Code2Video-AA: how well do AI models design videos in code?
Artificial Analysis' implementation of HeyGen's Code2Video Bench, evaluating how well models create short product and brand videos from 114 design briefs using HyperFrames HTML.
Overview
Code2Video-AA is Artificial Analysis' independent implementation of HeyGen's Code2Video Bench, a set of design briefs for short product and brand videos. Each model designs the video in code.
Key findings
- Claude Opus 5.5 (max with fallback) leads: at 1536 Elo it is 93 points ahead of the next model, and it is first on all five judge axes
- Two models are tied for second: Claude Sonnet 5.5 (max with fallback) at 1443 and GPT-6 Astra (max) at 1427 are within noise of each other. GPT-6.1 Sol (max) is fourth at 1369
- Cost per task varies more than 300x: from $0.01 to $3.89. GPT-6.1 Sol (max), fourth, costs $0.31, about a tenth of the $3.08 for Claude Opus 5.5 (max with fallback)
- Open weights models trail by at least 483 points: DeepSeek V4.1 Flash (max) at 1053, MiMo-V2.6-Flash at 1052 and MiMo-V2.6-Pro at 1052 are the top open weights models
Code2Video-AA Elo
Example tasks
Three tasks from the public split of Code2Video Bench, each with every model's rendered video. Models are in order of Code2Video-AA Elo.
Model
Videos from 27 of 27 models
The briefs are from the Code2Video Bench public split by HeyGen, licensed under CC BY 4.0.
How Code2Video-AA works
We send the model a design brief and HeyGen's system prompt in one call, with no tools. It returns one HyperFrames HTML composition, index.html, with HTML for layout and GSAP for animation.
The methodology page covers how we select the briefs, generate and render the videos, judge them, sample the comparisons and fit the Elo.
Claude Opus 5.5 leads on all five axes
The lead is largest on craft, 128 points, and smallest on prompt intent, 48 points, both over Claude Sonnet 5.5 (max with fallback). Temporal consistency separates models the least: its Elo range is 1057 points, against 1222 to 1349 points on the other four axes.
Each of the five judge axes has its own comparisons and its own Elo, and the overall Elo is their mean. The order of the models is almost the same on each axis: no model moves more than six places between an axis and the overall Elo.
Code2Video-AA Elo by Judge Axis
Cost per task varies more than 300x
Cost per task ranges from $0.01 for MiMo-V2.6-Flash to $3.89 for Claude Fable 5.1 (max with fallback). Eight models are on the Elo vs. Cost per Task Pareto frontier, from MiMo-V2.6-Flash at 1052 and $0.01 to Claude Opus 5.5 (max with fallback) at 1536 and $3.08. On that frontier, Claude Haiku 5.5 (max) scores 1192 at $0.08 and GPT-6.1 Sol (max) scores 1369 at $0.31. GPT-6 Astra (max) costs 5x as much as GPT-6.1 Sol (max), $1.62 against $0.31, for 58 more Elo points.
Output tokens per task vary 16x
Output tokens per task range from ~11k for Nemotron 3 Ultra to ~176k for Claude Sonnet 5.5 (max with fallback), and most of the variation comes from reasoning tokens. Final answers, which contain the compositions, range from ~4k to ~12k tokens across all models. The three Claude 5.5 models use the most output tokens (~152k to ~176k per task), against a mean of ~42k and a median of ~34k for the other 24 models.
Code2Video-AA: Elo vs. Cost per Task
Every model renders at least 96% of its videos
A composition must render to a video before the judge can see it. 3042 of the 3078 tasks (98.8%) rendered, and 8 of the 27 models rendered all 114. GLM-5.3 (max) has the lowest render rate, with 110 of 114.
A composition that fails to render loses every comparison against a video that rendered. The three lowest-ranked models rendered 111 to 113 of 114 tasks, so their low Elo comes mostly from the judge's verdicts on videos that did render.
Code2Video-AA: Render Rate
Code2Video-AA resources
- The leaderboard is on the Code2Video-AA evaluation page
- The methodology page covers the current implementation, judging and scoring
- HeyGen's announcement post describes the benchmark
- The HyperFrames renderer is on GitHub
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