stepfun · Open weights
Step 3.5 Flash 2603
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.
| Step 3.5 Flash 2603stepfunThis model | Gemma 4 26B A4BGoogleRemove | |
|---|---|---|
| Provider | stepfun | |
| Discovery tier | Open weights | Open weights |
| Added | Apr 14, 2026 | Apr 2, 2026 |
| Limits | ||
| Context window | 262.14K | 262.14K |
| Max output | 65.54K | 131.07KBest |
| Average output | 3K tokens | 131 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10 | $0.09Best |
| Output | $0.30 | $0.30 |
| Cache read | — | $0.05 |
| Performance | ||
| Throughput | — | 82.0 tok/s |
| Time to first token | — | 1.4 s |
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 17.0Best | 16.7 |
| Coding Index | — | 39.3 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Video input | No | Yes |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Step 3.5 Flash 2603
- Gemma 4 26B A4B
Intelligence Index
- Step 3.5 Flash 260317.0
- Gemma 4 26B A4B16.7
Step 3.5 Flash 2603: Better than 45% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 39.3
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 1.9%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 0.2%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 315 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 565 Elo
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Step 3.5 Flash 2603: 63.0%
- Gemma 4 26B A4B: 65.7%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Step 3.5 Flash 2603: 24.5%Top
- Gemma 4 26B A4B: 19.3%
GPQA Diamond
Graduate-level science questions
- Step 3.5 Flash 2603: 82.6%Top
- Gemma 4 26B A4B: 79.2%
IFBench
Following precise output instructions
- Step 3.5 Flash 2603: 66.5%
- Gemma 4 26B A4B: 72.4%Top
Coding
SciCode
Scientific computing in Python
- Step 3.5 Flash 2603: —
- Gemma 4 26B A4B: 40.0%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Step 3.5 Flash 2603: 32.6%Top
- Gemma 4 26B A4B: 13.6%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Step 3.5 Flash 2603: 87.4%Top
- Gemma 4 26B A4B: 43.6%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Step 3.5 Flash 2603: 63.0%
- Gemma 4 26B A4B: 65.7%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Step 3.5 Flash 2603
- Gemma 4 26B A4B
| Leaderboard | Step 3.5 Flash 2603 | Gemma 4 26B A4B |
|---|---|---|
| Vision Style Control · Captioning | Not in top 20 | #5 · 1238.9 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
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.
- 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.
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
Related models
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