| Signal | GPT-3.5 Turbo (older v0613) | Delta | autofixer-01 |
|---|---|---|---|
Capabilities | 50 | +33 | |
Pricing | 2 | +2 | |
Context window size | 57 | +57 | |
Recency | 0 | -100 | |
Output Capacity | 60 | +40 | |
| Overall Result | 4 wins | of 5 | 1 wins |
7
days higher
3
days
20
days higher
OpenAI
Vercel
autofixer-01 saves you $200.00/month
That's $2400.00/year compared to GPT-3.5 Turbo (older v0613) at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo (older v0613) | autofixer-01 | Winner |
|---|---|---|---|
| Overall Score | 38 | 39 | autofixer-01 |
| Rank | #298 | #297 | autofixer-01 |
| Quality Rank | #298 | #297 | autofixer-01 |
| Adoption Rank | #298 | #297 | autofixer-01 |
| Parameters | -- | -- | -- |
| Context Window | 4K | -- | -- |
| Pricing | $1.00/$2.00/M | Free | -- |
| Signal Scores | |||
| Capabilities | 50 | 17 | GPT-3.5 Turbo (older v0613) |
| Pricing | 2 | 0 | GPT-3.5 Turbo (older v0613) |
| Context window size | 57 | 0 | GPT-3.5 Turbo (older v0613) |
| Recency | 0 | 100 | autofixer-01 |
| Output Capacity | 60 | 20 | GPT-3.5 Turbo (older v0613) |
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 38/100 (rank #298), placing it in the top -2% of all 290 models tracked.
Scores 39/100 (rank #297), placing it in the top -2% of all 290 models tracked.
With only a 1-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.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
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. autofixer-01 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (4K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (39/100) correlates with better nuance, coherence, and style in long-form content
GPT-3.5 Turbo (older v0613) and autofixer-01 are extremely close in overall performance (only 0.7999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-3.5 Turbo (older v0613)
Marginally better benchmark scores; both are excellent
Best for Cost
autofixer-01
100% lower pricing; better value at scale
Best for Reliability
GPT-3.5 Turbo (older v0613)
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo (older v0613)
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo (older v0613)
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo (older v0613) | autofixer-01 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Vercel
autofixer-01 saves you $4.20/month
That's 100% cheaper than GPT-3.5 Turbo (older v0613) 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 (older v0613) | autofixer-01 |
|---|---|---|
| Context Window | 4K | -- |
| Max Output Tokens | 4,096 | -- |
| Open Source | No | No |
| Created | Jan 25, 2024 | Oct 1, 2025 |
autofixer-01 scores 39/100 (rank #297) compared to GPT-3.5 Turbo (older v0613)'s 38/100 (rank #298), giving it a 1-point advantage. autofixer-01 is the stronger overall choice, though GPT-3.5 Turbo (older v0613) may excel in specific areas like certain benchmarks.
GPT-3.5 Turbo (older v0613) is ranked #298 and autofixer-01 is ranked #297 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.
autofixer-01 is cheaper at $0.00/M output tokens vs GPT-3.5 Turbo (older v0613)'s $2.00/M output tokens - 2000.0x more expensive. Input token pricing: GPT-3.5 Turbo (older v0613) at $1.00/M vs autofixer-01 at $0.00/M.
Context window information is available on the individual model pages.