Which LLM Should You Use?
Compare LLMs for research by benchmark performance, measured task cost and model size. Select a point to inspect its model, reasoning effort, measurements and source.
Which model setting gives the most capability for its measured cost?
Choose a benchmark below. The plotted model settings and both axes update together so that scores and costs always come from the same evaluation source. Upper-left is better.
Intelligence Index v5.0, reviewed 2026-11-01. Only published score/cost pairs from the same evaluation are shown.
Models without a comparable v5.0 result
GPT-5.6 Luna, GPT-5.6 Terra, GPT-5.6 Sol, GPT-6 Sol, Grok 4.6, Gemini 3.6 Flash, Gemini 3.1 Pro Preview.
Artificial Analysis Intelligence Index v5.0 versus weighted USD per task from the same evaluation. Each point is a published model / reasoning-effort setting.
Open weights · AA v5.0 measured sweep: Both published comparable v5.0 settings (Low and Max); High has no published AA score/cost pair. Custom GLM-5.3 license.
Intelligence Index v5.0 is plotted against weighted USD per evaluation task. Lines connect every published comparable effort setting; missing settings are omitted. The local-model reference rail encodes score only.
How does open-weight model size compare with intelligence?
Current benchmark scores plotted against model size. The Artificial Analysis view uses v5.0, with every published setting for the selected families. Settings without a v5.0 score are omitted. The LiveBench view shows the saved snapshot and its sync status. Upper-left is better.
Intelligence Index v5.0, reviewed 2026-11-01. Showing 21 models and 27 published settings. Large models include server-class references.
Tracked models without a v5.0 score
Qwen3.6 27B, Qwen3.6 35B-A3B, Gemma 4 26B A4B, Qwen3.5 9B, gpt-oss-20b, Gemma 4 E4B, Ministral 3 14B, Llama 4 Scout, Gemma 4 31B, Gemma 4 12B, gpt-oss-120b, Nemotron 3 Nano, Nemotron 3 Super, Nemotron 3 Nano Omni.
Current Artificial Analysis v5.0 scores versus total parameter count. Only published v5.0 scores are included; unavailable settings are omitted.
Select a model or focus a point to inspect its setting. All published points remain visible.
AA setting: Xhigh · Reasoning · 262k context · Apache 2.0 · Open weights · LiveBench measured; AA v5.0 measured sweep: Dense 27B multimodal open-weight model. All four published v5.0 settings are included; High is not published. The LiveBench row comes from the dated saved snapshot.
Total parameters describe the model-weight footprint; active parameters describe compute per token. Hundreds-of-billions and trillion-parameter models are server-class references. Practical memory requirements also depend on quantization, KV cache and runtime.
All published comparable v5.0 settings are shown. Vertical guides join settings at the same model size. The GPT-6 Astra rail encodes score only; equal displayed scores share a label.
The performance–cost plot pairs scores and task costs from the same evaluation. The open-weight size plot switches between total parameters and active parameters per token. Models with hundreds of billions to a trillion parameters include large-server references. Practical memory requirements also depend on quantization, context length and runtime.
AA measurements use Intelligence Index v5.0 throughout. LiveBench and Terminal-Bench-Science show their separately dated saved measurements and evaluation conditions. Scores and costs from different benchmarks are not directly comparable.
CC BY-NC 4.0 2026 © Shunichi Horigome.RSS