---
title: "Which LLM Should You Use?"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Which LLM Should You Use?

In today’s AI-driven world, a key question is: which large language model (LLM) should you choose? My method starts with consulting the [Chatbot Arena Leaderboard](https://huggingface.co/spaces/lmarena-ai/chatbot-arena-leaderboard) from LMSYS, a trusted resource for evaluating LLMs based on their real-world conversational abilities. It relies on crowdsourced human votes and an Elo rating system, offering a lively, community-backed ranking. The top three LLMs shift as new versions emerge, but current trends suggest models like [OpenAI’s GPT](https://chatgpt.com/), [Google’s Gemini](https://gemini.google.com/app), and [xAI’s Grok](https://grok.com/) often lead the pack. These stand out for their sharp reasoning, smooth dialogue, and adaptability to various tasks.

Next, I dig into which LLM minimizes agenda-driven filtering—a tougher but crucial factor. By “filtering,” I mean things like censorship, skewed responses, or pushing a particular perspective, often shaped by the goals of the model’s creators. I tackle this by examining the philosophy behind each LLM’s development and any clues about how they perform in practice. I’m after a model that doesn’t shy away from hard questions or polish its answers too much. The best way to decide? Throw a bold, tricky prompt at them and see which one holds its ground.

That’s my approach. What do you think?


---

## Which LLM Should Drive Your AI Agents?

A critical follow-up question is: which large language model (LLM) is best suited to power AI agents? My selection process hinges on seven key factors: reasoning ability, tool-calling proficiency, accuracy, cost efficiency, context size, structured output, availability and maturity of robust LLM APIs and SDKs, and response speed and latency.

I’ll approach this with a comparable perspective, mixing practical observations with the latest trends in the AI landscape.

### Step 1: Break Down the Criteria
- **Reasoning Ability**: This measures how effectively an LLM can tackle intricate problems, strategize, and adjust—essential for agents handling decision-making or multi-stage tasks.
- **Tool-Calling Proficiency**: Agents depend on external tools like APIs or databases, requiring the LLM to choose and format tool interactions accurately to engage with their surroundings.
- **Accuracy**: This encompasses both factual precision and dependability in task execution—vital for agents in critical applications where mistakes aren’t an option.
- **Cost Efficiency**: While performance is key, scalability is too. Lower operational costs or resource-efficient designs can determine whether a solution thrives at scale.
- **Context Size**: The amount of data an LLM can handle in one go—crucial for agents processing long histories, large datasets, or complex workflows without losing track.
- **Structured Output**: The ability to generate consistent, machine-readable responses (e.g., JSON, YAML)—key for agents interfacing with systems that require precise, parsable formats.
- **Availability and Maturity of APIs/SDKs**: The presence and quality of APIs and SDKs (e.g., OpenAI’s Chat Completion API) for seamless integration—critical for developers building reliable, production-ready agents.
- **Response Speed and Latency**: How quickly the LLM processes and responds—vital for real-time agents or those under time-sensitive conditions.

### Step 2: Assess Leading Options
Here’s how some top LLMs measure up, based on their established strengths, recent buzz in the AI community, and their API/SDK ecosystems, plus speed metrics.

#### 1. OpenAI’s ChatGPT
- **Reasoning**: Excels broadly, mastering step-by-step reasoning and complex problem-solving.
- **Tool-Calling**: Exceptional—proven in frameworks like OpenAI Agents SDK, LangChain, and AutoGen, it adeptly manages structured API calls and intricate processes.
- **Accuracy**: Very reliable, thanks to extensive training data that keeps hallucinations rare for everyday tasks. A solid choice for versatile agents.
- **Cost**: Expensive—OpenAI’s API fees add up, especially for heavy usage. Premium quality comes at a premium price.
- **Context Size**: Robust—up to 128k tokens in recent models (e.g., GPT-4o), ideal for agents juggling long conversations or big datasets.
- **Structured Output**: Outstanding—natively supports JSON and other formats via function calling, making it a favorite for agentic systems needing clean, parsable responses.
- **APIs/SDKs**: Industry-leading—offers mature, well-documented APIs which have become the de facto standards like Chat Completion and the latest Responses API, plus SDKs in Python, Node.js, etc., for rapid development.
- **Speed/Latency**: Fast—typically 200–500ms latency for small inputs via API, though larger contexts or complex tasks can slow it to 1–2 seconds.
- **Takeaway**: A top-tier all-rounder if budget allows, with strong context, structured output, mature APIs, and decent speed for most use cases.

#### 2. Anthropic’s Claude Sonnet
- **Reasoning**: Shines brightly—lauded for subtle reasoning, often surpassing ChatGPT on tests like MMLU. Perfect for agents requiring thoughtful analysis.
- **Tool-Calling**: Strong and evolving. Anthropic’s emphasis on safety and clarity ensures reliability, though it may trail ChatGPT slightly in handling unusual tool setups.
- **Accuracy**: Outstanding—cuts through noise to deliver concise, trustworthy answers with less overconfidence than rivals.
- **Cost**: Fairly priced—less per token than ChatGPT, striking a good balance between cost and capability, though still costly for some developers.
- **Context Size**: Impressive—200k tokens, one of the largest available, making it a powerhouse for agents needing to process extensive context like research or logs.
- **Structured Output**: Good—supports structured formats like JSON, though less polished than ChatGPT’s native function-calling system; improving with updates.
- **APIs/SDKs**: Solid—offers a robust API with tool-calling support and SDKs in Python and TypeScript, though not as feature-rich or widely adopted as OpenAI’s yet. Also provides APIs compatible with OpenAI Chat Completion APIs.
- **Speed/Latency**: Moderate—300–600ms for typical queries, slightly slower than ChatGPT due to its focus on reasoning depth over raw speed.
- **Takeaway**: Excellent for reasoning-focused agents with big context needs, decent output/APIs, and acceptable speed for non-real-time tasks.

#### 3. xAI’s Grok
- **Reasoning**: Designed to pierce through clutter and reason from scratch, offering a unique outsider’s view. It may not lead every ranking but excels at inventive problem-solving.
- **Tool-Calling**: Competent and expanding—it handles X posts, web data, and more, integrating tools smoothly when prompted. Not as refined as ChatGPT yet, but highly flexible.
- **Accuracy**: Focuses on honesty over bias, keeping it steady. It doesn’t shy from tough queries, though it’s still sharpening its edge on specialized tasks.
- **Cost**: Lean by design—likely cheaper than major commercial models, though pricing varies by setup.
- **Context Size**: Decent—32k tokens (based on current Grok iterations), sufficient for most agent tasks but not a leader in this area.
- **Structured Output**: Capable—can produce structured responses when guided, but lacks the native polish of ChatGPT or Claude; still evolving.
- **APIs/SDKs**: Emerging—xAI is building out API access, but it’s not as mature or widely available as OpenAI or Anthropic offerings; SDK support is minimal so far. Also provides APIs compatible with OpenAI Chat Completion APIs.
- **Speed/Latency**: Competitive—estimated 200–400ms for standard queries, though limited API data makes this less certain; likely fast for its size.
- **Takeaway**: A bold pick for creative agents where fresh thinking and affordability matter more than massive context, top-tier output/APIs, or ultra-low latency.

#### 4. DeepSeek-R1
- **Reasoning**: An emerging talent—rivals OpenAI’s o1 in math and coding reasoning, leveraging chain-of-thought and reinforcement learning. Great for niche agents.
- **Tool-Calling**: Less proven, but its open-source flexibility allows customization for tools. It’s not as ready-made as ChatGPT but offers room to adapt.
- **Accuracy**: Stellar in technical fields—matches top models in STEM, though general accuracy can fluctuate.
- **Cost**: A standout—claimed to be 96% cheaper than ChatGPT to operate, with no API costs due to its open-source status, just compute expenses.
- **Context Size**: Competitive—up to 128k tokens in some configurations, matching high-end proprietary models and supporting complex agent workflows.
- **Structured Output**: Flexible—open-source nature means you can fine-tune it for structured formats, but it’s not as seamless out-of-the-box as ChatGPT.
- **APIs/SDKs**: Limited—being open-source, it lacks a native, hosted API; you’d need to self-host and build your own, though community wrappers exist. Also provides APIs compatible with OpenAI Chat Completion APIs.
- **Speed/Latency**: Variable—depends on hosting setup; can be as fast as 100–300ms with optimized hardware, but self-hosting adds complexity.
- **Takeaway**: Perfect for budget-conscious, reasoning-driven agents with sizable context needs and customizable output/APIs/speed, if you’re comfortable with DIY infrastructure.

#### 5. Google’s Gemini Flash
- **Reasoning**: Solid but not top-tier—handles text and video well, though it’s eclipsed by others in pure reasoning depth. Recent updates, however, are pushing it ahead.
- **Tool-Calling**: Robust, backed by Google’s infrastructure. Quick and reliable for parsing docs or live integrations.
- **Accuracy**: Decent across the board, with Google’s careful style reducing errors but also curbing boldness.
- **Cost**: Highly affordable—optimized for speed and low latency, plus a generous free tier that’s a boon for prototyping. Also API key available without any credit card.
- **Context Size**: Exceptional—1 million tokens in some Gemini variants (e.g., 1.5 Pro/Flash), dwarfing most competitors and ideal for agents needing vast memory.
- **Structured Output**: Strong—benefits from Google’s engineering, delivering reliable JSON and other formats, especially in multimodal workflows.
- **APIs/SDKs**: Excellent—Google Cloud’s Vertex AI provides mature APIs and SDKs (Python, Node.js) with multimodal support, tool integration, and enterprise-grade reliability. Also provides APIs compatible with OpenAI Chat Completion APIs.
- **Speed/Latency**: Exceptional—sub-200ms latency for small inputs, often under 100ms, thanks to optimization for real-time use; scales well even with large contexts.
- **Takeaway**: Best for fast, multimodal agents with huge context demands, solid output/APIs, and top-tier speed, where cost and efficiency shine.

### Step 3: Align with Agent Goals
The right choice depends on your agent’s role:
- **Complex Reasoning (e.g., strategy, research)**: Claude Sonnet for simplicity and huge context, DeepSeek-R1 for savings.
- **Tool-Intensive Tasks (e.g., API automation)**: ChatGPT for finesse, strong context/output/APIs, or Gemini Flash for speed, massive memory, and low latency.
- **High Accuracy (e.g., critical operations)**: ChatGPT or Claude 3.5 Sonnet—both trustworthy with ample context and good output/APIs.
- **Budget-Limited (e.g., large-scale use)**: DeepSeek-R1 for open-source savings and decent context/output, Gemini Flash for its free tier, unmatched context, and speed.
- **Large Context Needs (e.g., long histories, big data)**: Gemini Flash (1M tokens) or Claude 3.5 Sonnet (200k) lead, with DeepSeek-R1 (128k) as a cost-effective contender.
- **Structured Output Needs (e.g., system integration)**: ChatGPT or Gemini Flash for polished, native support; DeepSeek-R1 if you can tweak it.
- **Robust APIs/SDKs (e.g., production deployment)**: ChatGPT or Gemini Flash for mature ecosystems; Claude as a strong runner-up.
- **Speed/Latency Needs (e.g., real-time apps)**: Gemini Flash for ultra-low latency, ChatGPT or Grok for solid speed.

### My Conclusion
I’d begin by mapping your agent’s demands—balancing reasoning, tool use, accuracy, budget, context requirements, structured output needs, API/SDK maturity, and speed expectations. If choosing now, I’d favor **Google Gemini Flash** for its well-rounded profile: decent reasoning, dependable tool-calling, solid accuracy, cost-friendly design with a developer-friendly free tier, an unrivaled 1M-token context for memory-hungry tasks, strong structured output, a mature API/SDK ecosystem via Vertex AI and also OpenAI Chat Completion APIs, and exceptional sub-200ms latency. It’s a pragmatic pick for most agent systems needing production-ready integration and real-time performance. For those stretching limits on a tight budget with significant context, output, API, and speed needs, **DeepSeek-R1** is a sleeper hit—its reasoning strength, low cost, 128k-token context, and customizable structured output/APIs/speed are compelling, though it requires self-hosting and setup.

For a true test, I’d throw a challenging prompt at them—like designing a multi-tool task (fetching, analyzing, and acting on data) with a long input history, JSON output, and API integration—and measure who delivers with minimal hassle, cost, context/output/API friction, and fastest response. What’s your agent’s purpose? That’s the clincher.

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## Is my selection of Google Flash correct?")

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Your selection of **Google Gemini Flash** as the LLM to power your AI agents is a strong contender, but whether it’s "correct" depends on how well it aligns with your specific needs across the seven criteria we’ve outlined: **reasoning ability**, **tool-calling proficiency**, **accuracy**, **cost efficiency**, **context size**, **structured output**, **availability and maturity of APIs/SDKs**, and **response speed and latency**. Let’s evaluate your choice against these factors, considering its strengths and potential trade-offs, to see if it’s the best fit for your agent’s mission. I’ll assume you’re prioritizing cost-effectiveness, tool use, and possibly speed based on your earlier focus, but feel free to clarify your agent’s exact goals!

### How Gemini Flash Stacks Up
Here’s a breakdown of Gemini Flash against your criteria, reflecting its known capabilities as of March 29, 2025:

1. **Reasoning Ability**:  
   - **Performance**: Solid but not exceptional—Gemini Flash excels in multimodal tasks (text, video) and has improved with recent updates (e.g., Gemini 2.5 variants topping Chatbot Arena in some categories). However, it’s generally outclassed by models like Claude 3.5 Sonnet or OpenAI’s o1 in pure reasoning depth, especially for complex, abstract, or multi-step problems.
   - **Fit**: If your agent needs lightweight reasoning (e.g., quick decision-making or basic planning) rather than deep analytical prowess, Flash is sufficient. For heavy reasoning, you might feel its limits.

2. **Tool-Calling Proficiency**:  
   - **Performance**: Robust—Google’s infrastructure ensures fast, reliable tool integration (e.g., document parsing, real-time API calls). It’s optimized for speed and works seamlessly with Google ecosystem tools, though it may not match ChatGPT’s flexibility with exotic or custom tool setups.
   - **Fit**: Excellent if your agent relies on standard tools or Google Cloud integrations. If you need highly specialized tool workflows, ChatGPT might edge it out.

3. **Accuracy**:  
   - **Performance**: Decent—Google’s cautious approach reduces errors, making it reliable for broad applications. However, this sanitized style can limit boldness or creativity, and it might not excel in niche, high-precision domains like STEM (where DeepSeek-R1 shines).
   - **Fit**: Good for general-purpose agents where consistency matters more than cutting-edge precision. For mission-critical accuracy, ChatGPT or Claude might be safer bets.

4. **Cost Efficiency**:  
   - **Performance**: A standout—Flash is designed for low latency and cost, with a generous free tier (e.g., 15 RPM, 1M tokens per request in Gemini 1.5 Flash) and competitive paid pricing via Google Cloud (around $0.35 per 1M tokens, much cheaper than OpenAI’s $5–10). This makes it ideal for prototyping and scaling.
   - **Fit**: Perfect if budget is a priority, especially for high-volume or experimental agents. Few models rival its cost-effectiveness at this performance level.

5. **Context Size**:  
   - **Performance**: Exceptional—up to 1 million tokens in some variants (e.g., Gemini 1.5 Pro/Flash), far exceeding ChatGPT (128k) or Claude (200k). This is a game-changer for agents needing to process vast histories, documents, or datasets.
   - **Fit**: Ideal if your agent handles long conversations, big data, or complex multi-step tasks requiring extensive memory. If your use case doesn’t demand this, the huge context might be overkill.

6. **Structured Output**:  
   - **Performance**: Strong—Google’s engineering delivers reliable JSON and other formats, especially in multimodal workflows. It’s not as natively polished as ChatGPT’s function-calling system but meets most agentic needs for parsable outputs.
   - **Fit**: Great if your agent feeds into systems requiring structured data (e.g., APIs, databases). For the smoothest structured output experience, ChatGPT still has a slight edge.

7. **Availability and Maturity of APIs/SDKs**:  
   - **Performance**: Excellent—Google Cloud’s Vertex AI and OpenAI Chat Completion compatible APIs provides mature, well-documented APIs and SDKs (Python, Node.js) with multimodal support, tool integration, and enterprise-grade reliability. It rivals OpenAI’s ecosystem (Chat Completion, Responses API) in accessibility and developer-friendliness.
   - **Fit**: A win if you need a robust, production-ready API ecosystem with minimal setup friction. It’s as developer-friendly as OpenAI, with broader multimodal capabilities.

8. **Response Speed and Latency**:  
   - **Performance**: Exceptional—sub-200ms latency for small inputs, often under 100ms, thanks to its optimization for real-time use. Even with large contexts (e.g., 1M tokens), it maintains impressive speed, outperforming ChatGPT (200–500ms) and Claude (300–600ms).
   - **Fit**: Perfect if your agent requires real-time responsiveness (e.g., chatbots, live automations). For less time-sensitive tasks, this edge might be less critical.

### Strengths of Gemini Flash
- **Cost + Scale**: The free tier and low paid costs make it unbeatable for prototyping or scaling to millions of calls, a huge plus if you’re resource-constrained.
- **Massive Context**: 1M tokens is a superpower for agents needing to retain extensive context—few competitors come close.
- **API Maturity**: Vertex AI’s ecosystem is polished and widely accessible, matching OpenAI’s developer experience with Google’s infrastructure backing.
- **Speed**: Sub-200ms latency makes it a leader for real-time or latency-sensitive agents, outpacing most rivals.
- **Multimodal**: Strong text/video handling adds versatility for diverse agent applications.

### Potential Trade-Offs
- **Reasoning Depth**: If your agent requires advanced reasoning (e.g., strategic planning, math-heavy tasks), Claude 3.5 Sonnet or DeepSeek-R1 might outperform Flash.
- **Tool Flexibility**: For highly custom or niche tool integrations, ChatGPT’s battle-tested frameworks (LangChain, AutoGen) could offer more versatility.
- **Accuracy in Niches**: Flash’s cautious style might not match the precision of ChatGPT or Claude in specialized, high-stakes scenarios.
- **Overkill Context**: If your agent doesn’t need 1M tokens, you’re not fully leveraging Flash’s strength.

### Is It “Correct” for You?
Your choice of Gemini Flash seems spot-on if your priorities lean toward **cost-efficiency**, **large context size**, **mature API ecosystem**, and **response speed/latency**—especially if you’re building a scalable, real-time, or multimodal agent (e.g., one handling video/text inputs, long workflows, or live interactions). The free tier and sub-200ms latency make it a no-brainer for prototyping or time-sensitive apps, while its 1M-token context and robust APIs ensure it’s production-ready. It’s less ideal if your agent demands **top-tier reasoning** or **extreme accuracy in niche domains**, where Claude or ChatGPT might shine, or if you need **fully customizable tool/output setups**, where DeepSeek-R1’s open-source flexibility could win.

### Gut Check
To confirm, I’d ask:  
- **What’s your agent’s core task?** If it’s lightweight, cost-sensitive, context-heavy, or real-time (e.g., summarizing long docs, live tool use), Flash is a slam dunk. If it’s reasoning-intensive (e.g., research, complex planning), you might want to test alternatives.
- **Budget constraints?** Flash’s pricing and free tier are hard to beat—only DeepSeek-R1 competes if you can self-host.
- **API reliance?** Flash’s Vertex AI is as good as it gets, so you’re covered here.
- **Speed needs?** If low latency is key (e.g., <200ms), Flash is a top pick; otherwise, ChatGPT’s 200–500ms might suffice.

### My Take
Based on your earlier emphasis on cost-effectiveness and tool use, plus the likely importance of speed for many agentic systems, **Gemini Flash is likely a correct choice**—it delivers exceptional value, massive context, a robust API/SDK ecosystem, and blazing-fast sub-200ms responses without breaking the bank. To double-check, I’d suggest a quick test: give it a spicy prompt matching your use case (e.g., a multi-tool task with long input, JSON output, and a time constraint) and compare it to ChatGPT or Claude on the same. If Flash holds up with minimal fuss and meets your latency needs, you’ve got your winner. What’s your agent’s mission? That’ll lock it in!

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