| Signal | GPT-4 | Delta | Llama 3 8B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 60 | +60 | |
Context window size | 62 | -- | |
Recency | 0 | -6 | |
Output Capacity | 60 | -10 | |
Benchmarks | 0 | -22 | |
| Overall Result | 1 wins | of 6 | 3 wins |
29
days ranked higher
1
days
0
days ranked higher
OpenAI
Meta
Llama 3 8B Instruct saves you $5995.00/month
That's $71940.00/year compared to GPT-4 at your current usage level of 100K calls/month.
| Metric | GPT-4 | Llama 3 8B Instruct | Winner |
|---|---|---|---|
| Overall Score | 44 | 37 | GPT-4 |
| Rank | #271 | #284 | GPT-4 |
| Quality Rank | #271 | #284 | GPT-4 |
| Adoption Rank | #271 | #284 | GPT-4 |
| Parameters | -- | 8B | -- |
| Context Window | 8K | 8K | Llama 3 8B Instruct |
| Pricing | $30.00/$60.00/M | $0.03/$0.04/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | GPT-4 |
| Pricing | 60 | 0 | GPT-4 |
| Context window size | 62 | 62 | GPT-4 |
| Recency | 0 | 6 | Llama 3 8B Instruct |
| Output Capacity | 60 | 70 | Llama 3 8B Instruct |
| Benchmarks | -- | 22 | 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 44/100 (rank #271), placing it in the top 7% of all 290 models tracked.
Scores 37/100 (rank #284), placing it in the top 2% of all 290 models tracked.
GPT-4 has a 7-point advantage, which typically translates to noticeably better 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 $1350.00/month with GPT-4 — a $1348.95 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 (8K 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 (44/100) correlates with better nuance, coherence, and style in long-form content
GPT-4 has a moderate advantage with a 7.299999999999997-point lead in composite score. It wins on more signal dimensions, but Llama 3 8B Instruct has specific strengths that could make it the better choice for certain workflows.
Best for Quality
GPT-4
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-4
Higher uptime and faster response speeds
Best for Prototyping
GPT-4
Stronger community support and better developer experience
Best for Production
GPT-4
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4 | Llama 3 8B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Meta
Llama 3 8B Instruct saves you $125.90/month
That's 100% cheaper than GPT-4 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-4 | Llama 3 8B Instruct |
|---|---|---|
| Context Window | 8K | 8K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | Yes | Yes |
| Created | May 28, 2023 | Apr 18, 2024 |
GPT-4 scores 44/100 (rank #271) compared to Llama 3 8B Instruct's 37/100 (rank #284), giving it a 7-point advantage. GPT-4 is the stronger overall choice, though Llama 3 8B Instruct may excel in specific areas like cost efficiency.
GPT-4 is ranked #271 and Llama 3 8B Instruct is ranked #284 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-4's $60.00/M output tokens — 1500.0x more expensive. Input token pricing: GPT-4 at $30.00/M vs Llama 3 8B Instruct at $0.03/M.
Llama 3 8B Instruct has a larger context window of 8,192 tokens compared to GPT-4's 8,191 tokens. A larger context window means the model can process longer documents and conversations.