AI Wisdom

Free guide

AI Model Comparison

AI model comparison weighs quality, latency, cost, context window, tool use, multimodal capability, deployment model, privacy, and operational fit.

Current models side by side

Example cost is 1,000 requests of 1,000 input and 500 output tokens, with no caching or batch discounts. Prices come from our model registry, so confirm on the vendor page before you budget. For your own numbers, use the AI Cost Calculator.

ModelInput per 1M tokensOutput per 1M tokensExample costContextMax output
Llama 3.3 70B$0.18$0.18$0.27128K8K
Claude Haiku 5.5$0.10$0.50$0.351M128K
GPT-6 Luna$0.10$0.50$0.351.05M128K
Llama 4 Maverick$0.27$0.85$0.691M16K
DeepSeek V4 Flash$0.30$1.20$0.901M33K
Gemini 3.5 Flash-Lite$0.30$2.50$1.551M66K
Gemini 3.8 Flash$0.75$3.75$2.631M66K
DeepSeek V4 Pro$1.32$3.96$3.301M33K
GPT-6.1 Terra$1$5$3.501.05M128K
Grok 4.7$2$6$5500K33K
Qwen 3.8 Max$2$6$51M33K
Claude Sonnet 5.5$2$10$71M128K
GPT-6.1 Sol$2$10$71.05M128K
Gemini 4 Argon$2$10$71.05M66K
Claude Opus 5.5$4$20$141M128K
Claude Fable 5.1$10$50$351M128K
GPT-6 Astra$10$50$351.05M128K
gpt-oss-120bNot verifiedNot verifiedNot verified131K131K
Gemma 4Not verifiedNot verifiedNot verified128K8K
Mistral Small 4Not verifiedNot verifiedNot verified256K33K
Phi-4Not verifiedNot verifiedNot verified16K4K
Command R+Not verifiedNot verifiedNot verified128K4K
Muse SparkNot verifiedNot verifiedNot verified1M16K
Llama 4 ScoutNot verifiedNot verifiedNot verified10M16K

How to choose

From task to modelBenchmarks narrow the list. Only your own evals decide.
flowchart TD
  A{"What matters most for this task?"} -->|"Hard reasoning or agents"| B["Top tier model"]
  A -->|"Everyday product work"| C["Balanced tier model"]
  A -->|"High volume, simple tasks"| D["Fast, low-cost tier"]
  A -->|"Data must stay with you"| E["Open-weight model you host"]
  B --> F["Test on your own eval set"]
  C --> F
  D --> F
  E --> F
  F --> G["Route easy work to the cheaper model"]

Comparison dimensions

Production model selection should compare task quality, benchmark fit, token cost, throughput, p95 latency, context behavior, structured output support, tool calling, region availability, and vendor risk.

Practical rule

Start with a strong default model, route easy work to cheaper models, evaluate on your own golden dataset, and keep telemetry on every model decision.