Live AI Model Inference Rates & Latency Benchmarks
The autonomous knowledge engine tracking real-world inference costs, TTFT latency, and reasoning benchmarks across every major AI provider.
What are the best value and lowest cost AI models in 2026?
🏆 Inference Value Ratio (IVR) Leaderboard
Proprietary metric combining coding (SWE-bench), reasoning (MMLU-Pro), and blended token unit economics.
DeepSeek-V3
DeepSeekFrontier foundation model developed by DeepSeek featuring Multi-head Latent Attention MoE (671B/37B) architecture.
DeepSeek-R1
DeepSeekFrontier foundation model developed by DeepSeek featuring Open-Weights Reasoning MoE (671B/37B) architecture.
OpenAI GPT-4o Mini
OpenAIFrontier foundation model developed by OpenAI featuring Distilled Omni architecture.
Gemini 2.0 Flash
GoogleFrontier foundation model developed by Google featuring Native Multimodal Transformer architecture.
Llama 3.3 70B
MetaFrontier foundation model developed by Meta featuring Dense Auto-regressive architecture.
Qwen 2.5 72B Instruct
Alibaba CloudOpen-source flagship model renowned for exceptional multilingual capabilities, math problem solving, and structured tabular extraction.
🧮 Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.14/1M in · $0.28/1M out
$3/1M in · $15/1M out
⚔️ Trending Head-to-Head Comparisons
Machine-synthesized direct comparisons evaluating cost differentials, latency, and coding capabilities.
Claude 3.5 Haiku vs Claude 3.5 Sonnet
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (180ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Claude 3.7 Sonnet
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (170ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Claude Opus 5
Claude 3.5 Haiku is 84.0% cheaper for input tokens ($0.80 vs. $5.00 per 1M tokens) and $4.00 vs. $25.00 for output tokens (6.2x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (200ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Codestral 25.01
Codestral 25.01 is 62.5% cheaper for input tokens ($0.30 vs. $0.80 per 1M tokens) and $0.90 vs. $4.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (10ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Composer 2.5
Claude 3.5 Haiku is 55.6% cheaper for input tokens ($0.80 vs. $1.80 per 1M tokens) and $4.00 vs. $7.20 for output tokens (2.2x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (40ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-R1
DeepSeek-R1 is 31.2% cheaper for input tokens ($0.55 vs. $0.80 per 1M tokens) and $2.19 vs. $4.00 for output tokens (1.5x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (480ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-V3
DeepSeek-V3 is 82.5% cheaper for input tokens ($0.14 vs. $0.80 per 1M tokens) and $0.28 vs. $4.00 for output tokens (5.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (200ms faster than DeepSeek-V3). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 82.5% cheaper for input tokens ($0.14 vs. $0.80 per 1M tokens) and $0.56 vs. $4.00 for output tokens (5.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (10ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
🌐 Cloud API Provider Latency & Uptime Radar
Real-time latency metrics and verified uptime across leading inference hosts.