| Signal | GPT-3.5 Turbo 16k | Delta | GPT-5.3-Codex |
|---|---|---|---|
Capabilities | 50 | -50 | |
Pricing | 4 | -10 | |
Context window size | 67 | -22 | |
Recency | 0 | -100 | |
Output Capacity | 60 | -25 | |
| Overall Result | 0 wins | of 5 | 5 wins |
0
days ranked higher
0
days
30
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OpenAI
OpenAI
GPT-3.5 Turbo 16k saves you $375.00/month
That's $4500.00/year compared to GPT-5.3-Codex at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo 16k | GPT-5.3-Codex | Winner |
|---|---|---|---|
| Overall Score | 40 | 85 | GPT-5.3-Codex |
| Rank | #286 | #29 | GPT-5.3-Codex |
| Quality Rank | #286 | #29 | GPT-5.3-Codex |
| Adoption Rank | #286 | #29 | GPT-5.3-Codex |
| Parameters | -- | -- | -- |
| Context Window | 16K | 400K | GPT-5.3-Codex |
| Pricing | $3.00/$4.00/M | $1.75/$14.00/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 100 | GPT-5.3-Codex |
| Pricing | 4 | 14 | GPT-5.3-Codex |
| Context window size | 67 | 89 | GPT-5.3-Codex |
| Recency | 0 | 100 | GPT-5.3-Codex |
| Output Capacity | 60 | 85 | GPT-5.3-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 40/100 (rank #286), placing it in the top 2% of all 290 models tracked.
Scores 85/100 (rank #29), placing it in the top 90% of all 290 models tracked.
GPT-5.3-Codex has a 45-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
GPT-3.5 Turbo 16k offers 56% better value per quality point. At 1M tokens/day, you'd spend $105.00/month with GPT-3.5 Turbo 16k vs $236.25/month with GPT-5.3-Codex - a $131.25 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. GPT-3.5 Turbo 16k 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 ($4.00/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 clearly outperforms GPT-3.5 Turbo 16k with a significant 45.1-point lead. For most general use cases, GPT-5.3-Codex is the stronger choice. However, GPT-3.5 Turbo 16k may still excel in niche scenarios.
Best for Quality
GPT-3.5 Turbo 16k
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-3.5 Turbo 16k
56% lower pricing; better value at scale
Best for Reliability
GPT-3.5 Turbo 16k
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo 16k
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo 16k
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo 16k | GPT-5.3-Codex |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
OpenAI
GPT-3.5 Turbo 16k saves you $9.75/month
That's 49% 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-3.5 Turbo 16k | GPT-5.3-Codex |
|---|---|---|
| Context Window | 16K | 400K |
| Max Output Tokens | 4,096 | 128,000 |
| Open Source | No | No |
| Created | Aug 28, 2023 | Feb 24, 2026 |
GPT-5.3-Codex scores 85/100 (rank #29) compared to GPT-3.5 Turbo 16k's 40/100 (rank #286), giving it a 45-point advantage. GPT-5.3-Codex is the stronger overall choice, though GPT-3.5 Turbo 16k may excel in specific areas like cost efficiency.
GPT-3.5 Turbo 16k is ranked #286 and GPT-5.3-Codex is ranked #29 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.
GPT-3.5 Turbo 16k is cheaper at $4.00/M output tokens vs GPT-5.3-Codex's $14.00/M output tokens - 3.5x more expensive. Input token pricing: GPT-3.5 Turbo 16k at $3.00/M vs GPT-5.3-Codex at $1.75/M.
GPT-5.3-Codex has a larger context window of 400,000 tokens compared to GPT-3.5 Turbo 16k's 16,385 tokens. A larger context window means the model can process longer documents and conversations.