Text-to-Image Arena Leaderboard
The latest AI text-to-image model leaderboard based on Text-to-Image Arena anonymous user voting. Covers Elo scores, confidence intervals, and vote counts for GPT-Image, FLUX, Midjourney, DALL-E, and more.
Top Model
GPT-image-2 (medium)
Top Score
1,380
Model Count
75
Data version
2026年08月07日
Data source: LM Arena
About This Leaderboard
This leaderboard ranks AI text-to-image models by generation quality. Data comes from LMArena's Text-to-Image Arena track, evaluated through anonymous blind testing by real users.
Methodology Overview
Blind testing: Users submit text prompts, two anonymous models generate images, and users vote for the better result.
Elo scoring: Based on the Bradley-Terry model, scientifically measuring each model's relative strength in text-to-image generation.
Diverse generation scenarios: Covers photorealistic scenes, artistic illustration, product design, character creation, and more.
DataLearner provides in-depth analysis on top of the raw data, linking leaderboard models to the DataLearner model database so you can quickly access model details, API pricing, benchmark scores, and more.
Ranking Table
| Rank | Model | Score | 95% CI | Votes | Organization | License |
|---|---|---|---|---|---|---|
GPT-image-2 (medium)OpenAI | 1,380 | +/-5 | 69,194 | OpenAI | Proprietary | |
| reve-2.1Reve | 1,302 | +/-8 | 7,598 | Reve | Proprietary | |
muse-imageMeta | 1,283 | +/-7 | 14,510 | Meta | Proprietary | |
| 4 | reve-2.0Reve | 1,270 | +/-6 | 14,650 | Reve | Proprietary |
| 5 | gemini-3.1-flash-image (nano-banana-2) [web-search]Google | 1,263 | +/-5 | 27,910 | Proprietary | |
| 6 | Qwen-Image-3.0阿里巴巴 | 1,258 | +/-10 | 3,735 | 阿里巴巴 | Proprietary |
| 7 | seedream-5.0-proBytedance | 1,257 | +/-5 | 26,510 | Bytedance | Proprietary |
| 8 | mai-image-2.5 MicrosoftAI | 1,256 | +/-5 | 44,940 | AI | Proprietary |
| 9 | gemini-3.1-flash-lite-image (nano-banana-2-lite)Google | 1,251 | +/-6 | 16,419 | Proprietary | |
| 10 | gemini-3-pro-image-2k (nano-banana-pro)Google | 1,246 | +/-3 | 138,756 | Proprietary | |
| 11 | GPT Image 1OpenAI | 1,239 | +/-3 | 142,427 | OpenAI | Proprietary |
| 12 | Nano Banana ProGoogle Deep Mind | 1,232 | +/-5 | 82,835 | Google Deep Mind | Proprietary |
| 13 | 1,228 | +/-4 | 48,786 | xAI | Proprietary | |
| 14 | ideogram-4.0-qualityIdeogram | 1,206 | +/-5 | 27,891 | Ideogram | Ideogram Open Model |
| 15 | qwen-image-2.0-pro-2026-06-22Alibaba | 1,191 | +/-6 | 12,026 | Alibaba | Proprietary |
| 16 | uni-1.1-max LumaAI | 1,188 | +/-6 | 13,491 | AI | Proprietary |
| 17 | MAI Image 2Microsoft Azure | 1,182 | +/-5 | 49,207 | Microsoft Azure | Proprietary |
| 18 | Cosmos3-Super-Text2Image (Agentic)Nvidia | 1,181 | +/-10 | 2,957 | Nvidia | OpenMDW-1.1 |
| 19 | uni-1.1 LumaAI | 1,180 | +/-5 | 26,927 | AI | Proprietary |
| 20 | 1,172 | +/-3 | 217,461 | xAI | Proprietary | |
| 21 | recraft-v4.1-utility-proRecraft | 1,169 | +/-11 | 2,519 | Recraft | Proprietary |
| 22 | flux-2-max Black ForestLabs | 1,162 | +/-4 | 117,464 | Labs | Proprietary |
| 23 | 1,161 | +/-4 | 93,711 | xAI | Proprietary | |
| 24 | Cosmos3-Super-Text2ImageNvidia | 1,158 | +/-9 | 4,669 | Nvidia | OpenMDW-1.1 |
| 25 | flux-2-flex Black ForestLabs | 1,156 | +/-3 | 149,402 | Labs | Proprietary |
| 26 | flux-2-pro Black ForestLabs | 1,155 | +/-3 | 175,196 | Labs | Proprietary |
| 27 | reve-v1.5Reve | 1,154 | +/-4 | 36,799 | Reve | Proprietary |
| 28 | HunyuanImage-3.0-Instruct腾讯AI实验室 | 1,151 | +/-3 | 173,366 | 腾讯AI实验室 | tencent-hunyuan-community |
| 29 | Gemini 2.5 Flash Image PreviewGoogle Deep Mind | 1,150 | +/-3 | 822,940 | Google Deep Mind | Proprietary |
| 30 | Imagen Ultra 4.0Google Deep Mind | 1,148 | +/-4 | 389,010 | Google Deep Mind | Proprietary |
| 31 | Seedance 2.0字节跳动Seed团队 | 1,146 | +/-3 | 234,737 | 字节跳动Seed团队 | Proprietary |
| 32 | flux-2-dev Black ForestLabs | 1,146 | +/-4 | 66,740 | Labs | flux-non-commercial-license |
| 33 | Seedream 4 2K字节跳动Seed团队 | 1,140 | +/-7 | 12,639 | 字节跳动Seed团队 | Proprietary |
| 34 | Seedream 5.0 Lite字节跳动Seed团队 | 1,136 | +/-4 | 96,040 | 字节跳动Seed团队 | Proprietary |
| 35 | Wan2.1-T2V-14B阿里巴巴 | 1,136 | +/-3 | 182,381 | 阿里巴巴 | Proprietary |
| 36 | recraft-v4.1-proRecraft | 1,130 | +/-11 | 2,682 | Recraft | Proprietary |
| 37 | Imagen 4.0Google Deep Mind | 1,129 | +/-3 | 532,862 | Google Deep Mind | Proprietary |
| 38 | Qwen-Image-2512阿里巴巴 | 1,126 | +/-4 | 91,329 | 阿里巴巴 | Apache 2.0 |
| 39 | krea-2-mediumKrea | 1,122 | +/-5 | 26,584 | Krea | Proprietary |
| 40 | hidream-o1-imageHiDream | 1,118 | +/-4 | 34,179 | HiDream | MIT |
| 41 | Wan2.1-T2V-14B阿里巴巴 | 1,117 | +/-3 | 230,234 | 阿里巴巴 | Proprietary |
| 42 | Seedream 4 (FAL)字节跳动Seed团队 | 1,116 | +/-7 | 11,911 | 字节跳动Seed团队 | Proprietary |
| 43 | GPT Image 1OpenAI | 1,115 | +/-3 | 265,373 | OpenAI | Proprietary |
| 44 | recraft-v4Recraft | 1,113 | +/-4 | 99,721 | Recraft | Proprietary |
| 45 | Seedream 4 High Res (FAL)字节跳动Seed团队 | 1,113 | +/-3 | 175,579 | 字节跳动Seed团队 | Proprietary |
| 46 | Kling 2.5 Turbo昆仑万维 | 1,110 | +/-6 | 18,650 | 昆仑万维 | krea-2-community-license |
| 47 | GPT-Image-1-miniOpenAI | 1,109 | +/-3 | 164,065 | OpenAI | Proprietary |
| 48 | krea-2-largeKrea | 1,105 | +/-5 | 26,191 | Krea | Proprietary |
| 49 | Wan2.7 Image Pro阿里巴巴 | 1,103 | +/-5 | 28,745 | 阿里巴巴 | Proprietary |
| 50 | Wan2.7 Image阿里巴巴 | 1,100 | +/-5 | 29,087 | 阿里巴巴 | Proprietary |
| 51 | MAI Image 1Microsoft Azure | 1,093 | +/-4 | 94,700 | Microsoft Azure | Proprietary |
| 52 | Seedream 3字节跳动Seed团队 | 1,082 | +/-5 | 37,009 | 字节跳动Seed团队 | Proprietary |
| 53 | Z-Image-Turbo阿里巴巴 | 1,081 | +/-6 | 20,856 | 阿里巴巴 | Apache 2.0 |
| 54 | flux-1-kontext-max Black ForestLabs | 1,074 | +/-3 | 65,741 | Labs | Proprietary |
| 55 | flux-2-klein-9b Black ForestLabs | 1,070 | +/-3 | 142,762 | Labs | flux-non-commercial-license |
| 56 | Qwen Image阿里巴巴 | 1,060 | +/-3 | 706,588 | 阿里巴巴 | Apache 2.0 |
| 57 | flux-1-kontext-pro Black ForestLabs | 1,059 | +/-3 | 331,629 | Labs | Proprietary |
| 58 | Imagen 3.0 (002)Google Deep Mind | 1,058 | +/-3 | 360,739 | Google Deep Mind | Proprietary |
| 59 | Qwen Image阿里巴巴 | 1,057 | +/-3 | 84,835 | 阿里巴巴 | Apache 2.0 |
| 60 | ideogram-v3-qualityIdeogram | 1,049 | +/-4 | 115,578 | Ideogram | Proprietary |
| 61 | photon LumaAI | 1,035 | +/-4 | 127,700 | AI | Proprietary |
| 62 | p-image Proprietary | 1,034 | +/-4 | 103,942 | — | — |
| 63 | flux-2-klein-4b Black ForestLabs | 1,030 | +/-3 | 144,584 | Labs | Apache 2.0 |
| 64 | runway-gen4Runway | 1,025 | +/-4 | 51,775 | Runway | Proprietary |
| 65 | recraft-v3Recraft | 1,021 | +/-4 | 191,960 | Recraft | Proprietary |
| 66 | flux-1.1-pro Black ForestLabs | 1,016 | +/-3 | 70,460 | Labs | Proprietary |
| 67 | lucid-origin LeonardoAI | 1,013 | +/-3 | 285,797 | AI | Proprietary |
| 68 | ideogram-v2Ideogram | 1,013 | +/-4 | 72,090 | Ideogram | Proprietary |
| 69 | GLM-Image智谱AI | 1,010 | +/-9 | 4,626 | 智谱AI | MIT |
| 70 | Gemini 2.5 Flash Image PreviewGoogle Deep Mind | 975 | +/-3 | 257,623 | Google Deep Mind | Proprietary |
| 71 | FLUX.1-Kontext-devBlack Forest Labs | 969 | +/-4 | 49,221 | Black Forest Labs | Open |
| 72 | DALL·E3OpenAI | 968 | +/-4 | 239,491 | OpenAI | Proprietary |
| 73 | flux-1-kontext-dev Black ForestLabs | 940 | +/-4 | 215,833 | Labs | flux-1-dev-non-commercial-license |
| 74 | stable-diffusion-v35-large Open | 938 | +/-5 | 23,396 | — | — |
| 75 | BAGEL字节跳动Seed团队 | 898 | +/-6 | 12,404 | 字节跳动Seed团队 | Apache 2.0 |
Data is for reference only. Official sources are authoritative. Click model names to view DataLearner model profiles.
2026-08 Market Signals
Current Best (SOTA)
GPT-Image-1.5 High-Fidelity (OpenAI)
Gemini 3 Pro Image Preview 2K (Google)
Gemini 3 Pro Image Preview (Google)
Best China Model
HunyuanImage-3.0 (腾讯)
Seedream-4.5 (字节跳动)
Qwen-Image-2512 (阿里)
Best Open Model
- •Qwen-Image-2512 (阿里)
- •Z-Image-Turbo (阿里)
- •GLM-Image (智谱)
FAQ
What is the difference between text-to-image and image editing?
Text-to-image creates a new image from a prompt. Image editing modifies an existing image, which is better for local changes, style transfer, and production refinements.
Which models are suitable for commercial poster design?
For commercial posters, prioritize models with strong text rendering, controllable composition, high-resolution output, and license terms that fit your use case. The top-ranked model may not be the best option if typography or brand control matters most.
What is prompt engineering?
Prompt engineering means structuring the text input to guide the generated image. Clear descriptions of subject, style, lighting, composition, and constraints usually improve quality and alignment.
How large is the gap between open and closed image models?
The gap has narrowed, especially for customizable open models. Closed models can still lead on instruction following, typography, and detail consistency, while open models are attractive for local deployment, tuning, and cost control.






