Vision Model Leaderboard: Best Vision-Language Models

Ranked by LMArena Vision Arena scores: users upload an image, ask a question, and vote between two anonymous models’ answers.

#1 DiagramsData Oct 19, 2026
Claude Fable 5 (High)Anthropic · 1357

In LMArena Vision Arena data published Oct 19, 2026, Anthropic’s Claude Fable 5 (High) ranks #1 of 167 models with 1325 (14,714 votes).

Full leaderboard · Diagrams

30 models

Sorted by LMArena score by default; ± is half the 95% confidence interval. Category columns show the model’s score on that sub-board; – means no score.

# Model LMArena · Diagrams License
Score Rank Votes
1 GLM-5.3-FlashZ.aiOpen 1317±10 20 3,995 MIT
2 Kimi K2.6Moonshot AIOpen 1298±10 35 4,304 Modified MIT
3 DeepSeek-V4.1-Flash-20260910 (Max)DeepSeekOpen 1297±19 37 1,085 MIT
4 Qwen3.8-27BAlibabaOpen 1285±14 46 1,863 Apache 2.0
5 Gemma 4 31BGoogleOpen 1283±8 49 10,238 Apache 2.0
6 Kimi K2.5 (Thinking)Moonshot AIOpen 1282±8 50 7,385 Modified MIT
7 MiMo-V2.6-ProXiaomiOpen 1281±27 51 473 MIT
8 Qwen3.5-397B-A17BAlibabaOpen 1277±7 52 8,623 Apache 2.0
9 MiMo-V2.6-FlashXiaomiOpen 1276±20 54 919 MIT
10 MiMo-V2.5XiaomiOpen 1269±8 59 7,723 MIT
11 MiniMax-M3MiniMaxOpen 1266±9 62 5,289 MiniMax Community License
12 Gemma 4 26B A4BGoogleOpen 1264±9 66 6,239 Apache 2.0
13 Kimi K2.5 (Instant)Moonshot AIOpen 1257±19 68 955 Modified MIT
14 Qwen3-VL-235B-A22B-InstructAlibabaOpen 1255±11 70 2,727 Apache 2.0
15 Mistral Medium 3.5Mistral AIOpen 1254±16 73 1,538 Modified MIT
16 TInkling-SmallThinking MachinesOpen 1251±14 75 2,071 Apache 2.0
17 Qwen3.5-122B-A10BAlibabaOpen 1249±11 77 3,455 Apache 2.0
18 Qwen3.5-27BAlibabaOpen 1249±8 78 7,927 Apache 2.0
19 Mistral Large 3Mistral AIOpen 1240±13 85 2,334 Apache 2.0
20 NNVIDIA-Nemotron-3.5-Super-VL-120B-A12B (NVFP4)NVIDIAOpen 1223±24 88 651 OpenMDW-1.1
21 Qwen3-VL-235B-A22B-ThinkingAlibabaOpen 1214±27 91 464 Apache 2.0
22 GLM-4.6VZ.aiOpen 1182±23 102 578 MIT
23 Gemma 3 27B ITGoogleOpen 1156±15 109 1,715 Gemma
24 SStep-3StepFunOpen 1155±30 110 347 Apache 2.0
25 GLM-4.5VZ.aiOpen 1151±30 113 369 MIT
26 Llama 4 Maverick 17B 128E InstructMetaOpen 1134±19 118 947 Llama 4
27 Mistral Small 3.2 (2506)Mistral AIOpen 1125±18 119 1,166 Apache 2.0
28 Llama 4 Scout 17B 16E InstructMetaOpen 1125±20 120 845 Llama
29 Mistral Small 3.1 24B Instruct (2503)Mistral AIOpen 1119±16 121 1,627 Apache 2.0
30 Molmo 2 8BAi2Open 1072±39 123 284 Apache 2.0

Scores: LMArena leaderboard-dataset (lmarena-ai on Hugging Face, CC-BY-4.0, vision config), Diagrams published Oct 19, 2026. Please credit the source when citing.

Sources & methodology

  1. Scores are Bradley-Terry Arena ratings fitted from human votes between two anonymous answers to the same image prompt.
  2. Categories (OCR, diagrams, homework, captioning, Chinese/English…) are separate LMArena boards computed on prompts of that type, not a breakdown of the overall score; ranks can differ a lot.
  3. Fields without public data (price, context length, release date…) are not shown; blanks are –. Nothing is estimated.
  4. Synced daily. JSON API: /api/public/vision-leaderboard.php

This week

Collecting data: 1/7 days recorded; weekly changes appear after 7 days

Synced automatically once a day; ranks are compared only between real snapshots, never backfilled.

FAQ

What is the best vision-language model right now?

In LMArena Vision Arena data published Oct 19, 2026, Anthropic’s Claude Fable 5 (High) ranks #1 of 167 models with 1325 (14,714 votes).

What is each vendor’s best vision model?

Top overall entry per vendor: Anthropic Claude Fable 5 (High) (1325, #1), Google Gemini 3.8 Flash (High) (1315, #7), Alibaba Qwen3.8-Max (1313, #10), Meta Muse Spark 1.3 (Max) (1308, #12), OpenAI GPT-5.4 (High) (1303, #18).

Which model is best at OCR?

On LMArena’s OCR board (123 models), Claude Fable 5 (High) is #1 with 1337.

What is the best open-weight vision model?

50 open-weight models are listed; the top one is GLM-5.3-Flash (1294, MIT).

How is this different from benchmarks like MMMU?

MMMU and similar benchmarks auto-score a fixed question set; this board comes from blind human votes on people’s own images and questions, reflecting real-use preference. They are not convertible.