Best AI Models for Agents
Models ranked by Berkeley Function Calling Leaderboard (BFCL), structured output compliance, and multi-step execution.
Top Ranked Agentic Models
Ranked by BFCL AST accuracy and structured tool execution.
OpenAI o3-mini
Next-generation cost-efficient reasoning model optimized for STEM, competitive math, and coding.
200k tokens
Not Disclosed
$1.10 / $4.40 / MTok
Claude 3.5 Sonnet (v2)
Frontier-class workhorse model combining high-speed code generation, computer use, and visual reasoning.
200k tokens
Not Disclosed
$3.00 / $15.00 / MTok
DeepSeek-V3
Frontier open-weights 671B MoE base and chat model with multi-head latent attention (MLA) and dual-pipe training.
64k tokens
671B (37B active)
$0.14 / $0.28 / MTok
GPT-4o
OpenAI's omni-modal flagship model natively processing text, audio, images, and vision in real time.
128k tokens
Not Disclosed
$2.50 / $10.00 / MTok
Gemini 2.0 Flash
Google's high-speed multimodal workhorse with 1M token context, native tool use, and real-time audio/video streaming.
1M tokens
Not Disclosed
$0.10 / $0.40 / MTok
Llama 3.3 70B Instruct
Meta's open-weights 70B flagship matching Llama 3.1 405B capabilities on industry benchmarks at 1/5th the compute.
128k tokens
70B
$0.12 / $0.30 / MTok
Claude 3.7 Sonnet
Anthropic's first hybrid reasoning frontier model with dynamic thinking budget control and state-of-the-art coding capabilities.
200k tokens
Not Disclosed
$3.00 / $15.00 / MTok
Gemini 2.0 Pro (Experimental)
Google's flagship intelligence model engineered for complex coding, mathematical proofs, and 2M token context.
2M tokens
Not Disclosed
$0.00 / $0.00 / MTok
DeepSeek-R1
Open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold start.
64k tokens
671B (37B active)
$0.55 / $2.19 / MTok
Codestral 2501
Mistral AI's dedicated code generation model with 256k context window and fill-in-the-middle (FIM) capabilities.
256k tokens
22B
$0.30 / $0.90 / MTok
OpenAI o1
Flagship deep reasoning model trained with reinforcement learning for frontier science, math, and coding.
200k tokens
Not Disclosed
$15.00 / $60.00 / MTok
Qwen 2.5 Coder 32B Instruct
Alibaba's open-weights code generation specialist matching GPT-4o on coding benchmarks while fitting on a single GPU.
128k tokens
32.5B
$0.00 / $0.00 / MTok
Claude 3.5 Haiku
Ultra-fast and cost-efficient frontier intelligence model for high-throughput and sub-agent workflows.
200k tokens
Not Disclosed
$0.80 / $4.00 / MTok
Qwen 2.5 72B Instruct
Alibaba's flagship open foundation model with world-class multilingual, mathematical, and coding capabilities.
128k tokens
72.7B
$0.00 / $0.00 / MTok
Mistral Large 2 (2407)
Mistral AI's flagship 123B model specialized in multilingual reasoning, precision code generation, and agentic tool use.
128k tokens
123B
$2.00 / $6.00 / MTok
Llama 3.1 405B Instruct
Meta's largest open foundation model with 405 billion dense parameters, rivaling leading closed frontier models.
128k tokens
405B
$1.79 / $1.79 / MTok
GPT-4o mini
High-speed, ultra-affordable multimodal small model designed to replace GPT-3.5 Turbo at 60% lower cost.
128k tokens
Not Disclosed
$0.15 / $0.60 / MTok
Gemini 1.5 Pro
Enterprise workhorse foundation model with 2M context window, high-fidelity recall, and audio/video understanding.
2M tokens
Not Disclosed
$1.25 / $5.00 / MTok
Agent Evaluation FAQ
What makes a model good for AI Agents?
Agent models must excel at accurate tool calling (AST schema adherence), multi-turn state preservation, avoiding parameter hallucination, fast inference latency, and recovering gracefully from execution errors.
What is BFCL (Berkeley Function Calling Leaderboard)?
BFCL is the standard industry benchmark measuring an LLM's ability to invoke functions with correct parameter types, parse AST trees, and handle multi-turn conversational tool selection across REST and Python APIs.
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.