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GPT-3.5 Turbo vs Llama 3.1 405B Instruct
Comprehensive side-by-side LLM comparison
Llama 3.1 405B Instruct leads with 20.7% higher average benchmark score. Llama 3.1 405B Instruct offers 235.5K more tokens in context window than GPT-3.5 Turbo. Both models have similar pricing. Llama 3.1 405B Instruct is available on 8 providers. Overall, Llama 3.1 405B Instruct is the stronger choice for coding tasks.
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OpenAI
GPT-3.5 Turbo was developed as an optimized version of GPT-3.5, designed to provide a balance of capability and efficiency for conversational and completion tasks. Built to serve as a cost-effective option for applications requiring reliable language understanding and generation, it became widely adopted for chatbots, content generation, and general-purpose AI assistance.
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Meta
Llama 3.1 405B was developed as Meta's largest open-source language model, designed to provide frontier-level capabilities with 405 billion parameters. Built to demonstrate that open-source models can match proprietary systems in capability, it enables researchers and developers to experiment with and deploy a powerful foundation model without licensing restrictions.
1 year newer
GPT-3.5 Turbo
OpenAI
2023-03-21
Llama 3.1 405B Instruct
Meta
2024-07-23
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Pricing Comparison
Cost per million tokens (USD)
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GPT-3.5 Turbo
Input:$0.50
Output:$1.50
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Llama 3.1 405B Instruct
Input:$0.89
Output:$0.89($0.22 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 5 common benchmarks
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GPT-3.5 Turbo
Average Score:56.4%
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Llama 3.1 405B Instruct
Average Score:77.1%(+20.7%)
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Knowledge Cutoff
Training data recency comparison
GPT-3.5 Turbo
2021-09-30
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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GPT-3.5 Turbo
2 providers
Azure
Throughput: 90 tok/s
Latency: 0.8ms
OpenAI
Throughput: 100 tok/s
Latency: 0.5ms
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GPT-3.5 Turbo
Avg Score:56.4%
Providers:2
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Llama 3.1 405B Instruct
Avg Score:77.1%(+20.7%)
Providers:8