Best LLM for AI Agents
Ranked on multi-step reasoning, tool-use reliability, and long-horizon stability. Agentic workloads amplify small accuracy gaps.
Updated April 2026. Top 3 this month: DeepSeek: R1 0528, Qwen: Qwen3.5 Plus 2026-02-15, DeepSeek: DeepSeek V3.
How we rank
Agents chain dozens of tool calls per run. Even a 95%-reliable tool-use model compounds down to near-zero after 20 steps, so the gap between the top model and the runner-up matters a lot. We weight SWE-Bench Verified heavily because it is the best proxy for long-horizon agentic success, then reasoning benchmarks, then price.
Pillars and weights: SWE-Bench Verified (40%) · AgentBench (30%) · MMLU (15%) · price (15%). Our full methodology is published on the methodology page.
Top ranked models
| Rank | Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|---|
| 1 | DeepSeek: R1 0528 | DeepSeek | $0.50 | $2.15 | 163,840 |
| 2 | Qwen: Qwen3.5 Plus 2026-02-15 | Qwen | $0.26 | $1.56 | 1,000,000 |
| 3 | DeepSeek: DeepSeek V3 | DeepSeek | $0.32 | $0.89 | 163,840 |
| 4 | Qwen: Qwen3.5 397B A17B | Qwen | $0.39 | $2.34 | 262,144 |
| 5 | Tencent: Hunyuan A13B Instruct | Tencent | $0.14 | $0.57 | 131,072 |
| 6 | MiniMax: MiniMax M2.1 | MiniMax | $0.29 | $0.95 | 196,608 |
| 7 | Arcee AI: Trinity Large Preview | Arcee AI | $0.00 | $0.00 | 131,000 |
| 8 | OpenAI: GPT-4o (2024-11-20) | OpenAI | $2.50 | $10.00 | 128,000 |
| 9 | MiniMax: MiniMax-01 | MiniMax | $0.20 | $1.10 | 1,000,192 |
| 10 | Anthropic: Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | 1,000,000 |
Tips for ai agents
- Plan for retries. Instrument every tool call with structured logging and a budget ceiling.
- Prefer models with native structured-output mode to avoid JSON-fixup loops.
- Cache system prompts aggressively — agentic flows repeat the same preamble many times.
Frequently asked questions
Which LLM is best for agents?
As of April 2026, our weighted top 3 are DeepSeek: R1 0528, Qwen: Qwen3.5 Plus 2026-02-15, DeepSeek: DeepSeek V3.
How much does accuracy matter at each step?
A lot. A 2% per-step improvement can double end-to-end reliability on a 20-step task. Prefer the top-tier model for agent loops and a cheaper model for one-shot tasks.
Do open-weight models keep up for agents?
Open-weight models are catching up on tool use but still trail the frontier for long-horizon agents. Evaluate on your actual task before committing.
Related tasks
Want to model your own workload? Use the volume and switch-cost calculators on the main tool page. Sign in with Google to unlock compare-my-prompt with real tokenizer counts.
Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.