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StepFun/Step 3Efficient

step-3.7-flash

costspeedchat
step-3.7-flash Pricing per 1M tokens
Input$0.200
Output$1.15
View StepFun
Context Window
256K
tokens
step-3.7-flash Max Output
8K
tokens
step-3.7-flash Throughput
200
tokens/sec
step-3.7-flash Latency P50
300ms
time to first token
About step-3.7-flash

step-3.7-flash is a efficient-tier language model from StepFun. step-3.7-flash is a proprietary model available exclusively through StepFun's API. The Step 3 family is StepFun's model series for this capability tier.

step-3.7-flash is priced at $0.2/1M input tokens and $1.15/1M output tokens. Output tokens are 5.7× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, step-3.7-flash costs approximately $0.78 per 1,000 API calls. StepFun does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.2/1M input rate.

Its 256K-token context window is in the top tier for production models, enabling full-document analysis, long multi-turn conversations, and large codebase understanding without chunking. Median latency (P50 time-to-first-token) is approximately 300ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 200 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 8K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

step-3.7-flash is best suited to cost, speed, chat. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how step-3.7-flash compares to similar models across these dimensions.

step-3.7-flash sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, step-3.7-flash is only available via StepFun's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing step-3.7-flash to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (74/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.

step-3.7-flash Intelligence Score
74/100
74

Composite quality score based on reasoning, coding, and instruction-following benchmarks.

step-3.7-flash Pricing vs Efficient Tier
CheapestThis modelMost expensive
$0.030$0.200$1.50

Input price vs 217 efficient models tracked. Avg: $0.321/1M.

step-3.7-flash Capabilities
Vision / Images✗
Function Calling✓
Streaming✓
Prompt Caching✗
Reasoning / CoT✗
Open Weights✗
Computer Use✗
Web Search✗
step-3.7-flash Full Specifications
Model IDstep-3.7-flash
ProviderStepFun
Model FamilyStep 3
TierEfficient
Context Window256,000 tokens (256K)
Max Output Tokens8,192 tokens
Input Price$0.2 / 1M tokens
Output Price$1.15 / 1M tokens
Cached Input PriceNot available
Throughput200 tokens/sec
Latency (P50)300ms
Intelligence Score74/100
LiveBench Score—
Cost per Task (LiveBench)—
Data Confidence—
HLE Score—
GPQA Diamond—
SWE-bench Score—
OSWorld Score—
BrowseComp Score—
Terminal-Bench Score—
AutoBench Score—
Parameters—
Vision SupportNo
Function CallingYes
StreamingYes
Reasoning ModelNo
Open WeightsNo
Data SourceStatic (verified)
Last Updated2026-10-15

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step-3.7-flash Frequently Asked Questions