Model profile
StepFun
New in 2026

StepFun: Step 3.5 Flash

StepFun: Step 3.5 Flash is a budget-priced text-first model from StepFun with partial runtime data, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsN/A latencyLarge contextBudget pricing
Intelligence
26.0

Benchmark blend

Coding
N/A

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.10

Budget tier

Decision snapshot
56

StepFun: Step 3.5 Flash currently reads as a budget text-first option with large context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Agent workflows
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
26.0
26

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.10
86

$0.30 output / 1M

Efficient

Editorial Profile

StepFun: Step 3.5 Flash in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score N/AMath score N/A

Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....

Identity

StepFun text-first profile

Positioning

Long-context research / Agent workflows with large context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

Explore Next

Similar profiles worth opening next

StepFun

StepFun: Step 3.7 Flash

Intelligence
30.9
Context
262K Tokens
Input Price
$0.20
StepFun

Step 3.5 Flash 2603

Intelligence
26.5
Context
N/A
Input Price
$0.10
StepFun

Step3 VL 10B

Intelligence
9.3
Context
N/A
Input Price
$0.00

Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
26.0
GPQA
83.1%
HLE
21.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

SciCode
40.4%

Extra benchmark cuts are not available for this category yet.

Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
64.6%
TAU2
94.4%
TerminalBench Hard
27.3%
LCR
48.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
262K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
65536
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokensreasoningtemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.10
Output
per 1M output tokens
$0.30
Blended
AA 3:1 mix
$0.15

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.