Step 5 Preview vs Muse Spark 1.3

Step 5 Preview and Muse Spark 1.3 side by side: specs, capabilities, parameters, and live pricing from the EmpirioLabs catalog.

StepFun Meta AI Text Generation

Step 5 Preview vs Muse Spark 1.3 at a glance

Step 5 Preview from StepFun and Muse Spark 1.3 from Meta AI are both in the Text Generation category, available through the EmpirioLabs API.

Key differences

  • Step 5 Preview is the newer release (Sep 15, 2026); Muse Spark 1.3 was released on Sep 2, 2026.
  • Muse Spark 1.3 has the larger context window: 1M tokens, against 1.02M for Step 5 Preview.
  • Input types only Muse Spark 1.3 accepts: Document.
  • Capabilities listed only for Step 5 Preview: vision, video, prompt cache, agentic coding.
  • Capabilities listed only for Muse Spark 1.3: multimodal, video understanding, web search, cache, document understanding.
  • Step 5 Preview lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens).

Step 5 Preview vs Muse Spark 1.3: specs

SpecStep 5 PreviewMuse Spark 1.3
Model IDstep-5-previewmuse-spark-1-3
AuthorStepFunMeta AI
ReleasedSep 15, 2026Sep 2, 2026
Context window1.02M tokens1M tokens
Max output131,072 tokens131,072 tokens
Input
Text Image Video
Text Image Video Document
Output
Text
Text
Structured outputJSON SchemaJSON Schema
RegionInternational-
Endpoints
POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/step-5-preview:generateContent
POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/muse-spark-1-3:generateContent
Capabilities
reasoning function calling structured output vision video prompt cache agentic coding
reasoning multimodal video understanding function calling structured output web search cache document understanding

Step 5 Preview vs Muse Spark 1.3: API pricing

Live rates from the EmpirioLabs catalog for both models. Each rate card always matches what the API charges.

Step 5 Preview vs Muse Spark 1.3: API parameters

Request parameters each model lists on EmpirioLabs, with the allowed values or range where one applies. A dash marks a parameter the model does not list.

ParameterStep 5 PreviewMuse Spark 1.3
temperature0 to 2 Default: 0.50 to 2 Default: 1
top_p0.01 to 1 Default: 0.90.01 to 1 Default: 1
max_tokens1 to 131072 Default: 409616 to 131072 Default: 16384
stoparray-
frequency_penalty-2 to 2 Default: 0-
reasoning_effortlow, medium, high Default: lowminimal, low, medium, high, xhigh Default: medium
reasoning_formatgeneral, deepseek-style Default: general-
toolsarrayarray Default: []
tool_choicestringobject
response_formatobjectenum
web_search_linkupboolean Default: false-
disable_formattingboolean Default: false-
tool_web_search-boolean Default: false
parallel_tool_calls-boolean

Step 5 Preview vs Muse Spark 1.3: limits and notes

Step 5 Preview

Supports text, image, and video input with a 1M token context, parallel function tools, strict JSON schema output, and reasoning_effort low, medium, or high. Prompt-cache hits are billed at the cache-read rate. Video input supports MP4 under 128 MB, with clips under 5 minutes recommended. This is a preview release, so its behavior and identifier may change.

Muse Spark 1.3

Reasoning is always on and cannot be disabled. reasoning_effort accepts minimal through xhigh. On /v1/chat/completions the reasoning text is not returned; on /v1/responses you can request reasoning summaries with reasoning.summary, and reasoning tokens are billed as output tokens and count against the output budget either way. Text, image, MP4 video, and PDF inputs are supported; up to 50 images can be attached. Audio input is not supported on this checkpoint; the Muse Spark 1.1 and 1.2 checkpoints accept audio. PDFs use text from the first 100 pages and page images from the first 50 pages. Built-in web search adds $0.00825 per executed search query and reports the count in usage.tool_usage. OpenAI Responses requests also support custom tools and deferred tool search. tool_choice supports auto and none. Prompts and completions sent to this Standard checkpoint are not used by Meta to train its models.

How to call Step 5 Preview and Muse Spark 1.3

Step 5 Preview serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id step-5-preview. Both models are called through the same endpoint. Set model to step-5-preview or muse-spark-1-3. Get an API key from the EmpirioLabs dashboard.

cURL
curl https://api.empiriolabs.ai/v1/chat/completions \
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "step-5-preview",
    "messages": [
      {"role": "user", "content": "Write a haiku about the ocean."}
    ]
  }'

Step 5 Preview vs Muse Spark 1.3: tests and guides

Prompt tests and comparison runs that cover both models.

Step 5 Preview vs Muse Spark 1.3: common questions

What is the difference between Step 5 Preview and Muse Spark 1.3?

Step 5 Preview is the newer release (Sep 15, 2026); Muse Spark 1.3 was released on Sep 2, 2026. Muse Spark 1.3 has the larger context window: 1M tokens, against 1.02M for Step 5 Preview. Input types only Muse Spark 1.3 accepts: Document. The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.

Which is newer, Step 5 Preview or Muse Spark 1.3?

Step 5 Preview is the newer release (Sep 15, 2026); Muse Spark 1.3 was released on Sep 2, 2026.

Which has the lower rate, Step 5 Preview or Muse Spark 1.3?

Step 5 Preview lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens). Both live rate cards are on this page and always match what the API charges.

Can I switch between Step 5 Preview and Muse Spark 1.3 without rebuilding my integration?

Yes. Both models use POST /v1/chat/completions with the same EmpirioLabs API key, so most integrations switch by changing the model value. Check the parameters table on this page first if you rely on a model-specific parameter.

Can I try Step 5 Preview and Muse Spark 1.3 in the browser before integrating?

Yes. Open each model in the EmpirioLabs playground and run the same prompt on both, with the parameters the API exposes.

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