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Step 5 Preview

StepFun · released Oct 8, 2026

Open weightsReasoningTool useStructured outputVisionVideo in

Step 5 Preview is StepFun's flagship model for agentic work, built on a sparse Mixture-of-Experts architecture (27B active / 600B total parameters). It performs strongly in software engineering and professional...

Specification

Context window
1.05M
Max output
64K
Knowledge cutoff
Not stated
Parameters
604.3B
Licence
other
Serving providers
2
Moderated
No
Uptime
100.0%

Intelligence

43.7

94th percentile

Coding

—

Coding Index

Agentic

—

Agentic Index

Output speed

49 t/s

Median across providers

Latency

2.57s

Time to first token

Cost per task

$0.27

Estimated

Benchmarks

Where the score comes from

The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.

Evaluation scores

Percentage correct · higher is better

  • AA-LCR (long context)
    88.3%
  • SciCode
    58.9%
  • Humanity's Last Exam
    46.5%

An evaluation missing from this list was not run for this model — it is not a zero.

View as table
EvaluationScore
AA-LCR (long context)88.3%
SciCode58.9%
Humanity's Last Exam46.5%

Against its peers

Intelligence Index · this model highlighted, nearest peers in grey

Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.

View as table
ModelIntelligence
Qwen3.8 Max (0902)45.4
GLM 5.344.8
Grok 4.644.3
Step 5 Preview43.7
Kimi K343.6
Claude Haiku 5.543.4
GPT-5.6 Terra42.1
GLM 5.3 Flash41.8

Percentile among all indexed models

Intelligence94th

Pricing

What it costs to run

List prices per million tokens, plus what one representative task works out to.

List price

Input / 1M tokens
$1
Output / 1M tokens
$2.7
Cached input / 1M
$0.05
Blended 3:1
$1.43

One task, estimated

$0.27

Input tokens
50,000
Output tokens
80,000
Profile
Reasoning

Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).

Serving providers

Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.