Benchmark LLMs response, cost and response time
LLM vs Cost per input + output token ($)
Bar Graph Excel Sheet here
| Model | Provider | Cost per input + output token ($) |
|---|---|---|
| openrouter/mistralai/mistral-7b-instruct | openrouter | 0.0 |
| ollama/llama2 | ollama | 0.0 |
| ollama/llama2:13b | ollama | 0.0 |
| ollama/llama2:70b | ollama | 0.0 |
| ollama/llama2-uncensored | ollama | 0.0 |
| ollama/mistral | ollama | 0.0 |
| ollama/codellama | ollama | 0.0 |
| ollama/orca-mini | ollama | 0.0 |
| ollama/vicuna | ollama | 0.0 |
| perplexity/codellama-34b-instruct | perplexity | 0.0 |
| perplexity/llama-2-13b-chat | perplexity | 0.0 |
| perplexity/llama-2-70b-chat | perplexity | 0.0 |
| perplexity/mistral-7b-instruct | perplexity | 0.0 |
| perplexity/replit-code-v1.5-3b | perplexity | 0.0 |
| text-bison | vertex_ai-text-models | 0.00000025 |
| text-bison@001 | vertex_ai-text-models | 0.00000025 |
| chat-bison | vertex_ai-chat-models | 0.00000025 |
| chat-bison@001 | vertex_ai-chat-models | 0.00000025 |
| chat-bison-32k | vertex_ai-chat-models | 0.00000025 |
| code-bison | vertex_ai-code-text-models | 0.00000025 |
| code-bison@001 | vertex_ai-code-text-models | 0.00000025 |
| code-gecko@001 | vertex_ai-chat-models | 0.00000025 |
| code-gecko@latest | vertex_ai-chat-models | 0.00000025 |
| codechat-bison | vertex_ai-code-chat-models | 0.00000025 |
| codechat-bison@001 | vertex_ai-code-chat-models | 0.00000025 |
| codechat-bison-32k | vertex_ai-code-chat-models | 0.00000025 |
| palm/chat-bison | palm | 0.00000025 |
| palm/chat-bison-001 | palm | 0.00000025 |
| palm/text-bison | palm | 0.00000025 |
| palm/text-bison-001 | palm | 0.00000025 |
| palm/text-bison-safety-off | palm | 0.00000025 |
| palm/text-bison-safety-recitation-off | palm | 0.00000025 |
| anyscale/meta-llama/Llama-2-7b-chat-hf | anyscale | 0.0000003 |
| anyscale/mistralai/Mistral-7B-Instruct-v0.1 | anyscale | 0.0000003 |
| openrouter/meta-llama/llama-2-13b-chat | openrouter | 0.0000004 |
| openrouter/nousresearch/nous-hermes-llama2-13b | openrouter | 0.0000004 |
| deepinfra/meta-llama/Llama-2-7b-chat-hf | deepinfra | 0.0000004 |
| deepinfra/mistralai/Mistral-7B-Instruct-v0.1 | deepinfra | 0.0000004 |
| anyscale/meta-llama/Llama-2-13b-chat-hf | anyscale | 0.0000005 |
| amazon.titan-text-lite-v1 | bedrock | 0.0000007 |
| deepinfra/meta-llama/Llama-2-13b-chat-hf | deepinfra | 0.0000007 |
| text-babbage-001 | text-completion-openai | 0.0000008 |
| text-ada-001 | text-completion-openai | 0.0000008 |
| babbage-002 | text-completion-openai | 0.0000008 |
| openrouter/google/palm-2-chat-bison | openrouter | 0.000001 |
| openrouter/google/palm-2-codechat-bison | openrouter | 0.000001 |
| openrouter/meta-llama/codellama-34b-instruct | openrouter | 0.000001 |
| deepinfra/codellama/CodeLlama-34b-Instruct-hf | deepinfra | 0.0000012 |
| deepinfra/meta-llama/Llama-2-70b-chat-hf | deepinfra | 0.0000016499999999999999 |
| deepinfra/jondurbin/airoboros-l2-70b-gpt4-1.4.1 | deepinfra | 0.0000016499999999999999 |
| anyscale/meta-llama/Llama-2-70b-chat-hf | anyscale | 0.000002 |
| anyscale/codellama/CodeLlama-34b-Instruct-hf | anyscale | 0.000002 |
| gpt-3.5-turbo-1106 | openai | 0.000003 |
| openrouter/meta-llama/llama-2-70b-chat | openrouter | 0.000003 |
| amazon.titan-text-express-v1 | bedrock | 0.000003 |
| gpt-3.5-turbo | openai | 0.0000035 |
| gpt-3.5-turbo-0301 | openai | 0.0000035 |
| gpt-3.5-turbo-0613 | openai | 0.0000035 |
| gpt-3.5-turbo-instruct | text-completion-openai | 0.0000035 |
| openrouter/openai/gpt-3.5-turbo | openrouter | 0.0000035 |
| cohere.command-text-v14 | bedrock | 0.0000035 |
| gpt-3.5-turbo-0613 | openai | 0.0000035 |
| claude-instant-1 | anthropic | 0.00000714 |
| claude-instant-1.2 | anthropic | 0.00000714 |
| openrouter/anthropic/claude-instant-v1 | openrouter | 0.00000714 |
| anthropic.claude-instant-v1 | bedrock | 0.00000714 |
| openrouter/mancer/weaver | openrouter | 0.00001125 |
| j2-mid | ai21 | 0.00002 |
| ai21.j2-mid-v1 | bedrock | 0.000025 |
| openrouter/jondurbin/airoboros-l2-70b-2.1 | openrouter | 0.00002775 |
| command-nightly | cohere | 0.00003 |
| command | cohere | 0.00003 |
| command-light | cohere | 0.00003 |
| command-medium-beta | cohere | 0.00003 |
| command-xlarge-beta | cohere | 0.00003 |
| j2-ultra | ai21 | 0.00003 |
| ai21.j2-ultra-v1 | bedrock | 0.0000376 |
| gpt-4-1106-preview | openai | 0.00004 |
| gpt-4-vision-preview | openai | 0.00004 |
| claude-2 | anthropic | 0.0000437 |
| openrouter/anthropic/claude-2 | openrouter | 0.0000437 |
| anthropic.claude-v1 | bedrock | 0.0000437 |
| anthropic.claude-v2 | bedrock | 0.0000437 |
| gpt-4 | openai | 0.00009 |
| gpt-4-0314 | openai | 0.00009 |
| gpt-4-0613 | openai | 0.00009 |
| openrouter/openai/gpt-4 | openrouter | 0.00009 |
| gpt-4-32k | openai | 0.00018 |
| gpt-4-32k-0314 | openai | 0.00018 |
| gpt-4-32k-0613 | openai | 0.00018 |
git clone https://github.com/BerriAI/litellm
cd to benchmark dir
cd litellm/cookbook/benchmark
pip install litellm click tqdm tabulate termcolor
In benchmark/benchmark.py select your LLMs, LLM API Key and questions
Supported LLMs: https://docs.litellm.ai/docs/providers
# Define the list of models to benchmark
models = ['gpt-3.5-turbo', 'togethercomputer/llama-2-70b-chat', 'claude-2']
# Enter LLM API keys
os.environ['OPENAI_API_KEY'] = ""
os.environ['ANTHROPIC_API_KEY'] = ""
os.environ['TOGETHERAI_API_KEY'] = ""
# List of questions to benchmark (replace with your questions)
questions = [
"When will BerriAI IPO?",
"When will LiteLLM hit $100M ARR?"
]python3 benchmark.py
Running question: When will BerriAI IPO? for model: claude-2: 100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:13<00:00, 4.41s/it]
Benchmark Results for 'When will BerriAI IPO?':
+-----------------+----------------------------------------------------------------------------------+---------------------------+------------+
| Model | Response | Response Time (seconds) | Cost ($) |
+=================+==================================================================================+===========================+============+
| gpt-3.5-turbo | As an AI language model, I cannot provide up-to-date information or predict | 1.55 seconds | $0.000122 |
| | future events. It is best to consult a reliable financial source or contact | | |
| | BerriAI directly for information regarding their IPO plans. | | |
+-----------------+----------------------------------------------------------------------------------+---------------------------+------------+
| togethercompute | I'm not able to provide information about future IPO plans or dates for BerriAI | 8.52 seconds | $0.000531 |
| r/llama-2-70b-c | or any other company. IPO (Initial Public Offering) plans and timelines are | | |
| hat | typically kept private by companies until they are ready to make a public | | |
| | announcement. It's important to note that IPO plans can change and are subject | | |
| | to various factors, such as market conditions, financial performance, and | | |
| | regulatory approvals. Therefore, it's difficult to predict with certainty when | | |
| | BerriAI or any other company will go public. If you're interested in staying | | |
| | up-to-date with BerriAI's latest news and developments, you may want to follow | | |
| | their official social media accounts, subscribe to their newsletter, or visit | | |
| | their website periodically for updates. | | |
+-----------------+----------------------------------------------------------------------------------+---------------------------+------------+
| claude-2 | I do not have any information about when or if BerriAI will have an initial | 3.17 seconds | $0.002084 |
| | public offering (IPO). As an AI assistant created by Anthropic to be helpful, | | |
| | harmless, and honest, I do not have insider knowledge about Anthropic's business | | |
| | plans or strategies. | | |
+-----------------+----------------------------------------------------------------------------------+---------------------------+------------+
🤝 Schedule a 1-on-1 Session: Book a 1-on-1 session with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.