Llm For Agents

LLMs for Agents: Which Model to Choose for your AI Agents in 2025

🔎 Why the choice of LLM changes everything for an AI agent

An AI agent doesn't just answer questions. It plans, executes, iterates, corrects its mistakes. This cycle of autonomous reasoning stresses the model in a radically different way than a classic chatbot.

In 2025, general benchmarks are no longer enough to predict a model's performance in agentic mode. An LLM can score 90/100 on MMLU and fail on a 3-step web search task. The reason: agentic work requires long chain-of-thought reasoning, tool management (function calling), and above all, the ability to recognize its own failures to bounce back.

Forbes points out in its 2025 review that work driven by autonomous agents is the major trend of the year. But this promise hinges on a crucial detail: the underlying model.

The essentials

• The agentic ranking differs significantly from the general ranking: Claude Mythos Preview dominates (100/100), followed by GPT-5.5 (98.2) and Gemini 3 Pro Deep Think (95.4) according to the llm-stats.com leaderboard.
• Three criteria make the difference for an agent: depth of reasoning, reliability of function calling, and stability over long contexts.
• Open source models (DeepSeek V4 Pro, Kimi K2.6, GLM-5) reach agentic scores of 80-88, paving the way for local and privacy-first deployments.

Recommended tools

What differentiates a good agentic LLM from a good chatbot

A chatbot receives a question, generates an answer, stops. An agent receives an objective, breaks it down into subtasks, calls tools, analyzes the results, decides on the next action, and iterates until resolution.

This loop requires three distinct skills that classic benchmarks measure poorly.

The first: planning. The model must break down a complex objective into sequential steps without supervision. Claude Mythos Preview excels here thanks to its architecture designed for distributed reasoning, which explains its perfect score of 100 on the llm-stats.com agentic ranking.

The second: reliable function calling. The agent must format its API calls correctly, handle return errors, and never hallucinate parameters. OpenAI's GPT-5.5 benefits from the most mature tool use ecosystem on the market, with native support for dozens of integrations.

The third: self-correction. When a step fails, the agent must diagnose why and adjust its plan. Google's Gemini 3 Pro Deep Think shines on this point thanks to its extended reasoning capability (deep thinking) that simulates multi-pass reflection.

NextGenAITool actually recommends testing a model in real agentic conditions before relying on general benchmarks. A high score on MMLU does not guarantee that an agent will know how to navigate the web or manipulate files.

Detailed ranking of the best LLMs for AI agents

The top trio: Claude Mythos, GPT-5.5, Gemini 3 Pro Deep Think

Claude Mythos Preview takes first place in the agentic ranking with a perfect score of 100/100 on llm-stats.com. Anthropic has clearly optimized this model for autonomous scenarios: better management of long cycles, increased tolerance to ambiguities in instructions, and particularly robust function calling. It is the default choice for complex agents.

OpenAI's GPT-5.5 follows at 98.2. Its main strength remains the ecosystem: native integration with OpenAI custom assistants, support for varied tools, and a massive developer user base. For an agent that needs to integrate into an existing stack, it is often the path of least resistance.

Gemini 3 Pro Deep Think (95.4) brings something unique: in-depth reasoning that takes the time to "think" before acting. Google designed this model for tasks requiring multi-step analysis with a massive context window. For agents that need to process long documents or entire codebases, it is a serious candidate.

The adaptive models: Claude Opus 4.7 and GPT-5.4 Pro

Claude Opus 4.7 Adaptive (94.3) offers an interesting approach: the model adapts its level of reasoning to the complexity of the task. For an agent that handles both simple queries and complex problems, this optimizes costs and latency without sacrificing quality on difficult tasks.

GPT-5.4 Pro (91.8) and its standard version (87.6) offer a good price/performance ratio for agents of intermediate complexity. OpenAI intelligently segments its range to cover different agentic budgets.

The open source challengers: DeepSeek, Kimi, GLM-5

This is where the market has evolved the most in 2025. DeepSeek V4 Pro reaches 88 in agentic mode (estimation based on its general scores of 88/84 and its documented reasoning capabilities). Its affordable price makes it a serious alternative for teams that want to deploy agents at scale without blowing up their API budget.

Moonshot AI's Kimi K2.6 (88.1 in agentic, 84 in general) stands out through its self-host availability. For organizations that cannot send their data to third-party APIs, this is a major asset. Its agentic score actually exceeds its general score, suggesting specific optimization for autonomous tasks.

Z.AI's GLM-5 (82 in agentic, reasoning) completes the open source trio. Less performant than DeepSeek or Kimi on complex tasks, it remains viable for simple agents with well-defined workflows. Palmer Consulting cites it among the models shaping the current AI landscape alongside the American giants.

How to choose according to your use case

Research and analysis agents

For an agent that navigates the web, synthesizes information, and produces reports, in-depth reasoning is paramount. Gemini 3 Pro Deep Think is an excellent choice thanks to its extended context window and its "deep think" mode that simulates thorough analysis.

Claude Mythos Preview remains the absolute reference if your agent needs to cross-reference numerous sources and produce a nuanced synthesis. Its function calling is more reliable than average for integrations with search engines and scraping APIs.

Code and development agents

Models excellent at code are not necessarily the best at pure agentic work. GPT-5.3 Codex (80 in agentic, 87 in general) is an interesting case: excellent for generating code, but its more moderate agentic score suggests limitations when it comes to autonomously planning an entire project.

For a complete development agent (that analyzes a repo, identifies bugs, proposes fixes, and implements them), Claude Mythos Preview or GPT-5.5 remain the most reliable options. Their ability to maintain a coherent action plan over long sequences of steps makes the difference.

Conversational agents and assistance

For an agent that interacts with end users (customer support, coaching, tutoring), the key criterion is the naturalness of the dialogue combined with the capacity for action. Claude Sonnet 4.6 (81.4 in agentic) offers a good balance: cheaper than premium models, smart enough to manage assistance workflows with escalations and action-taking.

xAI's Grok 4.1 (79 in agentic, 90 in general) can be relevant for agents integrated into the X/Twitter ecosystem, with native access to platform data.

Local and privacy-first agents

This is the use case that has benefited the most from open source in 2025. To deploy an agent locally, you need a model that runs on your hardware while maintaining decent agentic capabilities.

For this scenario, Kimi K2.6 in self-host (88.1 agentic) and GLM-5 (82 agentic) are among the best LLMs to run locally. If your machine has the necessary resources, Kimi K2.6 is clearly the recommended choice.

DeepSeek V4 Pro in self-host is also an option, but its hardware requirements are higher. Check compatibility with your configuration before committing.

DataScientist.fr recommends evaluating the quality/cost ratio based on your volume of agent calls. An agent that executes 10 steps per request consumes significantly more tokens than a simple chatbot.

Architecture: how to connect an LLM to an agent framework

The model doesn't do everything. The architecture around the LLM determines 50% of your agent's final performance.

The 5 agent patterns that work

All high-performing agents in 2025 fall into one of the 5 AI agent patterns identified in the literature: the reflector, the planner, the orchestrator, the evaluator, and the iterator. Each pattern taps into the underlying LLM differently.

The "reflector" pattern requires a model capable of honest self-evaluation. Claude Mythos Preview excels here. The "planner" pattern requires pure logical reasoning: GPT-5.5 and Gemini 3 Pro Deep Think are well-suited. The "orchestrator" pattern delegates subtasks and requires little deep reasoning but high formatting reliability: even an intermediate model like Claude Sonnet 4.6 can be suitable.

Configuring SOUL, AGENTS, and Skills

For advanced frameworks like OpenClaw, LLM configuration happens at multiple levels. The OpenClaw configuration guide (SOUL, AGENTS, and Skills) details how to assign different models to different layers of your agent.

In practice, you can use a premium model (Claude Mythos) for the planning layer (SOUL) and a lighter model (Claude Sonnet 4.6 or DeepSeek V4 Pro) for executing individual skills. This hybrid architecture optimizes costs without sacrificing overall decision quality.

Multi-agents: making multiple AIs collaborate

The most performant systems in 2025 do not rely on a single agent, but on multi-agent systems where multiple LLM instances collaborate. A "researcher" agent with Gemini 3 Pro Deep Think, a "writer" agent with Claude Mythos, and a "critic" agent with GPT-5.5, all orchestrated by a supervisor.

This approach leverages the strengths of each model rather than searching for a single perfect model. It complicates the architecture, but the results on complex tasks largely justify the investment. Frameworks like OpenClaw and AutoGPT natively support this multi-model architecture.

Performance, cost, and latency: the three trade-off variables

Execution speed and TTFT

An agent that calls a tool, waits for the response, thinks, calls another tool: each cycle adds latency. Time To First Token (TTFT) becomes critical in agentic mode.

Artificial Analysis measures these metrics for over 100 models. As a rule, deep reasoning models (Gemini 3 Pro Deep Think, Claude Opus 4.7 Adaptive) have a higher TTFT because they "think" before producing the first token. For agents where speed matters more than depth (real-time chat), GPT-5.4 or Claude Sonnet 4.6 are better choices.

Cost per agentic cycle

The true cost of an agent is not measured in tokens per request, but in tokens per complete cycle. An agent that requires 5 reasoning iterations + 3 tool calls easily consumes 10,000 to 50,000 tokens for a task that would seem simple in direct chat.

The OpenRouter ranking, based on real usage data from millions of developers, shows that cost is the primary selection criterion once the minimum quality threshold is reached. DeepSeek V4 Pro and Claude Sonnet 4.6 dominate on this criterion for intermediate use cases.

Context window and memory

Agents accumulate context throughout their iterations: reasoning history, tool results, intermediate states. An agent that has been running for 10 minutes can easily exceed 32K tokens of internal context.

Gemini 3 Pro Deep Think and GPT-5.5 offer the most generous context windows according to Artificial Analysis. For long-term agents, this is a decisive selection criterion. A model with a small context window will "forget" the initial steps of its plan and lose coherence.

24pm notes in its 2025 comparison that context management has become the number one criterion for enterprise agentic architectures, even ahead of the pure reasoning score.

French LLMs and agents: do you need a Francophone model?

For agents that interact in French with end users, the question arises. For data sovereignty constraints or specific Francophone use cases, the Meilleurs Llm Francais include models from Mistral and Francophone variants of large models.

In practice, top-tier agents (Claude Mythos, GPT-5.5) master French perfectly. Their function calling and agentic reasoning are not affected by the interface language. The quality of the French output is excellent.

The argument for a pure Francophone model becomes relevant only in two cases: data sovereignty constraints (government, banking, healthcare) and costs for simple agents where a Francophone open source model is sufficient.

For a complex agent that needs to reason in French, choosing a less performant model solely for linguistic reasons is a mistake. It is better to use Claude Mythos with French instructions than a Francophone model with an agentic score of 60.

❌ Common mistakes

Mistake 1: Choosing an LLM solely based on its overall score

A model can be excellent at Q&A and mediocre at agentic tasks. General rankings (MMLU, HumanEval) do not measure the ability to plan, call tools reliably, or self-correct. Always check the specific agentic score on llm-stats.com or Artificial Analysis.

Mistake 2: Neglecting cumulative latency

An agent makes 5 to 15 LLM calls per task. A TTFT of 2 seconds becomes 10 to 30 seconds of perceived latency. Test your agent end-to-end with the chosen model, not just in direct chat.

Mistake 3: Using a premium model for every subtask

In a multi-agent architecture, not all tasks require Claude Mythos. Delegate simple tasks (formatting, extraction) to lightweight models like Claude Sonnet 4.6 or DeepSeek V4 Pro High. Reserve the premium tier for planning and critical evaluation.

Mistake 4: Ignoring the cost of accumulated context

An agent that iterates 8 times with 5 tool calls can consume 30K tokens for a simple task. Calculate the cost per cycle, not per request. Low cost-per-token models (DeepSeek) become very attractive in agentic settings.

Mistake 5: Deploying a local agent without testing the hardware

Kimi K2.6 and GLM-5 in self-host mode require significant GPU resources. A local agent that constantly swaps will be slower than an API call to a cloud model. Measure real performance before choosing local for cost reasons.

❓ Frequently asked questions

What is the best LLM for a beginner agent?

Claude Sonnet 4.6 offers the best simplicity/performance ratio for getting started. Its agentic score of 81.4 is sufficient for single-task agents with 2-3 steps, and its cost remains manageable even with iterations.

Should you always choose the model with the best agentic score?

No. The agentic score measures maximum capacity, not efficiency. For a well-constrained agent with a deterministic workflow, a model scoring 80 may be sufficient and cost 5 times less than a model scoring 100.

Can open source models really compete in agentic tasks?

Kimi K2.6 (88.1) and DeepSeek V4 Pro show that yes, for agents of intermediate complexity. For agents that must handle ambiguity and the unexpected, premium models retain a significant advantage.

Can you change LLMs without redoing the entire agent architecture?

Yes, if you use a framework that separates the orchestration logic from the model (OpenClaw, architectures based on standardized interfaces). This is one more reason not to couple your agent to a single provider.

How much does an agent in production cost per month?

It depends on volume, but for an agent with 1000 tasks/day, each consuming an average of 15K tokens, the cost ranges from $50 to $500/month depending on the chosen model. DeepSeek V4 Pro at the bottom, Claude Mythos at the top.

✅ Conclusion

Choosing the LLM for an AI agent is an architecture decision, not a brand preference. Claude Mythos Preview dominates the agentic ranking in 2025, but the best model for you depends on your complexity, your budget, and your latency constraints. Test in real conditions, isolate the agent logic from the model, and optimize with a multi-model architecture. To go further, check out our Claude, GPT, Gemini, Llama : quel modèle choisir en 2026 ? and our guide to the meilleurs LLM gratuits to prototype without risking your budget.