Top 5 LLMs for November 2025

(Updated for GPT-5.1, Grok 4.1 & Gemini 3)

With three frontier models dropping in the same week—GPT-5.1, Grok 4.1, and Gemini 3—the LLM landscape just shifted again.

Benchmarks are changing, vendors are racing to plug models into search, agents, and apps, and a lot of “Top LLMs” lists are already out of date. This updated guide keeps the same evidence-driven approach as your original article, but refreshes the lineup around the latest releases.

We still rely on:

• Dynamic, decontaminated coding benchmarks like SWE-rebench
• Human preference data from arenas like LM/Chatbot Arena
• Official technical reports & launch posts from OpenAI, Google, xAI, DeepSeek, and Qwen

…and combine that with real-world deployment considerations: cost transparency, ecosystem fit, and control.

Quick Answer (TL;DR)

Top 3 frontier LLMs right now (November 2025):

• GPT-5.1 / GPT-5.1-Codex – Best overall for adaptive reasoning, coding, and long-horizon agents in the OpenAI ecosystem (same pricing as GPT-5, but faster and more efficient).
• Gemini 3 Pro – Best for search-integrated multimodal workflows and “generative UI” experiences across Google Search, the Gemini app, and the new Antigravity dev environment.
• Grok 4.1 – Best for emotionally intelligent, creative assistants, with top scores on emotional-intelligence benchmarks and sharply reduced hallucinations.

Rounding out the Top 5:

• DeepSeek-V3 (with DeepSeek-R1) – Best open-weight option for code + reasoning.
• Qwen3-235B / Qwen3-32B – Best for controllable open deployments with a “thinking budget” and strong multilingual performance.

How We Selected the Top LLMs (November 2025)

The methodology stays the same, but the models are new:

• Dynamic, real-world evaluation – SWE-rebench for repo-level coding; LiveCodeBench and Aider editing benchmarks for open models.
• Human preference & Elo – LM/Chatbot Arena-style ratings and vendor-shared preference studies for assistant-like tasks.
• New 2025 benchmarks – Emotional-intelligence tests (EQ-Bench3) where Grok 4.1 currently leads, plus generative-UI and search-integration metrics for Gemini 3.
• Transparency & cost realism – Only models with clear pricing or open-weight releases made the cut. GPT-5.1 keeps GPT-5’s pricing; Qwen3 and DeepSeek-V3 are open-weight.
• Architectural innovation – Adaptive thinking (GPT-5.1), Deep Think (Gemini 3), emotional intelligence focus (Grok 4.1), thinking budgets (Qwen3), and MoE scaling (DeepSeek-V3).

Comparison at a Glance

(Repo-level coding numbers reference current SWE-rebench data for GPT-5 / GPT-5-Codex; GPT-5.1 is a faster, more efficient successor but hasn’t yet replaced them on the public board.)

1. GPT-5.1 / GPT-5.1-Codex – Best Overall for Adaptive Reasoning & Coding

Why it’s here

OpenAI’s GPT-5.1 is the new flagship in the GPT-5 series, available both in ChatGPT (Instant vs Thinking) and via the API. It’s designed to think more efficiently: using fewer tokens on easy tasks and ramping up depth only when needed.

Key Features

• Adaptive reasoning – Adjusts how much “thinking” it does per task, often using ~50% fewer tokens than GPT-5 while matching or beating its accuracy in partner evals.
• Extended prompt caching (24h) – Cache windows jump from minutes to up to a full day, ideal for long-running agents, coding sessions, and RAG chat. Cached tokens are ~90% cheaper.
• New tools for agents – APIs expose operations like apply_patch and shell, making it easier to build autonomous coding and DevOps agents that can modify repos and run commands safely.
• Pricing & availability – Same pricing and rate limits as GPT-5, with both gpt-5.1 and gpt-5.1-chat-latestavailable to all paid API tiers, plus specialized gpt-5.1-codex variants for long-running coding jobs.

Pros

• Excellent performance-to-cost ratio, now with better token efficiency than GPT-5.
• Strong upgrade path if you’re already invested in OpenAI tooling.
• Great for long-horizon agent workflows thanks to 24h prompt caching.

Cons

• SWE-rebench still lists GPT-5 variants, so public repo-level coding scores lag the actual 5.1 rollout.
• Vendor-hosted only—no open weights.

Best For:
Teams that want one primary hosted model for coding, RAG, and agents—and are already in the OpenAI / Microsoft ecosystem.

2. Gemini 3 Pro – Best for Search-Integrated Multimodal & Generative UI

Why it’s here

Google’s Gemini 3 is positioned as its most intelligent model so far, powering AI Mode in Search, the Gemini app, and the new Antigravity “agent-first” dev environment.

Key Features

• Search + AI Mode integration – Gemini 3 is now wired directly into Google Search, returning interactive, AI-generated interfaces instead of just links for complex queries.
• Deep Think mode – A special long-reasoning mode for hard problems, designed to push reasoning depth beyond Gemini 2.5’s chain-of-thought performance.
• Generative UI – Gemini 3 Pro can render dynamic UIs in its replies (dashboards, galleries, timelines), not just text, making it ideal for agentic products and complex search experiences.
• Antigravity IDE – A new coding environment that orchestrates multiple agents across editor, terminal, and browser, built around Gemini 3 Pro but also supporting other models.
• Benchmarks – Early reports show Gemini 3 Pro surpassing Gemini 2.5 Pro in major reasoning and coding benchmarks, with LM-Arena-style Elo around ~1500.

Pros

• Deep integration across Google’s stack: Search, Workspace, and Gemini app.
• Strong multimodal reasoning plus generative UI for highly interactive apps.
• Attractive for enterprises already standardized on Google Cloud / Vertex AI.

Cons

• Pricing details are still emerging; early access is tied to premium Gemini subscriptions and enterprise plans.
• Less transparent about token-level costs than OpenAI’s API docs.

Best For:
Organizations leaning into Google’s ecosystem that want search-native, multimodal, and UI-rich AI experiences—especially where “AI Mode” in Search is central to user flows.

3. Grok 4.1 – Best for Emotional Intelligence & Creative Assistants

Why it’s here

xAI’s Grok 4.1 focuses explicitly on conversational quality, emotional understanding, and creative writing, while maintaining strong reasoning. It’s rolling out across grok.com, X, and the Grok mobile apps.

Key Features

• Emotional-intelligence leader – xAI reports that Grok 4.1 is now top of EQ-Bench3, an emotional-intelligence benchmark measuring empathy and nuanced emotional responses.
• Fewer hallucinations – Launch coverage reports that Grok 4.1 is roughly 3× less likely to fabricate facts compared to previous Grok versions, thanks to updated training and safety pipelines.
• Conversational tone upgrade – The model card emphasizes more natural, fluid dialogue while keeping strong core reasoning—essentially a “warmer” Grok without losing its edge.
• Availability – Live today for users on X, grok.com, and iOS/Android apps, with Auto mode routing to Grok 4.1 by default.

Pros

• One of the best options for empathetic, emotionally aware chatbots.
• Strong creative writing and storytelling performance.
• Competitive on general reasoning while focusing on safety and reduced hallucinations.

Cons

• API access and pricing are still maturing vs more established developer ecosystems.
• Less battle-tested for large-scale enterprise workflows than GPT-5.1 or Gemini.

Best For:
Consumer-facing chatbots, coaching/companion apps, and creative tools where tone, empathy, and emotional nuancematter as much as raw reasoning.

4. DeepSeek-V3 (with DeepSeek-R1) – Best Open-Weight Model for Code & Reasoning

Why it’s here

DeepSeek-V3 is a massive Mixture-of-Experts (MoE) model (671B parameters, ~37B active per token) positioned as a frontier-level open-weight system. Paired with DeepSeek-R1 for reasoning, it anchors the current open-source frontier.

Key Features

• MoE architecture – 671B total parameters with ~37B routed per token, using custom MoE + multi-token prediction tricks to reach high performance with manageable inference cost.
• Reasoning companion (R1) – DeepSeek-R1 is a dedicated reasoning model that rivals OpenAI’s o-series on math, code, and logic benchmarks, and is fully open-weighted.
• Open weights + API – You can self-host on your own GPUs or use hosted endpoints, giving you flexibility on cost and privacy.
• Benchmarks – Community benchmarks like LiveBench and Aider’s code-editing tests place DeepSeek-V3 among the very top non-reasoning LLMs and as arguably the best open-weight model today.

Pros

• Frontier-level performance with full control over deployment.
• Excellent for code-editing agents and research-grade reasoning.
• No vendor lock-in or data residency concerns when self-hosting.

Cons

• Operational overhead: you manage GPUs, scaling, and updates.
• You may need fine-tuning and prompt engineering to match hosted competitors on specific workloads.

Best For:
Teams that want maximum control and privacy—especially for code and reasoning workloads—without paying hosted frontier prices.

5. Qwen3-235B / Qwen3-32B – Best for Controllable Open Deployments

Why it’s here

Alibaba’s Qwen3 family is the other major open-weight contender, built around a MoE design and a unique “thinking budget” that lets you trade latency for reasoning depth on demand.

Key Features

• Thinking budget – You can cap or expand the reasoning token budget per request, dynamically balancing speed and accuracy.
• MoE + dense lineup – From the flagship Qwen3-235B-A22B to smaller dense models (32B, 14B, 8B, etc.), all open-weighted under Apache-2.0.
• Multilingual strength – Trained on massive multilingual corpora (100+ languages), with strong results on reasoning-focused benchmarks.
• Reasoning focus – “Thinking” variants like Qwen3-235B-A22B-Thinking-2507 are tuned for deep chain-of-thought reasoning, rivaling DeepSeek-R1 and other RLLMs in open-source evals.

Pros

• Fine-grained control over latency vs quality.
• Strong multilingual and agent performance.
• Flexible deployment: on-prem, cloud, or hybrid.

Cons

• Requires more engineering effort than hosted GPT-5.1 / Gemini 3.
• Documentation and ecosystem are improving, but not yet as mature as OpenAI’s.

Best For:
Teams building cost-sensitive, multilingual, open-source pipelines where you want to dial reasoning depth up or down per request.

How to Choose the Right LLM for Your Needs

Quick Decision Framework

1. You want one primary hosted model for everything (code, RAG, agents):
   → Start with GPT-5.1 / GPT-5.1-Codex.
2. You want search-native, multimodal, UI-rich experiences:
   → Choose Gemini 3 Pro.
3. You want emotionally intelligent, creative assistants:
   → Go with Grok 4.1.
4. You want maximum control and open weights for code & reasoning:
   → Pick DeepSeek-V3 (plus R1).
5. You want controllable open deployments with strong multilingual support:
   → Deploy Qwen3-235B / 32B.

Questions to Ask Before You Decide

• Do you need self-hosting, or is a cloud API fine?
• Are your workloads code-heavy, multimodal, or conversation-focused?
• How important is cost predictability vs. absolute performance?
• Will your agents benefit more from adaptive thinking (GPT-5.1, Qwen3, Deep Think) or emotional intelligence(Grok 4.1)?
• Are you already committed to OpenAI, Google Cloud, or a fully open-source stack?

Common Mistakes to Avoid

• Using static, contaminated benchmarks as your only signal—prefer dynamic, repo-level and arena-style tests.
• Ignoring context limits when planning large-repo or multi-document workflows.
• Underestimating output token costs (they often dominate your bill).
• Assuming open weights are always cheaper—hardware, engineering time, and ops still cost real money.

Frequently Asked Questions

What is the “best” LLM overall in November 2025?

There’s no single champion, but:

• GPT-5.1 is the most balanced choice for general coding, agents, and RAG.
• Gemini 3 Pro looks strongest for search-integrated, multimodal, generative UI experiences.
• Grok 4.1 leads for emotional intelligence and creative assistants.

Which LLM is best for open-source or private deployment?

DeepSeek-V3 (with R1) and Qwen3-235B / 32B are the top open-weight options, giving you frontier-level performance with full control over data and deployment.

Which model has the largest context window?

For now, the GPT-5 family still advertises some of the largest practical context windows (up to ~400K tokens), and GPT-5.1 inherits that ecosystem.

What benchmark should I trust most in 2025?

Use a mix of:

• SWE-rebench for repo-level coding,
• LiveBench / LiveCodeBench for evolving workloads, and
• arena-style human preference data for assistant quality—plus any domain-specific evals you run yourself.

Conclusion

In November 2025, the LLM landscape is defined by rapid iteration and clear differentiation:

• GPT-5.1 – your default pick for adaptive reasoning, coding, and agent workflows.
• Gemini 3 Pro – the search-native, multimodal powerhouse with generative UI.
• Grok 4.1 – the emotionally intelligent, creative conversationalist.
• DeepSeek-V3 + R1 – open-weight performance for serious code and reasoning.
• Qwen3-235B / 32B – controllable open deployments with thinking budgets and strong multilingual support.

Next Step:
Start by piloting GPT-5.1 or Gemini 3 Pro for your primary workloads, then pair that with DeepSeek-V3 or Qwen3 as an open-weight counterpart. A hybrid strategy—one hosted frontier model + one open model—gives you the best mix of performance, privacy, and price.