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{
"model_name": "gpt-4o",
"provider": "openai",
"input_cost_per_1k": 0.0025,
"output_cost_per_1k": 0.010,
"context_window": 128000,
"reasoning_score": 0.92,
"coding_score": 0.93,
"math_score": 0.90,
"instruction_following_score": 0.95,
"latency_score": 0.70,
"max_complexity": 1.0,
"notes": "Flagship multimodal model. Best quality/cost for hard tasks."
},
{
"model_name": "gpt-4o-mini",
"provider": "openai",
"input_cost_per_1k": 0.000150,
"output_cost_per_1k": 0.000600,
"context_window": 128000,
"reasoning_score": 0.78,
"coding_score": 0.78,
"math_score": 0.72,
"instruction_following_score": 0.85,
"latency_score": 0.90,
"max_complexity": 0.75,
"notes": "Cost-efficient small model for lightweight tasks."
},
{
"model_name": "gpt-3.5-turbo",
"provider": "openai",
"input_cost_per_1k": 0.0005,
"output_cost_per_1k": 0.0015,
"context_window": 16385,
"reasoning_score": 0.62,
"coding_score": 0.65,
"math_score": 0.55,
"instruction_following_score": 0.75,
"latency_score": 0.92,
"max_complexity": 0.60,
"notes": "Legacy fast model. Good for simple chat tasks."
},
{
"model_name": "claude-3-5-haiku-20241022",
"provider": "anthropic",
"input_cost_per_1k": 0.00080,
"output_cost_per_1k": 0.00400,
"context_window": 200000,
"reasoning_score": 0.78,
"coding_score": 0.80,
"math_score": 0.75,
"instruction_following_score": 0.85,
"latency_score": 0.92,
"max_complexity": 0.75,
"notes": "Fast, affordable Anthropic model for everyday tasks."
},
{
"model_name": "claude-3-5-sonnet-20241022",
"provider": "anthropic",
"input_cost_per_1k": 0.003,
"output_cost_per_1k": 0.015,
"context_window": 200000,
"reasoning_score": 0.93,
"coding_score": 0.95,
"math_score": 0.88,
"instruction_following_score": 0.96,
"latency_score": 0.75,
"max_complexity": 1.0,
"notes": "Top-tier coding and reasoning model from Anthropic."
},
{
"model_name": "claude-3-haiku-20240307",
"provider": "anthropic",
"input_cost_per_1k": 0.00025,
"output_cost_per_1k": 0.00125,
"context_window": 200000,
"reasoning_score": 0.65,
"coding_score": 0.65,
"math_score": 0.60,
"instruction_following_score": 0.75,
"latency_score": 0.95,
"max_complexity": 0.60,
"notes": "Cheapest Anthropic model. Good for classification, summarization."
},
{
"model_name": "gemini-1.5-flash",
"provider": "google",
"input_cost_per_1k": 0.000075,
"output_cost_per_1k": 0.000300,
"context_window": 1000000,
"reasoning_score": 0.74,
"coding_score": 0.74,
"math_score": 0.70,
"instruction_following_score": 0.78,
"latency_score": 0.88,
"max_complexity": 0.72,
"notes": "Extremely cheap and fast. Long context support."
},
{
"model_name": "gemini-1.5-pro",
"provider": "google",
"input_cost_per_1k": 0.00125,
"output_cost_per_1k": 0.005,
"context_window": 2000000,
"reasoning_score": 0.88,
"coding_score": 0.87,
"math_score": 0.85,
"instruction_following_score": 0.90,
"latency_score": 0.72,
"max_complexity": 0.95,
"notes": "Massive context window. Great for long-doc analysis."
},
{
"model_name": "mistral-small-latest",
"provider": "mistral",
"input_cost_per_1k": 0.001,
"output_cost_per_1k": 0.003,
"context_window": 32000,
"reasoning_score": 0.68,
"coding_score": 0.70,
"math_score": 0.62,
"instruction_following_score": 0.75,
"latency_score": 0.88,
"max_complexity": 0.65,
"notes": "Cost-effective European model."
},
{
"model_name": "mistral-large-latest",
"provider": "mistral",
"input_cost_per_1k": 0.003,
"output_cost_per_1k": 0.009,
"context_window": 128000,
"reasoning_score": 0.85,
"coding_score": 0.84,
"math_score": 0.80,
"instruction_following_score": 0.88,
"latency_score": 0.75,
"max_complexity": 0.90,
"notes": "Strong European flagship model."
},
{
"model_name": "llama3.2:3b",
"provider": "ollama",
"input_cost_per_1k": 0.0,
"output_cost_per_1k": 0.0,
"context_window": 128000,
"reasoning_score": 0.50,
"coding_score": 0.48,
"math_score": 0.42,
"instruction_following_score": 0.60,
"latency_score": 0.95,
"max_complexity": 0.45,
"notes": "Free local model. Use for simple/private tasks."
},
{
"model_name": "llama3.1:8b",
"provider": "ollama",
"input_cost_per_1k": 0.0,
"output_cost_per_1k": 0.0,
"context_window": 128000,
"reasoning_score": 0.65,
"coding_score": 0.64,
"math_score": 0.58,
"instruction_following_score": 0.72,
"latency_score": 0.85,
"max_complexity": 0.62,
"notes": "Free local model with decent reasoning."
},
{
"model_name": "llama3.1:70b",
"provider": "ollama",
"input_cost_per_1k": 0.0,
"output_cost_per_1k": 0.0,
"context_window": 128000,
"reasoning_score": 0.82,
"coding_score": 0.82,
"math_score": 0.78,
"instruction_following_score": 0.85,
"latency_score": 0.55,
"max_complexity": 0.85,
"notes": "Free local large model. Needs beefy hardware."
},
{
"model_name": "deepseek-chat",
"provider": "deepseek",
"input_cost_per_1k": 0.00014,
"output_cost_per_1k": 0.00028,
"context_window": 64000,
"reasoning_score": 0.88,
"coding_score": 0.90,
"math_score": 0.92,
"instruction_following_score": 0.85,
"latency_score": 0.72,
"max_complexity": 0.92,
"notes": "Exceptional value model especially strong in math and code."
}
]
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