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.
- Top open-weight
GLM-5.3-Flash1363Z.ai
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 · Chinese
36 modelsSorted 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 · Chinese | License | ||
|---|---|---|---|---|---|
| Score↓ | Rank | Votes | |||
| 1 | 1363±23 | 15 | 699 | MIT | |
| 2 | 1346±20 | 25 | 1,027 | Modified MIT | |
| 3 | 1328±34 | 36 | 273 | Modified MIT | |
| 4 | 1327±42 | 37 | 199 | MIT | |
| 5 | 1326±14 | 38 | 2,734 | Apache 2.0 | |
| 6 | 1326±17 | 39 | 1,580 | MIT | |
| 7 | 1326±30 | 40 | 409 | Apache 2.0 | |
| 8 | 1324±16 | 41 | 1,767 | Modified MIT | |
| 9 | 1314±15 | 43 | 2,045 | Apache 2.0 | |
| 10 | 1312±17 | 45 | 1,609 | Apache 2.0 | |
| 11 | 1311±20 | 46 | 1,048 | MiniMax Community License | |
| 12 | 1301±20 | 51 | 1,011 | Apache 2.0 | |
| 13 | 1301±15 | 52 | 1,997 | Apache 2.0 | |
| 14 | TInkling-SmallThinking MachinesOpen | 1292±28 | 56 | 461 | Apache 2.0 |
| 15 | 1283±19 | 62 | 998 | Apache 2.0 | |
| 16 | 1244±38 | 76 | 287 | Modified MIT | |
| 17 | 1240±26 | 77 | 569 | Apache 2.0 | |
| 18 | 1219±43 | 85 | 164 | Apache 2.0 | |
| 19 | 1217±41 | 87 | 166 | MIT | |
| 20 | 1164±21 | 96 | 1,020 | Gemma | |
| 21 | 1163±23 | 97 | 847 | Apache 2.0 | |
| 22 | 1155±21 | 99 | 1,251 | Apache 2.0 | |
| 23 | 1141±34 | 101 | 294 | Llama 4 | |
| 24 | 1107±55 | 105 | 119 | Qwen | |
| 25 | 1106±36 | 106 | 278 | Llama | |
| 26 | 1083±51 | 108 | 156 | MRL | |
| 27 | 1072±45 | 109 | 291 | Qwen | |
| 28 | OInternVL2 26BOpenGVLabOpen | 1065±48 | 110 | 287 | MIT |
| 29 | 999±46 | 116 | 298 | Apache 2.0 | |
| 30 | 952±43 | 120 | 393 | Llama 3.2 | |
| 31 | 951±45 | 121 | 335 | Apache 2.0 | |
| 32 | OInternVL2 4BOpenGVLabOpen | 950±55 | 122 | 177 | MIT |
| 33 | 936±55 | 124 | 182 | Apache 2.0 | |
| 34 | LLLaVA 1.6 34BOpen | 915±47 | 126 | 415 | Apache 2.0 |
| 35 | 915±49 | 127 | 266 | Llama 3.2 | |
| 36 | 890±54 | 128 | 171 | Apache 2.0 | |
Scores: LMArena leaderboard-dataset (lmarena-ai on Hugging Face, CC-BY-4.0, vision config), Chinese published Oct 19, 2026. Please credit the source when citing.
Sources & methodology
- LMLMArena leaderboard dataset (Hugging Face, CC-BY-4.0)
Vision config, published Oct 19, 2026; 167 models on the overall board.
- Scores are Bradley-Terry Arena ratings fitted from human votes between two anonymous answers to the same image prompt.
- 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.
- Fields without public data (price, context length, release date…) are not shown; blanks are –. Nothing is estimated.
- 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.