better value per quality point. At 1M tokens/day, you'd spend $40.95/month with Qwen3.5 397B A17B vs $236.25/month with GPT-5.3-Codex - a $195.30 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 397B A17B 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 ($2.34/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-5.3-Codex has a moderate advantage with a 3.200000000000003-point lead in composite score. It wins on more signal dimensions, but Qwen3.5 397B A17B has specific strengths that could make it the better choice for certain workflows.
Best for Quality
GPT-5.3-Codex
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5 397B A17B
83% lower pricing; better value at scale
Best for Reliability
GPT-5.3-Codex
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.3-Codex
Stronger community support and better developer experience
Best for Production
GPT-5.3-Codex
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-5.3-Codex | Qwen3.5 397B A17B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
Alibaba
Qwen3.5 397B A17B saves you $16.44/month
That's 82% cheaper than GPT-5.3-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-5.3-Codex | Qwen3.5 397B A17B |
|---|---|---|
| Context Window | 400K | 262K |
| Max Output Tokens | 128,000 | 65,536 |
| Open Source | No | Yes |
| Created | Feb 24, 2026 | Feb 16, 2026 |
GPT-5.3-Codex scores 85/100 (rank #29) compared to Qwen3.5 397B A17B's 82/100 (rank #65), giving it a 3-point advantage. GPT-5.3-Codex is the stronger overall choice, though Qwen3.5 397B A17B may excel in specific areas like cost efficiency.
GPT-5.3-Codex is ranked #29 and Qwen3.5 397B A17B is ranked #65 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 397B A17B is cheaper at $2.34/M output tokens vs GPT-5.3-Codex's $14.00/M output tokens - 6.0x more expensive. Input token pricing: GPT-5.3-Codex at $1.75/M vs Qwen3.5 397B A17B at $0.39/M.
GPT-5.3-Codex has a larger context window of 400,000 tokens compared to Qwen3.5 397B A17B's 262,144 tokens. A larger context window means the model can process longer documents and conversations.