The Best Large Language Models (LLMs), Compared
The best large language models in 2026 come from a tight group of labs: OpenAI (GPT), Anthropic (Claude), Google DeepMind (Gemini), Mistral AI (Mistral), Meta (Llama), DeepSeek and Alibaba (Qwen). The frontier moves fast, and no single model wins every task.
This page compares the leading LLMs by what actually matters for builders — reasoning, context window, multimodality, open versus closed weights and cost — and links each lab through to its profile in the wider AI ecosystem. It is part of our generative AI cluster.
Methodology
Methodology: how we compared
"Best" depends on the job. Rather than crown one winner, we compare the leading large language models across five criteria that decide real-world fit. All figures are publisher-stated or drawn from independent leaderboards and are marked as of June 2026; the field moves fast and rankings change between updates.
We cross-checked claims against the labs' own model cards, independent leaderboards including LMArena and the aggregated LLM-Stats index, and the Stanford HAI AI Index. Where a model is unreleased or specs are provisional, we say so. We do not invent benchmark numbers.
Gap between the best closed and best open-weight model on Chatbot Arena by early 2025, down from ~8% a year earlier.
Token context window now standard at the frontier (GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro, DeepSeek V4).
OpenAI, Anthropic, Google, DeepSeek (2026)
Cheaper output tokens for the best open-weight models (DeepSeek V4) versus top closed frontier models.
DeepSeek / LLM-Stats, 2026
LLM comparison table
The table below summarises the leading models as of June 2026. Context windows are publisher-stated maximums; "open" means downloadable open-weight licences, "closed" means API-only. Specifications evolve quickly — treat this as a snapshot.
| Model | Company | Type | Context window | Strengths |
|---|---|---|---|---|
| GPT-5.5 | OpenAI | Closed | ~1M tokens | Agentic coding, tool use, broad ecosystem |
| Claude Opus 4.8 | Anthropic | Closed | ~1M tokens | Reasoning, long-horizon coding, safety |
| Claude Sonnet 4.6 | Anthropic | Closed | 1M tokens (beta) | Fast, high-volume agents, cost balance |
| Gemini 3.1 Pro | Google DeepMind | Closed | 1M tokens | Multimodal, long context, Google stack |
| Mistral Large 3 | Mistral AI | Open (Apache 2.0) | Large (MoE) | Leading European open-weight model |
| Mistral Medium 3.5 | Mistral AI | Open weights | Mid-large | Self-hostable on ~4 GPUs, balanced |
| Llama 4 Maverick | Meta | Open weights | ~1M tokens | Natively multimodal MoE, ecosystem |
| Llama 4 Scout | Meta | Open weights | Up to ~10M tokens | Industry-leading context length |
| DeepSeek V4 | DeepSeek | Open (MIT) | 1M tokens | Frontier-class at very low cost |
| Qwen3.6 / 3.7 | Alibaba | Mixed (open + API) | 256K–1M tokens | Strong coding, efficient dense models |
GPT (OpenAI)
OpenAI's GPT family remains the default reference point for the category and the most widely integrated through ChatGPT and the API. The current flagship, GPT-5.5, shipped in the API in April 2026 as OpenAI's first model with a 1-million-token context window, priced at $5 per million input tokens and $30 per million output tokens.
For most teams, GPT is the safe baseline for AI agents and coding assistants, with the deepest tooling and integrations.
Claude (Anthropic)
Anthropic's Claude is the model most often preferred for reasoning, long-horizon coding and writing. The flagship Claude Opus 4.8 (released May 2026) is the top Opus-tier model for complex reasoning and high-autonomy agentic work, with a 1-million-token context window and pricing around $5 input / $25 output per million tokens. Claude Sonnet 4.6 is the faster, cheaper hybrid-reasoning model for real-time agents and high-volume workloads, with a 1M-token window in beta.
Gemini (Google)
Google DeepMind's Gemini leads on native multimodality and long context. As of early 2026, Gemini 3.1 Pro is Google's most advanced Pro-tier reasoning model, natively handling text, audio, images, video, PDFs and entire code repositories within a 1-million-token context window. Faster Flash-tier models (Gemini 3.5 Flash and later) target high-throughput agentic and coding work at lower cost.
Mistral (Mistral AI)
Mistral AI, founded in Paris in 2023, is Europe's leading LLM lab and the strongest open-weight option from the continent. Mistral Large 3 (December 2025) is described as the largest open-weight Mixture-of-Experts model from a major lab, around 675B total parameters with 41B active, released under Apache 2.0. Mistral Medium 3.5 (March 2026) merges instruction-following, reasoning and coding into a single 128B dense model that self-hosts on as few as four GPUs, while Mistral Small 4 folds reasoning, vision and agentic coding into one compact model.
Llama (Meta)
Meta's Llama 4 family (launched April 2025) was Meta's first natively multimodal, Mixture-of-Experts open-weight line. Llama 4 Scout offers an industry-leading context window of up to ~10 million tokens (109B total / 17B active). Llama 4 Maverick (~400B total / 17B active, 128 experts) targets best-in-class multimodal performance with a ~1M-token window. The largest model, Llama 4 Behemoth, was previewed as still in training; as of mid-2026 public weights had not shipped.
DeepSeek
China's DeepSeek reset open-weight economics. DeepSeek V4 (April 2026) is an MIT-licensed Mixture-of-Experts family in two variants: V4-Pro (1.6T total / 49B active) and V4-Flash (284B total / 13B active), both with a 1-million-token context window. Independent reports put V4-Pro at the top of open-weight SWE-bench Verified scores and competitive with top closed models on agentic benchmarks, at roughly 10–30x lower cost per output token.
Qwen (Alibaba)
Alibaba's Qwen family is among the most capable open-weight lines and a benchmark favourite for coding. Qwen3.6-27B (April 2026) is a dense, Apache-2.0 open-weight model that reportedly matches or beats far larger MoE models on agentic coding benchmarks, with a native 256K context extendable toward 1M via scaling. Alibaba has also moved its top-tier Qwen3.7 Max (May 2026) to a closed, API-only release with a full 1M-token context, a notable shift after years of fully open flagships.
Open vs closed models
The single most important choice is open-weight versus closed. Closed models (GPT, Claude, Gemini) are API-only: you get frontier quality and managed infrastructure, but you cannot self-host or inspect the weights. Open-weight models (Mistral, Llama, DeepSeek, most Qwen) can be downloaded, fine-tuned and run on your own hardware, giving control over privacy, cost and compliance, at the price of running the infrastructure yourself.
The capability gap has narrowed sharply. The Stanford HAI AI Index 2025 found the difference between the best closed and best open-weight model on Chatbot Arena fell from about 8% to 1.7% in a single year. In 2026, DeepSeek V4 and Qwen pushed open weights to genuine frontier parity on several benchmarks, while OpenAI, Anthropic and Google retained an edge on the hardest reasoning and agentic tasks.
Which LLM to choose
Map the model to the job, not to the leaderboard. As of June 2026:
Most serious teams now run a multi-model stack, routing each task to the best-fit model and re-evaluating quarterly as new versions ship. These are exactly the trade-offs debated by builders and researchers at AI Summit Europe.
The LLM frontier in 2026 is a fast-moving race between a handful of labs: OpenAI, Anthropic and Google at the closed frontier, Mistral, Meta, DeepSeek and Alibaba closing the gap with open weights. There is no permanent winner, only the best model for your task this month. The one constant is the pace of change, which is exactly why the European AI community keeps gathering, increasingly in Barcelona.
See the people building these models
The labs behind GPT, Claude, Gemini, Mistral, Llama, DeepSeek and Qwen, and the researchers and founders shaping them, gather at AI Summit Barcelona 2026. From foundation models to AI agents and enterprise deployment, it is where the next generation of LLMs is debated up close.
Get your tickets →Sources
Frequently asked questions
Which is the best LLM in 2026?
There is no single best LLM. As of June 2026, GPT-5.5 (OpenAI) and Claude Opus 4.8 (Anthropic) lead on reasoning and agentic coding, Gemini 3.1 Pro (Google) leads on multimodal and long context, and DeepSeek V4 and Qwen lead among open-weight models. The best choice depends on your task, budget and whether you need open weights. Rankings shift monthly.
Is Claude better than GPT?
It depends on the task. Claude Opus 4.8 is often preferred for rigorous reasoning, long-horizon agentic coding and careful writing, while GPT-5.5 has the broadest ecosystem and very strong agentic tool use. Both sit at the frontier with a small, frequently changing gap. Many teams use both and route tasks to whichever performs better.
What is the best open-source LLM?
Most open frontier models are open-weight rather than fully open-source. As of June 2026, DeepSeek V4 (MIT) offers frontier-class capability at very low cost, Mistral Large 3 (Apache 2.0) is the leading European open-weight model, Alibaba's Qwen is a top open-weight coder, and Llama 4 (Meta) has the largest ecosystem and extreme context length.
What is the best LLM for coding?
For agentic coding, Claude Opus 4.8 and GPT-5.5 are the strongest closed models, scoring highly on benchmarks like SWE-bench Verified and Terminal-Bench. Among open-weight options, DeepSeek V4 and Qwen are very competitive at a fraction of the cost, making them popular for self-hosted coding agents.
What is the best LLM for enterprise?
For managed, frontier-quality enterprise use, GPT-5.5 or Claude via API and major clouds are common defaults. For data residency, on-prem deployment and AI Act sovereignty, European enterprises often prefer open-weight Mistral or Llama 4. The right answer depends on compliance, latency and cost.
What is a context window and why does it matter?
A context window is how much text, code or data a model can process in a single request, measured in tokens. Larger windows let a model reason over whole codebases, long documents or many files at once. In 2026, a 1-million-token window is standard at the frontier (GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro, DeepSeek V4), and Llama 4 Scout reaches up to about 10 million tokens.
Reviewed by the AI Summit Barcelona editorial team: Guillaume Rostand, Tanguy Wincker, Adam Hruska.
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