| Signal | GPT-3.5 Turbo Instruct | Delta | autofixer-01 |
|---|---|---|---|
Capabilities | 33 | +17 | |
Pricing | 2 | +2 | |
Context window size | 57 | +57 | |
Recency | 0 | -99 | |
Output Capacity | 60 | +40 | |
| Overall Result | 4 wins | of 5 | 1 wins |
13
days higher
7
days
10
days higher
OpenAI
Vercel
autofixer-01 saves you $250.00/month
That's $3000.00/year compared to GPT-3.5 Turbo Instruct at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo Instruct | autofixer-01 | Winner |
|---|---|---|---|
| Overall Score | 35 | 34 | GPT-3.5 Turbo Instruct |
| Rank | #309 | #311 | GPT-3.5 Turbo Instruct |
| Quality Rank | #309 | #311 | GPT-3.5 Turbo Instruct |
| Adoption Rank | #309 | #311 | GPT-3.5 Turbo Instruct |
| Parameters | -- | -- | -- |
| Context Window | 4K | -- | -- |
| Pricing | $1.50/$2.00/M | Free | -- |
| Signal Scores | |||
| Capabilities | 33 | 17 | GPT-3.5 Turbo Instruct |
| Pricing | 2 | 0 | GPT-3.5 Turbo Instruct |
| Context window size | 57 | 0 | GPT-3.5 Turbo Instruct |
| Recency | 0 | 99 | autofixer-01 |
| Output Capacity | 60 | 20 | GPT-3.5 Turbo Instruct |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 35/100 (rank #309), placing it in the top -6% of all 290 models tracked.
Scores 34/100 (rank #311), placing it in the top -7% 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 (35/100) correlates with better nuance, coherence, and style in long-form content
GPT-3.5 Turbo Instruct and autofixer-01 are extremely close in overall performance (only 1.3999999999999986 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-3.5 Turbo Instruct
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 Instruct
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo Instruct
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo Instruct
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo Instruct | autofixer-01 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Vercel
autofixer-01 saves you $5.10/month
That's 100% cheaper than GPT-3.5 Turbo Instruct 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 Instruct | autofixer-01 |
|---|---|---|
| Context Window | 4K | -- |
| Max Output Tokens | 4,096 | -- |
| Open Source | No | No |
| Created | Sep 28, 2023 | Oct 1, 2025 |
GPT-3.5 Turbo Instruct scores 35/100 (rank #309) compared to autofixer-01's 34/100 (rank #311), giving it a 1-point advantage. GPT-3.5 Turbo Instruct is the stronger overall choice, though autofixer-01 may excel in specific areas like cost efficiency.
GPT-3.5 Turbo Instruct is ranked #309 and autofixer-01 is ranked #311 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
autofixer-01 is cheaper at $0.00/M output tokens vs GPT-3.5 Turbo Instruct's $2.00/M output tokens - 2000.0x more expensive. Input token pricing: GPT-3.5 Turbo Instruct at $1.50/M vs autofixer-01 at $0.00/M.
Context window information is available on the individual model pages.