Generative AI: The 2026 Field Guide

Public benchmarks should be taken with a grain of salt, we will get to why in a moment, but they are a useful starting point for comparing models, and the patterns in them are real. The two most-watched leaderboards are the Artificial Analysis text-to-image and text-to-videoText-to-videoGenerating video from text alone, with no starting image. benchmarks, which use human preference voting (people are shown two outputs from different models for the same promptPromptThe text description you provide to a model to specify what you want it to generate. and asked which they prefer), aggregate the votes into ELOEloA rating derived from head-to-head human preference votes, borrowed from chess, used to rank models on leaderboards.-style ratings, and publish the results.

Here are approximate snapshots as of early 2026, drawn from those benchmarks and from the public claims of the labs themselves. Numbers will be out of date by the time you read this, but the rough ordering is what matters.

IMAGE MODEL LEADERBOARD

Model

Lab

Architecture

Open weightsOpen weightsModels whose trained parameters are publicly downloadable, allowing anyone to run them locally or fine-tune them. Contrasts with closed/API-only models.

Strengths

Nano Banana Pro

Google

Gemini-native

Text rendering, world knowledge, 14-image composition

FLUX 1.1 [pro]

Black Forest Labs

12B MultimodalMultimodalHandling more than one kind of data, for example text and images together, in a single model. Diffusion TransformerTransformerThe neural network architecture built on attention that underpins modern language and, increasingly, image and video models. It scales well and handles long-range relationships better than convolutional networks. (MM-Diffusion Transformer (DiTDiT (Diffusion Transformer)A diffusion model architecture that uses a Transformer instead of a U-Net. Introduced by Peebles and Xie in 2022. Scales much better than U-Nets and is the basis for almost all current frontier models.))

No (pro)

General photoreal, prompt adherencePrompt adherenceHow faithfully a model follows the specifics of your prompt, meaning the objects, counts, colors, spatial relationships, and text you asked for. High adherence means you get what you described, not just something in the right neighborhood.

Ideogram 3.0

Ideogram

Latent diffusionLatent diffusionDiffusion that operates in a compressed 'latent' space produced by a VAE, rather than directly in pixel space. Dramatically reduces compute cost. Used by essentially every modern image and video model.

Typography, design, posters

Recraft V3

Recraft

Latent diffusion + vector

Long-form text, vector output

GPT-Image (4o)

OpenAI

LLM-native

Instruction following, edits

Seedream 5.0

ByteDance

MM-DiTMM-DiT (Multimodal Diffusion Transformer)A variant of DiT that processes text and image (or audio) tokens in separate parallel streams joined by attention. Used by Stable Diffusion 3, FLUX, and most newer models. Improves prompt following significantly.

Cinematic, e-commerce design

FLUX [dev]

Black Forest Labs

12B MM-DiT

Yes (non-comm)

Open ecosystem workhorse

Qwen-Image 2.0

Alibaba

7B MM-DiT

Yes

Chinese text, open license

SD 3.5 Large

Stability AI

8B MM-DiT

Yes

Permissive license

Midjourney V8.2

Midjourney

DiT (closed)

Aesthetic distinctiveness

VIDEO MODEL LEADERBOARD

Model

Lab

Audio

Max length

Cost / sec

Veo 3.1

Google

Yes (joint)

8s @ 1080p

$0.50 - $1.50

Kling 3.0

Kuaishou

Yes (joint)

15s cinematic

$0.30 - $0.80

Sora 2 Pro

OpenAI

Yes (joint)

25s @ 1792x1024

$0.50 - $2.00

Seedance 2.0

ByteDance

Yes

12s @ 1080p

$0.20 - $0.60

Hailuo 2.3

MiniMax

Partial

10s @ 1080p

$0.05 - $0.15

Runway Gen-4.5

Runway

Separate

10s @ 1080p

$0.25 - $0.60

Wan 2.5

Alibaba

Yes

10s @ 1080p

Free (open)

Luma Ray 3

Luma

Separate

10s @ 4K

$0.30 - $0.80

Pika 2.2

Pika

Separate

10s @ 1080p

$0.10 - $0.30

Hunyuan Video

Tencent

Separate

Variable

Free (open)

Notice the bifurcation. The top of the leaderboard is dominated by closed-source models from labs with massive resources (Google, Kuaishou, OpenAI, ByteDance). The middle is a mix of mid-priced commercial offerings (Hailuo, Runway, Luma, Pika). The bottom is open-source models (Wan, Hunyuan) that are free to use but require you to bring your own infrastructure. There is no single 'best' model, there is a quality-cost-openness trade-off, and your right answer depends on which axis matters most to you.

TRAINING COST COMPARISON

Model

Year

Type

Estimated cost

Compute scale

Stable Diffusion 1.5

2022

Image

~$600K

150K A100-hours

SDXL

2023

Image

$2-3M

Several hundred K A100-hours

Stable Diffusion 3

2024

Image

$5-10M

Multi-week multi-cluster

FLUX (BFL)

2024

Image

$10-30M (est)

Undisclosed

Open-Sora 2.0

2025

Video

$200K

Optimized open-source

Hunyuan Video

2024

Video

$5-15M (est)

13B params

Sora 1

2024

Video

$100M+ (est)

Months on thousands of GPUs

Veo 3

2025

Video

$100M+ (est)

Google internal

Kling 3.0

2026

Video

Undisclosed

Kuaishou internal

MODEL FUNDING & VALUATION

Lab

Total raised

Last valuation

Notable backers

OpenAI

$60B+

$500B+

Microsoft, Thrive, Khosla

Anthropic

$25B+

$170B+

Google, Amazon

Black Forest Labs

$450M+

$3.25B (Dec 2025)

a16z, Salesforce, AMP

Runway

$300M+

~$3B (est)

Google, Nvidia, Salesforce

Pika Labs

$135M

~$700M

Lightspeed, Spark, a16z

Stability AI

$100M+

Recovering

Coatue, Lightspeed

Luma Labs

$70M+

~$300M

a16z, Amplify

Ideogram

$80M+

Undisclosed

a16z, Index

Midjourney

$0 (bootstrapped)

Profitable

None - founder funded

AS OF LATE AUGUST 2026

The video arena split in two, and the two halves now have different winners. Artificial Analysis runs separate with-audio and no-audio brackets for text-to-video. In the with-audio bracket Alibaba's Wan 3.0 leads at 1241 Elo, ahead of Google's Gemini Omni Flash at 1237, fal's post-trained MiniMax H3 Max at 1235, and MiniMax H3 at 1227, a spread of fourteen points across four models that is close enough to read as a statistical tie. In the no-audio bracket Gemini Omni Flash still leads at 1324 with MiniMax H3 second at 1301. Chinese-built models hold eight of the top ten with-audio places and the one unambiguous exception is Google. On the image side, Microsoft's MAI-Image-2.6-Preview took the top of the image-editing board at 1285 Elo and sits second on text-to-image at 1351 behind OpenAI's GPT Image 2 at 1370. Two things follow. Which model is best now depends on whether you asked for sound, and a number one separated by single-digit Elo is a coin flip rather than a verdict.

AS OF EARLY SEPTEMBER 2026

The video arena tightened into a tie. On the Artificial Analysis with-audio text-to-video board, Alibaba's Wan 3.0 and Google's Gemini Omni Flash are level at 1238 Elo, with fal's post-trained MiniMax H3 Max at 1235 and MiniMax H3 itself at 1228, four models inside ten points. Ask for a still frame to animate instead and the order changes: on the with-audio image-to-video board H3 Max leads at 1200, ahead of Dreamina Seedance 2.0 at 1191, MiniMax H3 at 1186, Gemini Omni Flash at 1180, and Wan 3.0 at 1175. On images, OpenAI's GPT Image 2 still leads text-to-image at 1178 with Microsoft's MAI-Image-2.6 second at 1149, while MAI-Image-2.6 has taken the editing board at 1122 with GPT Image 2 second at 1117. One caution about reading these notes against each other: Elo baselines shift as models enter and leave the pool, so the orderings and the size of the gaps carry meaning across snapshots while the absolute numbers do not.

AS OF MID-SEPTEMBER 2026

The image board turned over and the video board did not. On the Artificial Analysis text-to-image leaderboard, OpenAI's two new variants now hold first and second, with GPT Image 2.5 Flare at max quality on 1188 Elo and Sunburst at max on 1181, ahead of GPT Image 2 at 1171 and Microsoft's MAI-Image-2.6 at 1145. The Arena board shows the same inversion, with Sunburst at 1421 and Flare at 1399 above gpt-image-2 at 1381. Microsoft had held the number two image slot for four days. On text-to-video the order barely moved: Alibaba's Wan 3.0 leads at 1243, Gemini Omni Flash at 1238, fal's post-trained MiniMax H3 Max at 1231, MiniMax H3 at 1225, and Dreamina Seedance 2.0 at 720p on 1220. On image-to-video H3 Max still leads at 1206, then Seedance 2.0 at 1197, H3 at 1190, HiDream-O1-Video at 1186, and Gemini Omni Flash at 1181. Google's Veo 3.1, still the flagship on DeepMind's own model page, now sits eleventh on image-to-video at 1088.

The numbers, what the leaderboards actually say

Free (open)

Kuaishou internal

None - founder funded

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