Rankings here prioritize Agentic Index, terminal tasks, tool-use benchmarks, and cost per Intelligence Index task where available, so the order reflects real workflow tradeoffs instead of generic chat quality alone.
The ranking now leans harder into Terminal-Bench, τ-Bench, and GDPval-AA style workflows that test longer agent trajectories.
Token price alone misses long-horizon agent cost. Cost per Intelligence Index task gives a better unit-economics view.
Cached input pricing now matters for agent loops with repeated context, especially when prompts and tool traces get large.
Agentic fit lab
Task
Autonomy needed
Context load
Optimize for
Best fit
OpenAI
Fit
92
Agentic
51.8
Cost / task
$0.682
Response
1.0m
Context
1.0M
Value route
OpenAI
Fit
62
Task cost
$0.095
Response
10.5s
Good balance across score and economics
Fast route
OpenAI
Fit
88
Task cost
$0.453
Response
20.2s
Good balance across score and economics
Open route
Z AI
Fit
75
Task cost
$0.468
Response
13.8s
Good balance across score and economics
Frontier map
14 high-fit models
112 with cost/task
GPT-5.6 Sol (xhigh)
OpenAI
Agentic
51.8
Task cost
$0.682
Agentic Index
54
OpenAI
Agentic Index
52.8
Anthropic
Agentic Index
50.1
Kimi
The models below are ranked for autonomous execution, tool use, multi-step reliability, and task-level economics.
| Rank | Model | Agentic | Cost / task | Terminal-Bench | τ-Bench | GDPval-AA | Response |
|---|---|---|---|---|---|---|---|
| 1 | GPT-5.6 Sol (max) OpenAI | 54 | $1.04 | 66% | 85% | 1743 | 2.6m |
| 2 | Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) Anthropic | 52.8 | $2.75 | 63% | 99% | 1760 | 1.9m |
| 3 | Kimi K3 Kimi | 50.1 | $0.954 | N/A | N/A | 1684 | 1.1m |
| 4 | GPT-5.6 Terra (max) OpenAI | 47.4 | $0.825 | 58% | 86% | 1581 | 2.7m |
| 5 | Claude Opus 4.8 (Adaptive Reasoning, Max Effort) Anthropic | 47.2 | $1.80 | 58% | 94% | 1600 | 39.3s |
| 6 | Claude Sonnet 5 (Adaptive Reasoning, Max Effort) Anthropic | 46.7 | $1.53 | N/A | N/A | 1607 | 2.7m |
| 7 | Grok 4.5 (high) SpaceXAI | 45.7 | $0.312 | N/A | N/A | 1535 | 17.4s |
| 8 | GPT-5.6 Luna (max) OpenAI | 45.6 | $0.209 | N/A | N/A | 1584 | 1.4m |
| 9 | Gemini 3.5 Flash (medium) | 45.4 | N/A | 39% | 96% | N/A | 19.3s |
| 10 | GPT-5.5 (xhigh) OpenAI | 44.9 | $0.993 | 61% | 94% | 1493 | 1.4m |
If your agents browse, call APIs, run tools, or plan several steps ahead, start here rather than on a generic leaderboard. The best agentic model is the one that stays reliable under execution at a cost and response-time profile you can actually ship.
Once you have a shortlist, open the finalists on Compare and validate whether the provider you want can deliver the right price and latency profile.
Agentic performance overlaps with coding and long-context performance, but it is not the same thing. Use the related pages below if your use case is specialized around software work, model routing, or large-document reasoning.
Model rankings
Browse the latest ranking pages for overall models, coding, local hardware, 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.
Coding-focused local models mapped to 8GB, 24GB, 64GB, and server-class hardware.
Ollama-first picks for coding, chat, reasoning, and low-friction local inference.
Best long-context models for large documents, codebases, and retrieval-heavy workflows.
The live top-ranked model on this page is the best starting point. The right answer depends on whether you prioritize raw capability, reliability under tool use, or latency in production. Check the ranking table above for the current leader.
Agentic models can maintain plans across many steps, call tools reliably, follow complex instructions, and recover gracefully when a workflow hits an unexpected state.
Not always. Coding strength helps with tool-writing and structured output, but agentic performance also requires strong planning, tool orchestration, and error recovery.
Use benchmarks to get a shortlist, then run the finalists on the actual tasks your agent will perform. Production testing on real workflows is the only way to make the final call.
Yes — significantly. In multi-step agents, slow thinking compounds across many tool calls. A model that is 30% slower can double the wall-clock time of a complex workflow.
Terminal-Bench, τ-Bench, and GDPval-AA are strong predictors of real agentic capability. IFBench remains useful as a supplemental instruction-following signal, but it is no longer the main filter for frontier models.