| Signal | GPT-4 | Delta | Llama 3.1 70B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Benchmarks | 66 | -6 | |
Pricing | 40 | -60 | |
Context window size | 56 | -17 | |
Recency | 0 | -5 | |
Output Capacity | 60 | -10 | |
| Overall Result | 0 wins | of 6 | 5 wins |
Score History
64.5
current score
Llama 3.1 70B Instruct
right now
64.9
current score
OpenAI
Meta
Llama 3.1 70B Instruct saves you $5940.00/month
That's $71280.00/year compared to GPT-4 at your current usage level of 100K calls/month.
| Metric | GPT-4 | Llama 3.1 70B Instruct | Winner |
|---|---|---|---|
| Overall Score | 65 | 65 | Llama 3.1 70B Instruct |
| Rank | #126 | #125 | Llama 3.1 70B Instruct |
| Quality Rank | #126 | #125 | Llama 3.1 70B Instruct |
| Adoption Rank | #126 | #125 | Llama 3.1 70B Instruct |
| Parameters | -- | 70B | -- |
| Context Window | 8K | 131K | Llama 3.1 70B Instruct |
| Pricing | $30.00/$60.00/M | $0.40/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | GPT-4 |
| Benchmarks | 66 | 72 | Llama 3.1 70B Instruct |
| Pricing | 40 | 100 | Llama 3.1 70B Instruct |
| Context window size | 56 | 73 | Llama 3.1 70B Instruct |
| Recency | 0 | 5 | Llama 3.1 70B Instruct |
| Output Capacity | 60 | 70 | Llama 3.1 70B 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%). Learn more about our methodology.
Scores 65/100 (rank #126), placing it in the top 57% of all 290 models tracked.
Scores 65/100 (rank #125), placing it in the top 57% of all 290 models tracked.
With only a 0-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 70B Instruct offers 99% better value per quality point. At 1M tokens/day, you'd spend $12.00/month with Llama 3.1 70B Instruct vs $1350.00/month with GPT-4 - a $1338.00 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Llama 3.1 70B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.40/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (65/100) correlates with better nuance, coherence, and style in long-form content
GPT-4 and Llama 3.1 70B Instruct are extremely close in overall performance (only 0.4000000000000057 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 70B Instruct
99% 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 3.1 70B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Meta
Llama 3.1 70B Instruct saves you $124.80/month
That's 99% 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 3.1 70B Instruct |
|---|---|---|
| Context Window | 8K | 131K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | No | Yes |
| Created | May 28, 2023 | Jul 23, 2024 |