22 Best Large Language Models (LLMs) to Use in 2025

Last Updated: May 23, 2026

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• The best large language model for you depends on your use case, data sensitivity, and budget, not just benchmark bragging rights.
• For most people, a mix of one premium API model and one open-source model covers 90% of day-to-day work.
• RAG, agents, and on-device models matter as much as raw model quality now, so you cannot just look at leaderboards.
• If you work in a regulated or enterprise setting, governance, logging, and deployment control usually matter more than which lab won the last benchmark.

Large language models are no longer just shiny chatbots; they are the quiet engine behind search, support, coding, analytics, and a lot of content you read every day.

You get the best results when you pick models based on your real workflows, mix open and proprietary options, and accept that you will probably change that stack at least once a year.

What are large language models now?

Large language models are AI systems trained on a huge mix of text, code, and often images and audio, then fine-tuned to follow instructions and reason step by step.

They predict the next token, but with the right training and tools around them, that prediction feels like planning, coding, research, and sometimes even strategy work.

Modern LLMs do more than chat in a browser tab.

They call tools, read documents, browse, write and run code, analyze video, and plug into CRMs, IDEs, BI tools, and your internal knowledge base.

Some people use them casually, almost like a smarter search box.

Others wire them deep into their workflows: support triage, code review, contract review, even experiment design in R&D labs.

Good results with LLMs come less from picking a magic model and more from pairing a solid model with the right data, tools, and guardrails.

So instead of asking “what is the single best model,” it is more useful to ask “which few models, in which setup, actually move my metrics.”

That is the lens we will use here.

Quick overview table: leading LLMs to use now

Here is a snapshot of major LLMs that matter right now for real work, not just demos.

I will focus on the core details that influence daily use.

Model	Provider	Modality	Approx. context	License / access	Key strength
GPT-4.1 / o-series	OpenAI	Text, code, images, some audio	Up to ~1M tokens (tier-dependent)	Proprietary API	Strong general reasoning and coding
Claude 3.5 Sonnet	Anthropic	Text, code, images	Long context, suitable for big docs	Proprietary API	Careful writing and safety profile
Claude 3.5 Haiku	Anthropic	Text, code, images	Long context	Proprietary API	Fast and cheaper for bulk tasks
Gemini 1.5 Pro	Google	Text, code, images, audio, video	Very long context (project-scale)	Proprietary API	Multimodal and integrated with Google tools
Gemini 1.5 Flash	Google	Text, code, images, audio, video	Long context	Proprietary API	Speed and low cost for everyday use
Llama 3 70B	Meta	Text, code	Typical long context for open weights	Open weights (permissive license)	Strong general model for self-hosting
Llama 3.1 8B / 405B (where available)	Meta	Text, code	Medium to long	Open or hosted variants	Balanced quality vs cost; good for edge
Mistral Large	Mistral AI	Text, code	Long context	Proprietary API	Strong coding and reasoning for European orgs
Mixtral 8x7B / 8x22B	Mistral AI	Text, code	Medium to long	Open weights	Good quality with efficient mixture-of-experts
Qwen 2.5 72B / 110B	Alibaba	Text, code, some vision	Long context	Open weights (varied licenses)	Strong multilingual and reasoning
DeepSeek V3 / R1-series	DeepSeek	Text, code	Medium to long	Open or semi-open weights	Reasoning and math at low cost
Phi-3 family	Microsoft	Text, code	Medium context	Open weights	Small yet capable, good on devices
GPT-4o mini / similar small OpenAI models	OpenAI	Text, code, images	Medium to long	Proprietary API	Cheap for bulk generation
Command-R / Command-R+	Cohere	Text, code	Long context	Proprietary API	Built for RAG and enterprise search
AI21 Jamba-family	AI21 Labs	Text, code	Long context	Proprietary and open variants	Mixture-of-experts tuned for long docs
Local small models (e.g., Llama 3 8B, Phi-3-mini)	Multiple	Text, code	Short to medium	Open weights	On-device, private, low-latency

These are not the only good models, but if you cover this set, you have strong options for almost every common use case.

Next, let us look at how these models actually work, because that still confuses a lot of teams.

How large language models work in practice

At the lowest level, an LLM is just a giant probability engine predicting the next token given the previous ones.

During training, it sees billions of examples of text and code, learns patterns, and then in deployment it uses those patterns to respond to your prompts.

Modern models usually go through at least three phases.

Pretraining on broad data, instruction tuning on human-written or curated prompts and answers, and then reinforcement-style methods to improve alignment and usefulness.

• Text understanding: The model turns your prompt into tokens, builds an internal representation, and tracks context across long conversations.
• Text and code generation: It outputs tokens one by one, which form words, code, or structured formats like JSON.
• Reasoning: Newer models simulate multi-step reasoning, sometimes with explicit chain-of-thought techniques or internal scratchpads.
• Multimodality: Vision-capable models also map pixels or audio frames into the same kind of internal space, so they can “talk” about images, audio, or video.

When people say a model is good at reasoning, they usually mean it keeps a longer, more accurate chain of intermediate steps before producing the final answer.

Raw model capability is only part of the story.

The rest comes from how you connect it to tools, what data you feed it, and how carefully you control prompting and output checks.

A closer look at the most influential LLMs right now

Instead of listing every model under the sun, let us zoom in on a handful that keep showing up in serious projects.

The strengths overlap, but they are not identical, which is why many teams mix two or three.

OpenAI GPT-4.1 and o-series

OpenAI’s top models still set the tone for a lot of benchmarks and real-world apps.

They are strong at reasoning, code generation, and general writing, and they integrate with a growing set of tools and assistants.

• Great choice for complex multi-step queries, data analysis, and system prompts that require careful behavior.
• Small variants like GPT-4o mini help with cheap bulk generation, draft content, and simple chatbots.
• API-first, so they fit cloud-native stacks, but you give up full control over the underlying model.

For product teams that want the “safe default” without self-hosting, GPT-style models still make sense.

Just do not assume they are always the cheapest or best for your niche; sometimes an open-source model plus RAG wins.

Claude 3.5 family (Sonnet and Haiku)

Anthropic’s Claude models gained a strong reputation for careful, structured writing and a conservative safety stance.

Sonnet handles complex tasks, while Haiku is tuned for speed and lower cost.

• Very good for long policy docs, contracts, documentation, and customer-facing copy where tone matters.
• Popular in legal, consulting, and research teams that want more cautious behavior.
• Less flexible than open-source for custom internal deployments, since access is via API.

If you care about long, well-structured, low-drama content, Claude 3.5 Sonnet is often a strong first test before you try anything else.

Gemini 1.5 Pro and Flash

Google’s Gemini models stand out for native integration with Google Workspace and strong multimodal support.

You can feed them documents, PDFs, images, and long transcripts, and keep everything inside a stack you might already use.

• Pro fits for larger, more complex tasks and long-context research.
• Flash is better for everyday tasks where you care more about speed and cost than perfect reasoning.
• If your data lives in Google Drive, Gmail, or Sheets, Gemini often reduces glue work.

I still recommend benchmarking Gemini vs OpenAI and Anthropic on your own prompts.

Different models surprise you on things like math, code, and niche jargon.

Llama 3 and its successors

Meta’s Llama 3 line reset the bar for what an open-weight model can do.

For many workloads, a well-tuned Llama 3 70B competes closely with paid APIs, especially when paired with good retrieval.

• Permissive licensing, which matters if you want to embed the model in your own product.
• Large ecosystem: quantized variants, fine-tuned chat and code models, and strong community tools.
• Works well in enterprise settings that insist on self-hosting within a VPC or on-prem hardware.

The tradeoff is that you now own the infrastructure, scaling, monitoring, and updates.

For some teams that is exciting, for others it is a distraction from the real business.

Mistral Large and Mixtral

Mistral has carved out a spot with strong models and a European base, which some organizations prefer for data reasons.

Mistral Large is a premium hosted model, while Mixtral is an open mixture-of-experts line that runs surprisingly well on modest hardware.

• Good blend of coding ability and natural language for product teams.
• Open variants let you experiment locally or in your own cloud without strict licensing headaches.
• Popular with companies that want something strong but not tied to US hyperscalers.

Qwen 2.5 and DeepSeek models

Qwen and DeepSeek are part of a newer wave of models that push hard on reasoning, math, and multilingual support, often at aggressive pricing.

They grew quickly in open-source circles and in regions under-served by US vendors.

• Qwen models are strong for multilingual tasks and technical content.
• DeepSeek R1-style models show up in math-heavy and research use cases where people want more internal reasoning.
• Licenses vary, so you need to read the fine print before building a commercial SaaS on top.

I see more engineering teams trying Qwen or DeepSeek as a second model in a routing setup.

They are not always the first choice, but they can be the one that saves a lot of inference cost.

Key 2026 trends shaping LLM choice

Once you have a basic sense of the main models, the bigger shifts explain why buying decisions look different now than they did two years ago.

These trends are not hype; you can see them in actual product roadmaps.

RAG 2.0: retrieval is now standard

Most serious deployments now use retrieval-augmented generation, not plain chat.

Your model fetches relevant documents or records from a vector database or search index, then answers using that context.

• For internal knowledge, RAG reduces hallucinations and keeps answers tied to your ground truth.
• For support and sales, it keeps responses consistent with your docs and legal language.
• For analytics, it lets non-technical users query warehouses with natural language.

Fine-tuning still matters, but many teams now start with RAG because it is faster to ship and easier to keep up to date.

Then they add fine-tuning or adapters later for tone and domain nuance.

Agentic workflows and tool calling

The next big shift is away from “single-turn chat” and toward agents that take actions: call APIs, write code, run scripts, and loop until a goal is reached.

Most top models now support structured tool calling and function calling out of the box.

• Customer service agents that look up orders, trigger refunds, and update CRMs.
• Dev agents that open pull requests, run tests, and comment on diffs.
• Ops agents that query dashboards, alert teams, and open tickets.

The question is no longer “can this model answer a support question” but “can this model safely decide when to change a real system.”

This is where safety, logging, and strong guardrails become non-negotiable.

If an agent can click buttons, you want to know exactly why it did that.

On-device, regulation, and the energy question

As models get stronger, cost, privacy, and regulation become real constraints, not side notes.

That is why smaller, more specialized models are now rising alongside the giants.

On-device and edge LLMs

Phones, laptops, and small servers can now run surprisingly capable models like Llama 3 8B or Phi-3 variants.

That changes what you can do offline, at the edge, or with strict data rules.

• Better privacy, since sensitive data never leaves the device or private network.
• Lower latency, because you skip round trips to the cloud for every token.
• Predictable cost once the hardware is in place.

The catch is that small models still lag top-tier cloud models on complex reasoning and niche expertise.

For things like summarizing local notes, drafting emails, or simple coding tasks, they are often “good enough.”

Regulation and compliance expectations

Governments and regulators have moved from talking about AI to writing rules for it.

If you work in finance, health, education, or the public sector, you are already feeling that pressure.

• Data residency and cross-border data flow are serious questions now, not an afterthought.
• You are expected to keep logs of inputs, outputs, and sometimes the model version used for key decisions.
• Vendors must explain training data policies, safety testing, and incident response processes.

This is one reason open-source and self-hosted models gained traction with compliance teams.

They are not a magic fix, but they make audits, reproducibility, and local control more realistic.

The cost and energy squeeze

Running huge models at scale is expensive in both money and energy.

As usage grows, companies start asking hard questions about ROI and footprint.

• Small and medium models are now treated as the default for many workloads.
• Premium models are reserved for high-value tasks: critical decisions, complex reasoning, or high-stakes content.
• Efficient architectures like mixture-of-experts and quantization tools help cut inference cost a lot.

This is one area where people sometimes take the wrong approach and chase the biggest headline model for everything.

In practice, routing 80% of tasks to a cheaper model and reserving the best model for the final 20% often saves serious money with little quality loss.

How to choose the right LLM for your situation

Picking a model just because it is “top of leaderboard X” rarely works.

You need to match your choice to your role and constraints.

If you are an individual or prosumer

Your priorities are simple: quality, ease of use, and price.

Infra and compliance are someone else’s problem.

• Use a mainstream chat product (ChatGPT, Claude, Gemini) as your daily driver.
• Layer in one open-source local model for private notes, simple coding, or offline work.
• Compare subscription cost vs how many hours it actually saves you each week.

Do not overcomplicate this; you do not need a cluster to write better emails and improve your research flow.

What matters more is building habits around prompts, templates, and review.

If you are a SaaS or startup integrating via API

Here the picture changes.

You care about latency, uptime, vendor lock-in, and unit economics per user.

• Pick at least two model providers and abstract them behind your own API layer.
• Use small models for fast previews or drafts, and call stronger models for final results.
• Think about regional coverage and data location early, so you are not blocked when you scale.

An internal routing layer is usually worth the effort by the time you have a few thousand active users.

It also gives you leverage when vendors change pricing or quality.

If you are an enterprise or in a regulated sector

For banks, hospitals, large retailers, and governments, the choice is much less about “cool” and more about control.

Some models are ruled out just because of data and legal constraints.

• Start with a clear map of sensitive data and where it can or cannot leave.
• Shortlist vendors that offer strong SLAs, logging, region-specific hosting, and clear data retention policies.
• Evaluate open-weight models for use inside a private cloud, especially for internal search and knowledge tasks.

For enterprise work, the better question is often “which deployment pattern do we trust” rather than “which single model is best on a benchmark.”

A common pattern now is hybrid: one or two proprietary models through strict enterprise contracts, plus at least one self-hosted model for workloads that can never leave your environment.

This is not the simplest setup, but it reflects real constraints.

Fine-tuning, adapters, and customization

General-purpose models are impressive, but for serious use, people usually want behavior tuned to their brand, jargon, and workflows.

You have several levels of customization, each with different cost and risk.

Prompting and few-shot examples

This is the lightest form of customization.

You write better system prompts, add a few examples, and maybe build prompt templates in your app.

• Easy to start with, no training needed.
• Good for tone, structure, and simple patterns.
• Can become brittle if prompts get huge or complex.

Many teams stop here and never fine-tune, and that is fine for lots of use cases.

If your prompts feel like a tangled script, that is a hint you might be forcing too much logic into text alone.

Lightweight fine-tuning and adapters

Adapters like LoRA let you train small parameter sets on top of an open-weight base model.

You get more control without retraining everything.

• Great for style, domain language, and recurring workflows.
• Works especially well with models like Llama, Qwen, and Mixtral.
• Requires clean training data and some MLOps basics.

This is usually the “sweet spot” for startups and internal tools that want a custom feel.

You control the weights, but you are not burning a hole in your budget.

Vendor fine-tuning on proprietary models

OpenAI, Anthropic, and others now offer hosted fine-tuning services.

They train custom variants of their models using your data, and you access them via API.

• Simpler than running your own training cluster.
• Good for very specific tasks, like classification, formatting, or house style.
• Less transparent and portable, since you cannot usually export the final weights.

This route is most attractive when integration with that provider is already deep in your stack.

If you are not sure how long you will stay with one vendor, you might prefer open weights.

Open-source vs proprietary LLMs

People sometimes frame this as a philosophical debate, but for most organizations it is a practical tradeoff.

You balance control, cost, and time to value.

Where open-source shines

Open-weight models like Llama, Mixtral, Qwen, and Phi give you something closed models cannot: direct access to the weights and full control over deployment.

That unlocks a few benefits.

• Deploy inside strict environments, including air-gapped systems and private clouds.
• Fine-tune with adapters or full training without asking permission.
• Inspect and audit behavior more deeply for compliance and research.

The flip side is that you now own scaling, uptime, security, and cost management.

If you do not have strong engineering support, that can become a burden fast.

Where proprietary models make sense

Proprietary models remove a lot of busywork.

You focus on prompts, UX, and business logic, while the vendor manages the scary stuff in the background.

• Quick to start; you can launch a prototype in days.
• Often ahead on cutting-edge benchmarks and safety work.
• Enterprise plans can include SLAs, support, and integration features.

You pay for that convenience in usage fees and some degree of lock-in.

That is why many teams mix both types instead of going all-in on one side.

Best LLMs for common use cases

Instead of only talking in abstract terms, let us map models to specific tasks that show up again and again.

Think of this as a starting point, not a final rulebook.

Best LLMs for coding and developer workflows

Coding is where speed, correctness, and context length all matter together.

Here are models that perform well for many teams.

• GPT-4.1 / top OpenAI models: Strong code completion, refactoring, and explanation; widely integrated into IDE tools.
• Claude 3.5 Sonnet: Good at reading and explaining large codebases and specs.
• Llama 3 70B or Qwen 2.5 large models: Good for self-hosted code copilots where data cannot leave your infra.
• Mistral Large / Mixtral: Often chosen by teams that prefer EU-based providers and strong code support.

For CI and code review, many teams now run agents that propose fixes, update tests, and comment on pull requests.

In those setups, latency and cost per repository matter more than flashy single-file completions.

Best open-source LLMs to self-host

If you want to bring the model into your own environment, these are common choices.

The exact pick depends on your hardware and tolerance for setup pain.

• Llama 3 8B / 70B: Strong general models with large community support and tools.
• Mixtral 8x7B: Efficient mixture-of-experts with good performance on many tasks.
• Qwen 2.5 32B / 72B: Good multilingual and technical performance.
• Phi-3 mini / small: Great when you need small models on low-end hardware.

If your team is new to self-hosting, starting with managed offerings that host these open weights for you can reduce friction.

Once you see value, you can move to your own cluster if the numbers make sense.

Best LLMs for enterprises and compliance-heavy use cases

Enterprises care about more than accuracy.

They need clear contracts, region control, logging, and sometimes custom evals for bias and fairness.

• Claude and GPT enterprise offerings: Popular for strong safety posture, admin controls, and legal frameworks.
• Mistral and European providers: Appealing for EU data residency and regional compliance comfort.
• Self-hosted Llama / Mixtral / Qwen: Used for internal knowledge, search, and summarization where data cannot leave the perimeter.

Many large organizations now run a “model platform” internally, where business units can try different models under a shared governance and logging layer.

This platform approach keeps teams from each secretly signing separate vendor deals and duplicating risk.

It also helps with consistent evals and incident response when outputs go wrong.

Typical pros and cons of leading LLMs

To make this more concrete, here is a high-level comparison across a few of the models people ask about the most.

Model	Strengths	Weaknesses
GPT-4.1 / o-series	Top-tier general performance, strong code, rich ecosystem	Fully proprietary, can be costly at scale
Claude 3.5 Sonnet	Careful writing, good for long docs, strong safety stance	API-only, region and pricing constraints for some users
Gemini 1.5 Pro	Deep multimodal support, works well with Google tools	Less flexible for self-hosting, access tied to Google stack
Llama 3 70B	Open weights, strong performance, large community	Infra and MLOps overhead for serious deployments
Mixtral 8x7B	Good quality vs compute, open, suited to RAG setups	Requires some expertise to tune and run efficiently
Qwen 2.5 large	Multilingual strength, strong reasoning for open weights	Licensing and documentation can feel less clear to newcomers
Phi-3 small models	Very capable for their size, great on limited hardware	Not ideal for the hardest reasoning or huge contexts

The exact rankings shift as new versions appear, but the pattern holds.

You trade off raw performance, cost, control, and ecosystem maturity on almost every choice.

Real-world use cases and modern challenges

Large language models now sit behind a surprising number of everyday tools and processes.

Once you know what to look for, you start spotting them everywhere.

Beyond chatbots: living examples

Here are a few patterns I keep running into across different industries.

They are not theoretical; they are already shipping.

• Autonomous customer service flows: Agents that answer questions, look up orders, trigger refunds, and escalate to humans when they hit confidence limits.
• Knowledge copilots: Internal tools that index wikis, tickets, and docs, then let employees ask questions in plain language.
• AI in BI and analytics: Natural language layers on top of warehouses so people can ask “Why did revenue drop in region X last quarter?” and get a structured answer.
• Domain copilots: Legal, medical, and financial copilots that combine RAG with strict guardrails and human review instead of replacing experts.
• Content and marketing systems: Structured workflows for drafts, variants, SEO briefs, and QA, not just “write me a blog post.”

In all these, the model is one part of a bigger pipeline that includes retrieval, validation, analytics, and humans in the loop.

If you only copy the chat interface, you miss most of the leverage.

New challenges in 2026

Models improved a lot, but new problems surfaced too.

Some are more subtle than early hallucination issues.

• Agent alignment: Getting multi-step agents to respect constraints, avoid risky actions, and know when to stop.
• Complex system behavior: When you combine several models and tools, bugs appear in the gaps, not just in one model.
• Security of AI-native apps: Prompt injection, data exfiltration through tools, and jailbreaks now require real security thinking.
• Evaluation at scale: You need automated eval suites, not just gut feel, to judge quality and regression across updates.

The biggest risk is rarely “this model cannot answer my question” but “this system confidently did the wrong thing and nobody noticed for weeks.”

This is why logging, monitoring, and targeted evals are no longer optional if you run LLMs in production.

You need feedback loops that catch drift before it hits customers or regulators.

How to keep your LLM stack future-resilient

The market changes quickly, so locking yourself into one model forever is risky.

You can design your stack to handle that churn more calmly.

• Build an internal abstraction layer so your app talks to your own API, not directly to one vendor.
• Keep at least one open-source model in your testing mix, even if you do not deploy it yet.
• Regularly re-run your eval suite when new models release or existing ones update.
• Track costs per feature and per user; do not guess on ROI.

That way, swapping models or adding a new one becomes a controlled experiment, not a rewrite.

It also gives you more negotiating power with vendors, which people often forget.

Bringing it back to your next step

If you are just starting, do not try to design the perfect LLM strategy on day one.

Pick a concrete problem that hurts right now and test two or three models against it.

Maybe that is support answer time, engineer time spent on boilerplate code, or content teams stuck in manual briefs.

Use that as your lab: try one hosted model, one cheaper variant, and one open-source setup with RAG, then track the difference.

From there you can grow into agents, custom fine-tuning, and hybrid deployments with more confidence.

The models will keep changing, but the core questions you ask about them will stay surprisingly stable: does this actually help your team, under your constraints, at a cost you can live with.

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