| Signal | GPT-3.5 Turbo 16k | Delta | Llama 3.1 8B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 4 | +4 | |
Context window size | 67 | -- | |
Recency | 0 | -23 | |
Output Capacity | 60 | -10 | |
Benchmarks | 0 | -40 | |
| Overall Result | 1 wins | of 6 | 3 wins |
6
days ranked higher
2
days
22
days ranked higher
OpenAI
Meta
Llama 3.1 8B Instruct saves you $495.50/month
That's $5946.00/year compared to GPT-3.5 Turbo 16k at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo 16k | Llama 3.1 8B Instruct | Winner |
|---|---|---|---|
| Overall Score | 45 | 46 | Llama 3.1 8B Instruct |
| Rank | #262 | #260 | Llama 3.1 8B Instruct |
| Quality Rank | #262 | #260 | Llama 3.1 8B Instruct |
| Adoption Rank | #262 | #260 | Llama 3.1 8B Instruct |
| Parameters | -- | 8B | -- |
| Context Window | 16K | 16K | GPT-3.5 Turbo 16k |
| Pricing | $3.00/$4.00/M | $0.02/$0.05/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | GPT-3.5 Turbo 16k |
| Pricing | 4 | 0 | GPT-3.5 Turbo 16k |
| Context window size | 67 | 67 | GPT-3.5 Turbo 16k |
| Recency | 0 | 23 | Llama 3.1 8B Instruct |
| Output Capacity | 60 | 70 | Llama 3.1 8B Instruct |
| Benchmarks | -- | 40 | Llama 3.1 8B Instruct |
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 45/100 (rank #262), placing it in the top 10% of all 290 models tracked.
Scores 46/100 (rank #260), placing it in the top 11% 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.
Llama 3.1 8B Instruct offers 99% better value per quality point. At 1M tokens/day, you'd spend $1.05/month with Llama 3.1 8B Instruct vs $105.00/month with GPT-3.5 Turbo 16k — a $103.95 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. Llama 3.1 8B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (16K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.05/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (46/100) correlates with better nuance, coherence, and style in long-form content
GPT-3.5 Turbo 16k and Llama 3.1 8B Instruct are extremely close in overall performance (only 1.2000000000000028 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-3.5 Turbo 16k
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 8B Instruct
99% 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 | Llama 3.1 8B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Meta
Llama 3.1 8B Instruct saves you $10.10/month
That's 99% cheaper than GPT-3.5 Turbo 16k 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 | Llama 3.1 8B Instruct |
|---|---|---|
| Context Window | 16K | 16K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | Yes | Yes |
| Created | Aug 28, 2023 | Jul 23, 2024 |
Llama 3.1 8B Instruct scores 46/100 (rank #260) compared to GPT-3.5 Turbo 16k's 45/100 (rank #262), giving it a 1-point advantage. Llama 3.1 8B Instruct is the stronger overall choice, though GPT-3.5 Turbo 16k may excel in specific areas like certain benchmarks.
GPT-3.5 Turbo 16k is ranked #262 and Llama 3.1 8B Instruct is ranked #260 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.
Llama 3.1 8B Instruct is cheaper at $0.05/M output tokens vs GPT-3.5 Turbo 16k's $4.00/M output tokens — 80.0x more expensive. Input token pricing: GPT-3.5 Turbo 16k at $3.00/M vs Llama 3.1 8B Instruct at $0.02/M.
GPT-3.5 Turbo 16k has a larger context window of 16,385 tokens compared to Llama 3.1 8B Instruct's 16,384 tokens. A larger context window means the model can process longer documents and conversations.