We put Muse Spark 1.3, Seed 2.1 Turbo, Step 5 Preview and DeepSeek V4.1 Flash against each other across four one-shot coding tests, all run on EmpirioLabs. Same prompt each, one attempt, no edits or retries, and every result rendered live in a real browser.
Watch the four way comparison
Specs at a glance
| Muse Spark 1.3 | Seed 2.1 Turbo | Step 5 Preview | DeepSeek V4.1 Flash | |
|---|---|---|---|---|
| Maker | Meta | ByteDance | StepFun | DeepSeek |
| Ventana de contexto | 1,048,576 tokens | 256,000 tokens | 1,024,000 tokens | 1,000,000 tokens |
| Entrada | Text, images, video and documents | Text, images and video | Text, images and video | Text and images |
| Reasoning effort | Up to max | Up to max | Up to max | Up to max |
| Structured output | Strict JSON Schema | Strict JSON Schema | Strict JSON Schema | JSON mode |
| Input price | $1.25 per 1M tokens | $0.63 per 1M tokens | $1.00 per 1M tokens | $0.30 per 1M tokens |
| Output price | $4.25 per 1M tokens | $3.13 per 1M tokens | $2.70 per 1M tokens | $1.20 per 1M tokens |
How we ran it
Each model received the identical prompt for four tasks: build the bouncing DVD screensaver in a single HTML file; build a hamster running on a wheel in a single HTML file; build a bubble machine in a single HTML file; build fireflies over a pond at dusk in a single HTML file. Every task asked for a single self-contained HTML file with no external libraries that animates on its own. All four models ran at reasoning_effort: "max" with a 65,536 token output budget, one shot, no retries. The line counts and tokens-per-second readouts on each panel are measured from the real API calls, and each result is the file the model returned, rendered as-is.
What to look for
DeepSeek V4.1 Flash returned its files fastest (about 130 tokens per second), wrote the longest (about 760 lines on average) and used the most output tokens (about 36,000 per task including its reasoning); Step 5 Preview wrote the most compact, about 450 lines. Watch how each model brings its scene to life: the motion, the detail and how it keeps running on its own. We are not declaring a winner. Run the clip and judge the outputs for your own use case.
Run the same test on EmpirioLabs
curl https://api.empiriolabs.ai/v1/chat/completions \
-H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "muse-spark-1-3",
"reasoning_effort": "max",
"messages": [{"role": "user", "content": "Build the bouncing DVD screensaver in a single HTML file."}]
}'Swap model to seed-2-1-turbo, step-5-preview or deepseek-v4-1-flash to run the same request against the others, or try them interactively in the playground.
Frequently asked questions
Were the results edited or retried?
No. Each model got one attempt per task with the identical prompt, and the rendered result is exactly the file it returned.
Why max reasoning?
A fair head to head shows each model at its best. All four expose a reasoning_effort control on EmpirioLabs, so all four ran at the highest setting.
Which model should I use?
DeepSeek V4.1 Flash has the lowest output price of the four here. Run your own workload against each before deciding: the same request works for all four with only the model name changed.
