Remember the good old days? When choosing an AI language model was like picking between vanilla and chocolate? Yeah, me neither. Nowadays, the landscape is packed with options, each singing its own siren song of capabilities, speed, and cost. It's enough to make your head spin!

As folks who live and breathe AI agents and workflows here at MindPal, we've spent countless hours wrestling with these different models, figuring out their quirks, celebrating their strengths, and occasionally cursing their weaknesses (and costs!). We even built MindPal, a platform where you can harness the power of these models, often side-by-side in multi-agent workflows.

Inspired by a recent deep dive (shoutout to Theo - ping.gg for the original video!), we decided to put together our own tier list. Think of it as a friendly guide through the LLM jungle, based on real-world usage, performance, cost-effectiveness, and overall impact. We'll rank them from S (Superb!) down to D (Proceed with Caution).

Let's dive in!

The Tiers Explained (Briefly)

• S-Tier: The absolute game-changers. Revolutionary, highly capable, often offering incredible value or setting new standards. Your go-to choices for demanding tasks, especially complex workflows and tool use.
• A-Tier: Excellent performers. Powerful, reliable, and often groundbreaking in their own right, but maybe with a slight edge missing compared to the S-tier (could be cost, API access limitations, or niche usability).
• B-Tier: Solid, dependable choices. The workhorses. They get the job done well but might not be the absolute best in class for performance or price.
• C-Tier: Situational stars. These models have specific strengths (like incredible speed on specialized hardware) but might lack general intelligence or require extra effort.
• D-Tier: Proceed with caution. Significant drawbacks in terms of cost, performance, or usability limit their practical application.

The S-Tier: The Game Changers

These are the models that genuinely shifted the landscape or offer such compelling packages that they stand out from the crowd.

• Gemini 2.0 Flash: Unbelievably good value. It's smarter than many models that came before it (even comparable to GPT-4o in some tests) but at a fraction of the cost. It's fast, making it fantastic for quick tasks, brainstorming, and as a default model. Its low cost and high speed mean you can iterate rapidly. It's so efficient, it feels almost free. A true revolutionary in democratizing access to capable AI.
• Claude 3.5 Sonnet: Despite its very high cost (seriously, it's expensive!), 3.5 Sonnet earned its spot by revolutionizing AI's capability in specific areas, particularly UI generation and complex instruction following. It was arguably the first model developers felt they could trust for intricate coding tasks. Many AI dev tools were built on its back. While newer models challenge it, its impact was undeniable.
• Claude 3.7 Sonnet (Thinking): This model climbs to S-Tier primarily due to its exceptional ability with tool use and multi-step reasoning. Anthropic's transparency in exposing the reasoning data via API is a huge plus. While it can sometimes be overly verbose ("enthusiastic intern" vibe), its proficiency in executing complex, chained agentic tasks makes it a powerhouse for building sophisticated multi-agent workflows in MindPal. If your goal is complex automation involving multiple tools and steps, 3.7 Reasoning is a top contender, despite its cost.
• Gemini 2.5 Pro: Benchmark performance is simply staggering, indicating a significant leap in intelligence and capability. While we're still waiting on final API pricing and reasoning data access details, its sheer potential to redefine what's possible, especially for complex problem-solving and when integrated into platforms like MindPal for building your AI workforce, earns it a solid S-Tier spot. Expect this model to power the next wave of advanced AI applications.

The A-Tier: Powerful Contenders

These models are seriously impressive and often represent the cutting edge, just shy of that S-Tier magic.

• 03 Mini (OpenAI): When OpenAI needed to respond to the rise of powerful open models, they dropped 03 Mini. It's significantly cheaper than its predecessors like 4o and 01, yet incredibly smart, leveraging powerful reasoning capabilities. It flies, delivering complex answers quickly. While OpenAI doesn't expose its "thinking" data via API (which can make the wait time feel uncertain), its raw power and value make it an excellent choice for hard problems where Claude 3.7's specific tool-use strength isn't the primary need.
• DeepSeek R1 (Standard & Distilled): R1 was revolutionary for bringing powerful reasoning ("thinking" models) to the open-source world. The standard R1, while incredibly smart, suffered from slow inference speeds on most platforms. The "Distilled" versions (especially Llama Distilled) offered a fantastic compromise, running much faster (especially on hardware like Groq's) by distilling R1's knowledge onto smaller bases. Great for coding, though not quite as reliable as the top dogs.
• DeepSeek V3 (0324 Update): The foundation upon which R1 was built, V3 is a criminally underrated non-thinking model. It offered performance close to Claude 3.5 at a tiny fraction of the cost when it launched. The quiet March 2024 update pushed its performance even higher, reportedly surpassing GPT-4.5. Its incredible value and capability make it a potential powerhouse, held back mainly by hosting speed limitations compared to API-first models.
• Grok 3: This one's tricky. On potential and reported capabilities (especially its unique personality and real-time info access), it could be S-Tier. However, the complete lack of API access for developers months after being promised is a major drawback. You simply can't build reliable applications or workflows with it yet. Until an API is released and benchmarked, its potential remains locked behind its web UI, landing it firmly in A-Tier based on promise versus practicality for builders.

The B-Tier: Solid Performers

Reliable options that serve specific purposes well.

• GPT-4o: The model that set the mid-range standard for a while. Decent performance, faster and cheaper than the older GPT-4 Turbo, but ultimately surpassed in value by Gemini Flash and in reasoning/tool-use by others. A solid all-rounder, but no longer the best deal.
• Claude 3.7 Sonnet (Standard): The non-reasoning version of 3.7. Still capable, especially for creative tasks and UI, but shares the high cost without the S-Tier tool-use proficiency and reasoning transparency of its sibling.
• Gemini Flash-lite: A lighter, faster, and cheaper version of Gemini Flash. If absolute maximum speed for simple tasks is your only priority and you need to shave every fraction of a cent, it's an option. However, standard Gemini Flash is already so fast and cheap, the slight extra cost is often worth the capability boost for most users.

The C-Tier: Situational Use

Use these when their specific niche fits your needs perfectly.

• Llama 3: By itself, this open model is okay, but not stellar. Its superpower comes when paired with specialized hardware like Groq's LPUs, which run inference at blazing speeds. If raw speed on compatible tasks is paramount, it shines; otherwise, smarter models offer better quality.
• GPT-4o Mini: Kickstarted the trend of cheaper, faster, smaller models from major labs. Revolutionary for its time and price point, but now largely superseded by models like Gemini Flash which offer better performance at a similar or lower cost. Its impact was huge, but you probably shouldn't use it today.

The D-Tier: Proceed with Caution

Models with significant issues making them hard to recommend.

• GPT-4.5: Confusingly positioned and priced absurdly high ($75/million input tokens!). Despite the massive size and world knowledge, performance didn't impress, even losing in creative writing comparisons to GPT-4o. Not great at code, not demonstrably better at writing. Hard to justify.
• 01 Pro (OpenAI): Even more expensive than 4.5 ($150/$600 per million tokens!). It can solve problems nothing else can, but the tiny percentage improvement doesn't warrant the astronomical cost and often clunky user experience.

Choosing the Right LLM for You with MindPal

So, which model should you use? Here's our updated quick guide:

• Need speed and value for general tasks? Start with Gemini 2.0 Flash. It's the best all-around default.
• Tackling complex problems, reasoning, or multi-step tool use? Your top choices are Gemini 2.5 Pro (for raw intelligence) or Claude 3.7 Sonnet (Reasoning) (especially for intricate tool chains).
• Need the absolute fastest response for simple tasks? Gemini Flash-lite offers maximum speed at the lowest cost tier.

The beauty of a platform like MindPal is that you don't always have to choose just one! You can build multi-agent workflows that leverage the strengths of different models for different steps. Use Gemini Flash for initial quick filtering, pass the results to Gemini 2.5 Pro for deep analysis, and maybe use Claude 3.7 Reasoning to execute a complex series of actions based on the analysis. Check out our Language Model Settings documentation to see the options available.

Conclusion

The LLM world is evolving at lightning speed. Today's S-Tier champion might be tomorrow's A-Tier specialist. Cost, speed, intelligence, tool use, and API access are all part of a complex trade-off.

Our best advice? Start with a high-value default like Gemini Flash, know when to escalate to powerhouses like Gemini 2.5 Pro or Claude 3.7 for complex tasks, and understand the niche roles other models can play. And most importantly, experiment!

What do you think of this updated ranking? Did we nail it, or are we way off base? Let us know in the comments below! And if you're ready to start building your own AI workforce with these powerful models, check out MindPal today!