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Benchmarks

LMArena Leaderboard (Now Arena): Who's #1 on Chatbot Arena Right Now

March 18, 2026
14 min read
LocalAimaster Research Team

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Looking for LMArena? The official live leaderboard is at arena.ai (redirects from lmarena.ai; the project rebranded to "Arena" in January 2026). This page is an independent guide, not the official site. Where the board stands in August 2026: Claude Fable 5 is back at #1 (~1525 ELO) after its July 1 restoration and a July 12 score re-baseline, ahead of a tight Claude Opus 4.8 / GPT-5.5 Pro / Gemini 3.1 Pro Preview cluster. The rest of the July wave is still filing in: the GPT-5.6 family (Luna, Terra, Sol) only joined the official Text Arena on July 31, Kimi K3 shipped its open weights on July 26 and leads the Frontend Code Arena, and Grok 4.5 is so far ranked on the Agent, Vision, and Document boards rather than the main text board. For today's exact order, check arena.ai/leaderboard.

When someone asks "what's the best AI model?" the honest answer is: check the Arena leaderboard. With over 6.8 million blind human votes across 360+ models, it's the closest thing we have to a definitive, unbiased ranking of AI capabilities.

Heads up — the name changed. As of January 28, 2026, LMArena rebranded to simply "Arena" (still at arena.ai). It's the same project — formerly LMSYS Chatbot Arena — now run as an independent company that raised a $150M Series A (led by Felicis and UC Investments, at a ~$1.7B valuation). You'll still see "LMArena" and "Chatbot Arena" used interchangeably across the web.

But reading the Arena correctly requires understanding what the numbers mean, where the rankings fall short, and how to use them for your specific needs. This guide breaks it all down.


What is LMArena? {#what-is-lmarena}

Arena (formerly LMSYS Chatbot Arena, then LMArena, now hosted at arena.ai) is an open platform for crowdsourced AI benchmarking. Created by researchers from UC Berkeley SkyLab in May 2023, it has grown into the most trusted public benchmark for comparing AI models.

How the Blind Voting Works

  1. You submit a prompt — any question, task, or conversation
  2. Two anonymous models respond side-by-side (you don't know which is which)
  3. You vote for the better response (or tie)
  4. Model names are revealed after voting
  5. ELO ratings update based on the outcome

This blind comparison is critical. When users know they're evaluating ChatGPT vs Claude, brand loyalty biases the results. LMArena eliminates this by keeping everything anonymous until after the vote.

Scale and Credibility

StatisticValue
Total votes6.8M+ (as of June 2026)
Models evaluated360+
Categories9 (Overall, Expert, Coding, Math, Creative Writing, etc.)
Created byUC Berkeley, Stanford, UCSD, CMU researchers
Update frequencyContinuous (new models within 1-2 weeks of release)
MethodologyBradley-Terry model (evolved from chess ELO)

No other public benchmark comes close to this volume of human evaluation data. This makes it extremely difficult to manipulate — you'd need to coordinate millions of votes to meaningfully shift a model's ranking.


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Current Rankings (August 2026) {#current-rankings}

Overall Text Leaderboard — Top Models

RankModelProviderELO ScoreNotes
1Claude Fable 5Anthropic~1525Back at #1 after its July 1 restoration; score re-baselined July 12
2Claude Opus 4.8Anthropic~1510Led the board during Fable 5's suspension
3GPT-5.5 ProOpenAI~1510Broad frontier capabilities
4GPT-5.5OpenAI~1506Fast frontier tier
5Gemini 3.1 Pro PreviewGoogle~1505Strong reasoning + multimodal
6Claude Opus 4.7Anthropic~1505Prior-gen flagship
7Grok 4.3xAI~1496Rapid improvement
8Claude Opus 4.6Anthropic~1490Still highly capable
9Qwen 3.7 MaxAlibaba~1488Top proprietary open-lab model
10Gemini 3.1 FlashGoogle~1473Fast + capable
11DeepSeek V4.1 ProDeepSeek~1410Top open-weight model on the text board (for now)

How Fable 5 got back to #1. Fable 5 launched June 9, 2026, briefly topped the board (~1525 ELO), then was suspended worldwide on June 12 under a U.S. export-control order. Anthropic restored access on July 1, 2026 with an enhanced safety classifier, and Arena re-baselined its score on July 12 to count only post-restoration votes. It has since settled back on top. (Its sibling Mythos 5 remains restricted to approved organizations.) See our Claude Fable 5 & Mythos 5 breakdown.

Scores are approximate midpoints and shift daily as votes accumulate. Three July entrants are missing from this table on purpose: the GPT-5.6 family joined the official Text Arena on July 31 and hasn't settled, while Kimi K3 and Grok 4.5 are so far listed on category boards only. Details in the changelog below; visit arena.ai/leaderboard for live rankings.

Latest Ranking Changes {#latest-changes}

A dated log of what has actually moved on the board, most recent first:

  • July 31 — OpenAI's GPT-5.6 family (Sol, Terra, Luna) added to the official Text, Code, Vision, and Document Arenas. First main-board listing for the trio; ELOs still settling.
  • July 27Kimi K3 added to the Agent Arena; Grok 4.5 added to the Vision and Document Arenas.
  • July 26 — Moonshot released Kimi K3's open weights on Hugging Face, a day ahead of its promised ~July 27 date. K3 leads the Frontend Code Arena at 1,679, ahead of Claude Fable 5 (1,631) and GPT-5.6 Sol (1,618).
  • July 13Grok 4.5 entered the Agent Arena, its first Arena listing.
  • July 12Claude Fable 5's scores re-baselined to count only votes cast on or after its July 1 restoration. It has since settled back at #1 overall (~1525).

For the raw feed, see the official Arena leaderboard changelog. We update this log after major board changes.


The July 2026 Model Wave: Who Landed Where {#july-2026-standings}

Four frontier releases hit the board in six weeks, the fastest churn the Arena has seen. A month on, here is where each one actually stands (vendor claims labeled as such):

DateModelMakerWhat it isWhere it stands now
Jul 1Claude Fable 5 (restored)AnthropicMythos-class tier above Opus, $10/$50 per MTokSettled back at #1 overall (~1525) after the July 12 re-baseline to post-restoration votes
Jul 8Grok 4.5xAIFirst Grok built specifically for coding/agentic work, $2/$6 per MTokMusk calls it "Opus-class"; shipped into Cursor on all plans. On Arena so far: Agent, Vision, and Document boards only; reported at 1,558 on Frontend Code Arena
Jul 9GPT-5.6 — Luna, Terra, SolOpenAIThree-tier family; Sol is the flagship coder ($5/$30), Terra is GPT-5.5-class at half price, Luna is the budget tierSol scores 80 on the Artificial Analysis Coding Agent Index — reported 2.8 points above Fable 5 at under half the output tokens. Joined the official Text Arena July 31; overall ELO still settling
Jul 16Kimi K3Moonshot AI2.8T-parameter MoE, the largest open-weight model ever (weights released July 26 on Hugging Face)#1 on Frontend Code Arena (1,679) ahead of Fable 5 (1,631) and GPT-5.6 Sol (1,618); trails both overall by Moonshot's own admission. Artificial Analysis now places it #3 on its Intelligence Index, comparable to Opus 4.8 and GPT-5.5

How to read this: Fable 5 is the only July entrant with a settled main-board score — it had a pre-suspension vote history to rebuild from. GPT-5.6 joined the official Text Arena on July 31, and Kimi K3 and Grok 4.5 are so far ranked on category boards only. New listings typically need 1-2 weeks of votes to stabilize, so treat their overall positions as provisional until mid-August. For the deep dives: GPT-5.6 Luna/Terra/Sol explained, Kimi K3 and whether you can run it, and Claude Fable 5 vs the rest of the Claude lineup.

What the ELO Gap Means

Understanding ELO differences helps you decide if a ranking difference actually matters:

ELO DifferenceWin Rate for Higher ModelPractical Meaning
0-20 points50-53%Essentially tied — pick either
20-50 points53-57%Slight edge — barely noticeable
50-100 points57-64%Meaningful difference — you'll notice
100-200 points64-76%Significant gap — clear quality difference
200+ points76%+Major gap — different tier entirely

Key insight: The top 5 models are often within 20-30 ELO points of each other. This means choosing between them often comes down to pricing, speed, and specific task performance rather than overall quality.


How ELO Ratings Work {#how-elo-works}

LMArena adapted the Bradley-Terry model (evolved from the ELO system used in chess) for AI model ranking.

The Math Behind It

When Model A faces Model B in a blind comparison:

  • Expected outcome is calculated from their current rating difference
  • If the favorite wins: small rating change (expected result)
  • If the underdog wins: large rating change (surprising result)
  • Ties: small adjustment based on rating gap

After thousands of matchups per model, ratings converge to reflect true relative quality. The system is self-correcting — a few bad votes don't meaningfully shift rankings when millions of votes exist.

Why Bradley-Terry Over Raw ELO

LMArena switched from traditional ELO to Bradley-Terry because:

  1. Maximum likelihood estimation — finds the most statistically likely true ranking
  2. Better confidence intervals — shows how certain the ranking is
  3. Handles asymmetric matchups — some model pairs get more votes than others
  4. More stable — less sensitive to vote ordering

The practical effect is the same: higher number = better model, and the gap between numbers predicts win rates.


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Category Leaderboards {#category-leaderboards}

LMArena breaks down rankings into specialized categories. A model's overall rank often differs significantly from its category rank.

Available Categories

CategoryWhat It MeasuresBest For
OverallGeneral chat qualityDefault model choice
ExpertTop 5.5% hardest promptsComplex reasoning, technical work
CodingCode generation, debuggingDeveloper tools, AI coding
MathMathematical reasoningData analysis, science
Creative WritingStory, poetry, contentContent creation
Instruction FollowingFollowing complex instructionsWorkflow automation
Multi-TurnExtended conversationsChatbots, tutoring
Hard PromptsTop 33% difficult promptsGeneral but challenging tasks
OccupationalJob-specific tasksProfessional applications

Why Category Rankings Matter

A model can rank #1 overall but #5 in coding. For example:

  • Claude models tend to gain points on Expert and Coding
  • Gemini models tend to excel on Vision and Multi-Turn
  • Older general-chat models drop significantly on Expert despite decent overall scores
  • DeepSeek V4.1 Pro punches above its weight on Math and Reasoning

If you have a specific use case, check the category leaderboard, not just the overall ranking.

Coding Leaderboard Highlights

For developers, the coding leaderboard is especially relevant:

RankModelCoding ELONotes
1Claude Opus 4.8~1500+Led the coding board through the pre-wave snapshot
2-3GPT-5.5 Pro / Gemini 3.1 Pro~1490+Trade the next spots
4Claude Opus 4.7~1480Strong code review
5DeepSeek V4.1 Pro~1465Best open-weight for code

Coding ELO and overall ELO are separate scales. The coding category has well over 100K votes as of June 2026. This table predates the July wave: Fable 5, GPT-5.6 Sol, and Kimi K3's coding placements are still settling, and K3 leads the separate Frontend Code Arena at 1,679.

For objective coding benchmarks (not just user preference), see our SWE-bench leaderboard guide.


LMArena vs Other Benchmarks {#lmarena-vs-benchmarks}

Comparison of Major AI Benchmarks

BenchmarkTypeWhat It MeasuresStrengthsWeaknesses
LMArenaHuman votesUser preferenceReal-world relevance, unbiasedStyle bias, English-heavy
SWE-benchAutomatedCode correctnessObjective, real bugsPython-only, scaffold-dependent
HumanEvalAutomatedAlgorithm codingClean measurementSynthetic, single-function
MMLUAutomatedKnowledge breadthWide coverageMultiple-choice, memorizable
GPQAAutomatedExpert reasoningPhD-level difficultySmall test set
Arena HardAutomatedHard prompt qualityCheap to runApproximation of human pref

When to Use Each Benchmark

  • Choosing a general assistant: LMArena Overall
  • Choosing a coding agent: SWE-bench Verified
  • Choosing a coding assistant: LMArena Coding
  • Choosing a reasoning model: LMArena Expert + GPQA
  • Choosing a local model: Our Ollama ranking (considers VRAM, speed, quality)

Open-Source Model Rankings {#open-source-rankings}

A major value of LMArena is tracking how open-source models compare to proprietary ones.

Open-Source vs Proprietary Gap (June 2026)

MetricTop ProprietaryTop Open-WeightGap
Overall ELO~1510 (Claude Opus 4.8)~1410 (DeepSeek V4.1 Pro)~55 points (~58% win rate)
Coding ELO~1500 (Claude Opus 4.8)~1465 (DeepSeek V4.1 Pro)~35 points (~55% win rate)
Math ELO~1490 (varies)~1460 (DeepSeek V4.1 Pro)~30 points (~54% win rate)

The gap is closing fast. In 2024, the gap was 100-150 ELO points. By June 2026, the best open-weight model (DeepSeek V4.1 Pro) sits within ~55 points of the top closed-source model — meaning the proprietary advantage is only a 54-58% win rate. For many tasks, open models are effectively equivalent.

August 2026 note: the open-weight picture is about to shift again. Kimi K3's weights landed on Hugging Face July 26, and the 2.8T-parameter MoE already leads the Frontend Code Arena (1,679). Its main text-board ELO is still settling, but expect the top-open-weight slot in the table above to be contested. Details in our Kimi K3 breakdown.

Best Open Models on LMArena

ModelELO RangeCan Run Locally?Hardware Needed
DeepSeek V4.1 Pro~1410Yes (via API or local)48GB+ for full model
Qwen 3.7 Max~1430Partially (quantized)32-64GB+
Llama 4 Maverick~1420Yes (quantized)48GB+
Qwen3-Coder-Next~1400 (coding)Yes46GB (Q4)
Llama 4 Scout~1390Yes24GB (1.78-bit)

For running these locally, see our best Ollama models guide and RTX 5090 vs 5080 comparison.


How to Use LMArena Rankings {#how-to-use}

Step 1: Identify Your Use Case

Don't look at overall rankings unless you need a general-purpose assistant. Use category leaderboards:

  • Developer? → Coding leaderboard + SWE-bench
  • Writer? → Creative Writing leaderboard
  • Researcher? → Expert leaderboard + Math
  • Building a chatbot? → Multi-Turn leaderboard
  • General use? → Overall leaderboard

Step 2: Check the ELO Gap

If two models are within 20 ELO points, they're essentially tied. Pick based on:

  • PriceGPT-OSS is free vs $20/month for ChatGPT Plus
  • Speed — Flash variants are 3-5x faster
  • PrivacyLocal models keep data on your device
  • Context window — ranges from 32K to 10M tokens

Step 3: Test on Your Actual Tasks

LMArena reflects average user preference. Your specific workflow might differ:

  1. Pick 2-3 top-ranked models from the relevant category
  2. Test them on 5-10 real tasks from your work
  3. Track quality, speed, and cost over 1-2 weeks
  4. Choose based on your experience, not just rankings

Step 4: Revisit Quarterly

The leaderboard shifts every few months as new models launch. Set a quarterly reminder to check arena.ai/leaderboard and re-evaluate if a significantly better model has appeared.


Sources {#sources}


FAQ {#faq}

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What is LMArena (Chatbot Arena)?

Arena (formerly LMSYS Chatbot Arena, then LMArena — rebranded to "Arena" on January 28, 2026, now at arena.ai) is the largest crowdsourced AI model benchmark. Created by UC Berkeley researchers, it ranks AI models using blind A/B voting: users chat with two anonymous models and vote for the better response. With over 6.8 million votes across 360+ models, it's considered the most reliable measure of real-world AI quality because it reflects what actual users prefer, not just synthetic benchmark scores.

How does the LMArena ELO rating system work?

LMArena uses the Bradley-Terry model (evolved from chess ELO). When two models face off in a blind comparison, the winner gains rating points and the loser drops. The exchange depends on expected outcomes: if a top model beats a weak one, few points change. If an underdog wins, the swing is large. A 100-point ELO gap means the higher model wins ~64% of the time. A 200-point gap means ~76% win rate. Ratings stabilize after thousands of votes per model.

What is the difference between LMArena and SWE-bench?

LMArena measures human preference through blind voting — which model "feels" better to use for chat, writing, reasoning, and coding. SWE-bench measures objective coding correctness — can the model actually fix real GitHub bugs? A model might rank high on LMArena (clear explanations, good formatting) but lower on SWE-bench (struggles with complex codebases). Use LMArena rankings for choosing a chat/assistant model. Use SWE-bench for choosing a coding agent. Both together give the complete picture.

Which AI model has the highest LMArena ELO rating?

As of August 2026, Claude Fable 5 holds #1 on the overall text leaderboard at roughly 1525 ELO. Arena re-baselined its score on July 12 to count only votes cast since its July 1 restoration, and it settled back on top. Behind it sits a tight cluster: Claude Opus 4.8 (~1510), GPT-5.5 Pro (~1510), GPT-5.5, Gemini 3.1 Pro Preview, and Claude Opus 4.7, all within about 20 points of each other. Expect more movement: the GPT-5.6 family (Sol, Terra, Luna) only joined the official Text Arena on July 31, and Kimi K3 is so far ranked on the Agent and Frontend Code boards. Check arena.ai/leaderboard for today's exact order.

Are LMArena rankings reliable?

Yes, Arena (LMArena) is considered the most reliable public AI benchmark because: (1) Over 6.8 million real human votes — impossible to game at scale, (2) Blind comparison — users don't know which model they're judging, (3) Diverse user base — not just researchers, but real users with real tasks, (4) Statistical confidence — models need thousands of votes before ranking stabilizes. Limitations: voting skews toward English speakers, chat-style interactions, and users may prefer style over substance. Category leaderboards (coding, math, creative writing) address some of this.

How do open-source models rank on LMArena?

Open-source (open-weight) models have improved dramatically. As of mid-2026, DeepSeek V4.1 Pro is the highest-ranked open-weight model on the main text board and sits within ~55 ELO points of the top closed-source model. That lead is now under threat: Kimi K3's weights were released on July 26, 2026, making it the largest open-weight model ever, and it already leads the Frontend Code Arena while its overall text-board ELO settles. Qwen 3.7 Max scores competitively on the Expert leaderboard, and Llama 4 Maverick performs well on general tasks. However, proprietary models (Claude, Gemini, GPT-5) still hold the top 5-10 positions on most category leaderboards. The gap has narrowed significantly — meaning the win-rate difference is often under 10%.

What are Arena Hard and Arena Expert?

Arena Hard filters for the toughest third of all Arena prompts, producing wider score spreads between models. Arena Expert (launched November 2025) is even stricter — only the top 5.5% of prompts by reasoning depth and specificity. Expert rankings often differ from overall rankings: top reasoning models like Claude Opus 4.8 and Qwen 3.7 Max gain significant points on Expert, while older general-chat models drop. Use the Expert leaderboard if your use case involves complex reasoning, multi-step problems, or technical work.

How often is the LMArena leaderboard updated?

The leaderboard updates continuously as new votes come in, but major ranking changes are announced in the changelog at arena.ai/blog/leaderboard-changelog. New models are typically added within 1-2 weeks of public release. As of June 2026, the platform evaluates 360+ models across categories: Overall, Expert, Coding, Math, Creative Writing, Instruction Following, Multi-Turn, Hard Prompts, and Occupational. Visit arena.ai/leaderboard for the latest live rankings.

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📅 Published: March 18, 2026🔄 Last Updated: August 3, 2026✓ Manually Reviewed
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