Rankings here combine tool use, terminal-task, and instruction-following signals β so the order reflects real agent workflow performance, not just general chat quality.
Agents need consistent function calling and strong recovery when tool outputs are imperfect.
Good agentic models can maintain a plan over several steps without collapsing into repetition.
In autonomous systems, slow thinking compounds. Speed and consistency matter as much as raw benchmark quality.
The models below are ranked for autonomous execution, tool use, and multi-step reliability.
| Rank | Model | Quality | Terminal-Bench | ΟΒ²-Bench | IFBench |
|---|---|---|---|---|---|
| 1 | GPT-5.5 (xhigh) OpenAI | 60.2 | N/A | N/A | N/A |
| 2 | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) Anthropic | 57.3 | N/A | N/A | N/A |
| 3 | Gemini 3.1 Pro Preview | 57.2 | N/A | N/A | N/A |
| 4 | GPT-5.4 (xhigh) OpenAI | 56.8 | N/A | N/A | N/A |
| 5 | Qwen3.7 Max Alibaba | 56.6 | N/A | N/A | N/A |
| 6 | Gemini 3.5 Flash (high) | 55.3 | N/A | N/A | N/A |
| 7 | Kimi K2.6 Kimi | 53.9 | N/A | N/A | N/A |
| 8 | MiMo-V2.5-Pro Xiaomi | 53.8 | N/A | N/A | N/A |
| 9 | GPT-5.3 Codex (xhigh) OpenAI | 53.6 | N/A | N/A | N/A |
| 10 | Grok 4.3 (high) xAI | 53.2 | N/A | N/A | N/A |
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, not just the one with the strongest chat benchmark.
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 or large-document reasoning.
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.
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 β 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 Hard, ΟΒ²-Bench, and IFBench are strong predictors of real agentic capability. This ranking weights these alongside general quality to surface the best models for autonomous tasks.