LMArena Leaderboard (Now Arena): Who's #1 on Chatbot Arena Right Now
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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
- You submit a prompt — any question, task, or conversation
- Two anonymous models respond side-by-side (you don't know which is which)
- You vote for the better response (or tie)
- Model names are revealed after voting
- 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
| Statistic | Value |
|---|---|
| Total votes | 6.8M+ (as of June 2026) |
| Models evaluated | 360+ |
| Categories | 9 (Overall, Expert, Coding, Math, Creative Writing, etc.) |
| Created by | UC Berkeley, Stanford, UCSD, CMU researchers |
| Update frequency | Continuous (new models within 1-2 weeks of release) |
| Methodology | Bradley-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
| Rank | Model | Provider | ELO Score | Notes |
|---|---|---|---|---|
| 1 | Claude Fable 5 | Anthropic | ~1525 | Back at #1 after its July 1 restoration; score re-baselined July 12 |
| 2 | Claude Opus 4.8 | Anthropic | ~1510 | Led the board during Fable 5's suspension |
| 3 | GPT-5.5 Pro | OpenAI | ~1510 | Broad frontier capabilities |
| 4 | GPT-5.5 | OpenAI | ~1506 | Fast frontier tier |
| 5 | Gemini 3.1 Pro Preview | ~1505 | Strong reasoning + multimodal | |
| 6 | Claude Opus 4.7 | Anthropic | ~1505 | Prior-gen flagship |
| 7 | Grok 4.3 | xAI | ~1496 | Rapid improvement |
| 8 | Claude Opus 4.6 | Anthropic | ~1490 | Still highly capable |
| 9 | Qwen 3.7 Max | Alibaba | ~1488 | Top proprietary open-lab model |
| 10 | Gemini 3.1 Flash | ~1473 | Fast + capable | |
| 11 | DeepSeek V4.1 Pro | DeepSeek | ~1410 | Top 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 27 — Kimi 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 13 — Grok 4.5 entered the Agent Arena, its first Arena listing.
- July 12 — Claude 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):
| Date | Model | Maker | What it is | Where it stands now |
|---|---|---|---|---|
| Jul 1 | Claude Fable 5 (restored) | Anthropic | Mythos-class tier above Opus, $10/$50 per MTok | Settled back at #1 overall (~1525) after the July 12 re-baseline to post-restoration votes |
| Jul 8 | Grok 4.5 | xAI | First Grok built specifically for coding/agentic work, $2/$6 per MTok | Musk 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 9 | GPT-5.6 — Luna, Terra, Sol | OpenAI | Three-tier family; Sol is the flagship coder ($5/$30), Terra is GPT-5.5-class at half price, Luna is the budget tier | Sol 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 16 | Kimi K3 | Moonshot AI | 2.8T-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 Difference | Win Rate for Higher Model | Practical Meaning |
|---|---|---|
| 0-20 points | 50-53% | Essentially tied — pick either |
| 20-50 points | 53-57% | Slight edge — barely noticeable |
| 50-100 points | 57-64% | Meaningful difference — you'll notice |
| 100-200 points | 64-76% | Significant gap — clear quality difference |
| 200+ points | 76%+ | 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:
- Maximum likelihood estimation — finds the most statistically likely true ranking
- Better confidence intervals — shows how certain the ranking is
- Handles asymmetric matchups — some model pairs get more votes than others
- 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
| Category | What It Measures | Best For |
|---|---|---|
| Overall | General chat quality | Default model choice |
| Expert | Top 5.5% hardest prompts | Complex reasoning, technical work |
| Coding | Code generation, debugging | Developer tools, AI coding |
| Math | Mathematical reasoning | Data analysis, science |
| Creative Writing | Story, poetry, content | Content creation |
| Instruction Following | Following complex instructions | Workflow automation |
| Multi-Turn | Extended conversations | Chatbots, tutoring |
| Hard Prompts | Top 33% difficult prompts | General but challenging tasks |
| Occupational | Job-specific tasks | Professional 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:
| Rank | Model | Coding ELO | Notes |
|---|---|---|---|
| 1 | Claude Opus 4.8 | ~1500+ | Led the coding board through the pre-wave snapshot |
| 2-3 | GPT-5.5 Pro / Gemini 3.1 Pro | ~1490+ | Trade the next spots |
| 4 | Claude Opus 4.7 | ~1480 | Strong code review |
| 5 | DeepSeek V4.1 Pro | ~1465 | Best 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
| Benchmark | Type | What It Measures | Strengths | Weaknesses |
|---|---|---|---|---|
| LMArena | Human votes | User preference | Real-world relevance, unbiased | Style bias, English-heavy |
| SWE-bench | Automated | Code correctness | Objective, real bugs | Python-only, scaffold-dependent |
| HumanEval | Automated | Algorithm coding | Clean measurement | Synthetic, single-function |
| MMLU | Automated | Knowledge breadth | Wide coverage | Multiple-choice, memorizable |
| GPQA | Automated | Expert reasoning | PhD-level difficulty | Small test set |
| Arena Hard | Automated | Hard prompt quality | Cheap to run | Approximation 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)
| Metric | Top Proprietary | Top Open-Weight | Gap |
|---|---|---|---|
| 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
| Model | ELO Range | Can Run Locally? | Hardware Needed |
|---|---|---|---|
| DeepSeek V4.1 Pro | ~1410 | Yes (via API or local) | 48GB+ for full model |
| Qwen 3.7 Max | ~1430 | Partially (quantized) | 32-64GB+ |
| Llama 4 Maverick | ~1420 | Yes (quantized) | 48GB+ |
| Qwen3-Coder-Next | ~1400 (coding) | Yes | 46GB (Q4) |
| Llama 4 Scout | ~1390 | Yes | 24GB (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:
- Price — GPT-OSS is free vs $20/month for ChatGPT Plus
- Speed — Flash variants are 3-5x faster
- Privacy — Local 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:
- Pick 2-3 top-ranked models from the relevant category
- Test them on 5-10 real tasks from your work
- Track quality, speed, and cost over 1-2 weeks
- 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}
- Arena (formerly LMArena) Official Leaderboard — Live rankings with 6.8M+ votes
- LMSYS Blog: Chatbot Arena Launch — Original methodology paper
- "LMArena is now Arena" — rebrand announcement (Jan 28, 2026) — Name change details
- Arena Leaderboard Changelog — Recent model additions and updates
- How to Read ELO Ratings — Statology — ELO rating interpretation guide
- LMArena on Hugging Face — Alternative leaderboard interface
- Moonshot AI releases weights for Kimi K3 — Tom's Hardware — Kimi K3 open-weight release coverage (July 26, 2026)
FAQ {#faq}
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