Agentic model routing
Match a workload to models using Agentic Index, task-level cost, response time, benchmark signals, and context requirements. The shortlist is built from the same live Artificial Analysis data used across WhatLLM.
Agentic fit lab
Ranking changes as task shape, autonomy, context, and economics change.
Task
Autonomy needed
Context load
Optimize for
Best fit
OpenAI
Fit
99
Agentic
72
Cost / task
$0.668
Response
1.0m
Context
922K
Value route
DeepSeek
Fit
87
Task cost
$0.056
Response
1.1m
Very strong task economics
Fast route
Anthropic
Fit
99
Task cost
$1.78
Response
43.4s
Expensive, reserve for high-risk work
Open route
DeepSeek
Fit
87
Task cost
$0.056
Response
1.1m
Very strong task economics
Frontier map
23 high-fit models
37 with cost/task
GPT-5.5 (high)
OpenAI
Agentic
72
Task cost
$0.668
Shortlist
GPT-5.5 (high)
72 agentic score
99
fit
Claude Opus 4.8 (max)
77.8 agentic score
99
fit
GPT-5.5 (xhigh)
74.1 agentic score
97
fit
Claude Fable 5 (Max Effort, Opus 4.8 Fallback)
80.6 agentic score
96
fit
Claude Opus 4.7 (max)
71.3 agentic score
92
fit
GPT-5.5 (medium)
69.4 agentic score
92
fit
Qwen3.7 Max
66.6 agentic score
91
fit
| Model | Fit | Agentic | Task cost | Response | Context |
|---|---|---|---|---|---|
| GPT-5.5 (high) OpenAI · Proprietary | 99 | 72 | $0.668 | 1.0m | 922K |
| Claude Opus 4.8 (max) Anthropic · Proprietary | 99 | 77.8 | $1.78 | 43.4s | 1.0M |
| GPT-5.5 (xhigh) OpenAI · Proprietary | 97 | 74.1 | $0.993 | 2.1m | 922K |
| Claude Fable 5 (Max Effort, Opus 4.8 Fallback) Anthropic · Proprietary | 96 | 80.6 | $3.25 | N/A | 1.0M |
| Claude Opus 4.7 (max) Anthropic · Proprietary | 92 | 71.3 | $1.97 | 28.7s | 1.0M |
| GPT-5.5 (medium) OpenAI · Proprietary | 92 | 69.4 | N/A | 17.2s | 922K |
| Qwen3.7 Max Alibaba · Proprietary | 91 | 66.6 | $0.456 | 19.0s | 1.0M |
| GPT-5.4 (xhigh) OpenAI · Proprietary | 90 | 68 | $1.03 | 2.0m | 1.1M |
| Gemini 3.5 Flash (high) Google · Proprietary | 90 | 70.3 | $0.614 | 22.8s | 1.0M |
| DeepSeek V4 Pro (Reasoning, Max Effort) DeepSeek · Open | 87 | 67.2 | $0.056 | 1.1m | 1.0M |
A small per-token price gap can become a large bill when an agent runs many turns, calls tools, and carries long state. Cost per Intelligence Index task gives a cleaner decision unit than token price alone.
Frontier models are worth it when mistakes are expensive. For routine automation, a cheaper high-fit model can preserve most of the capability while cutting task cost sharply.
Model rankings
Browse the latest ranking pages for overall models, coding, open source, Ollama, long context, and agentic workflows.
Live ranking of the best overall AI models by quality, price, speed, and context window.
Current coding leaderboard using LiveCodeBench, Terminal-Bench, and SciCode.
Top open-weight models for self-hosting, Ollama, and low-cost API use.
Best local AI models by hardware tier for self-hosting on Macs, RTX GPUs, and workstations.
Ollama-first picks for coding, chat, reasoning, and low-friction local inference.
Best long-context models for large documents, codebases, and retrieval-heavy workflows.
Rankings for tool use, multi-step execution, and autonomous agent workflows.