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stepfun · Open weights

Step 3.5 Flash 2603

Added Apr 14, 2026Open weights

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Use in the app

Step 3.5 Flash 2603 is optimized for high-frequency agentic and coding workflows with improved token efficiency and faster reasoning. NOTE: This model runs via StepFun, which may log and train on your prompts.

  • Reasoning
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.30

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
3K tokens
Longer than 96% of 465 models

Side by side

Step 3.5 Flash 2603 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Step 3.5 Flash 2603stepfunThis modelStep 5 PreviewstepfunRemove
Providerstepfunstepfun
Discovery tierOpen weightsBalanced
AddedApr 14, 2026Sep 20, 2026
Limits
Context window262.14K1MBest
Max output65.54K65.54K
Average output3K tokens1.5K tokens
Pricing, USD per 1M tokens
Input$0.10Best$1.00
Output$0.30Best$2.70
Cache read—$0.05
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index17.043.7Best
Capabilities
ReasoningYesYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Video inputNoYes
Parallel toolsNoYes
Reasoning effort—low, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Step 3.5 Flash 2603
  • Step 5 Preview

Intelligence Index

  • Step 3.5 Flash 260317.0
  • Step 5 Preview43.7

Step 3.5 Flash 2603: Better than 45% of 326 scored models

3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 51.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 17.7%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 1,423 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 1,587 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 14.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Step 3.5 Flash 2603: 63.0%
    • Step 5 Preview: 88.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 18.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Step 3.5 Flash 2603: 24.5%
    • Step 5 Preview: 46.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • Step 3.5 Flash 2603: 82.6%
    • Step 5 Preview: —

  • IFBench

    Following precise output instructions

    • Step 3.5 Flash 2603: 66.5%
    • Step 5 Preview: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 33.3%

  • SciCode

    Scientific computing in Python

    • Step 3.5 Flash 2603: —
    • Step 5 Preview: 58.9%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Step 3.5 Flash 2603: 32.6%
    • Step 5 Preview: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Step 3.5 Flash 2603: 87.4%
    • Step 5 Preview: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Step 3.5 Flash 2603: 63.0%
    • Step 5 Preview: 88.3%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
0.0%
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "stepfun-ai/step-3.5-flash-2603",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="stepfun-ai/step-3.5-flash-2603",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

stepfun

Other stepfun models, starting with the same family.