| Signal | GPT-4o (2024-05-13) | Delta | Llama 3 8B Instruct |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 60 | +35 | |
Pricing | 15 | +15 | |
Context window size | 81 | +19 | |
Recency | 9 | +4 | |
Output Capacity | 60 | -10 | |
| Overall Result | 5 wins | of 6 | 1 wins |
30
days ranked higher
0
days
0
days ranked higher
OpenAI
Meta
Llama 3 8B Instruct saves you $1245.00/month
That's $14940.00/year compared to GPT-4o (2024-05-13) at your current usage level of 100K calls/month.
| Metric | GPT-4o (2024-05-13) | Llama 3 8B Instruct | Winner |
|---|---|---|---|
| Overall Score | 53 | 30 | GPT-4o (2024-05-13) |
| Rank | #258 | #301 | GPT-4o (2024-05-13) |
| Quality Rank | #258 | #301 | GPT-4o (2024-05-13) |
| Adoption Rank | #258 | #301 | GPT-4o (2024-05-13) |
| Parameters | -- | 8B | -- |
| Context Window | 128K | 8K | GPT-4o (2024-05-13) |
| Pricing | $5.00/$15.00/M | $0.03/$0.04/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | GPT-4o (2024-05-13) |
| Benchmarks | 60 | 24 | GPT-4o (2024-05-13) |
| Pricing | 15 | 0 | GPT-4o (2024-05-13) |
| Context window size | 81 | 62 | GPT-4o (2024-05-13) |
| Recency | 9 | 5 | GPT-4o (2024-05-13) |
| Output Capacity | 60 | 70 | Llama 3 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 53/100 (rank #258), placing it in the top 11% of all 290 models tracked.
Scores 30/100 (rank #301), placing it in the top -3% of all 290 models tracked.
GPT-4o (2024-05-13) has a 22-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Llama 3 8B Instruct offers 100% better value per quality point. At 1M tokens/day, you'd spend $1.05/month with Llama 3 8B Instruct vs $300.00/month with GPT-4o (2024-05-13) - a $298.95 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 8B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.04/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (53/100) 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-4o (2024-05-13) clearly outperforms Llama 3 8B Instruct with a significant 22.300000000000004-point lead. For most general use cases, GPT-4o (2024-05-13) is the stronger choice. However, Llama 3 8B Instruct may still excel in niche scenarios.
Best for Quality
GPT-4o (2024-05-13)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3 8B Instruct
100% lower pricing; better value at scale
Best for Reliability
GPT-4o (2024-05-13)
Higher uptime and faster response speeds
Best for Prototyping
GPT-4o (2024-05-13)
Stronger community support and better developer experience
Best for Production
GPT-4o (2024-05-13)
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4o (2024-05-13) | Llama 3 8B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Meta
Llama 3 8B Instruct saves you $26.90/month
That's 100% cheaper than GPT-4o (2024-05-13) 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-4o (2024-05-13) | Llama 3 8B Instruct |
|---|---|---|
| Context Window | 128K | 8K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | No | Yes |
| Created | May 13, 2024 | Apr 18, 2024 |
GPT-4o (2024-05-13) scores 53/100 (rank #258) compared to Llama 3 8B Instruct's 30/100 (rank #301), giving it a 22-point advantage. GPT-4o (2024-05-13) is the stronger overall choice, though Llama 3 8B Instruct may excel in specific areas like cost efficiency.
GPT-4o (2024-05-13) is ranked #258 and Llama 3 8B Instruct is ranked #301 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 8B Instruct is cheaper at $0.04/M output tokens vs GPT-4o (2024-05-13)'s $15.00/M output tokens - 375.0x more expensive. Input token pricing: GPT-4o (2024-05-13) at $5.00/M vs Llama 3 8B Instruct at $0.03/M.
GPT-4o (2024-05-13) has a larger context window of 128,000 tokens compared to Llama 3 8B Instruct's 8,192 tokens. A larger context window means the model can process longer documents and conversations.