Models

grok-4-1-fast-non-reasoning vs Step 3.7 Flash

Compare grok-4-1-fast-non-reasoning from Grok and Step 3.7 Flash from StepFun on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.

Grokgrok-4-1-fast-non-reasoningStepFunStep 3.7 Flash
Grok logo
grok-4-1-fast-non-reasoning
Grok · text, image → text

Grok 4.1 is a new conversational model with significant improvements in real-world usability, delivering exceptional performance in creative, emotional, and collaborative interactions. It is more perceptive to nuanced user intent, more engaging to converse with, and more coherent in personality, while fully preserving its core intelligence and reliability. Built on large-scale reinforcement learning infrastructure, the model is optimized for style, personality, helpfulness, and alignment, and leverages frontier agentic reasoning models as reward evaluators to autonomously assess and iterate on responses at scale, significantly enhancing overall interaction quality.

Input$0.2 /M
Output$0.5 /M
Cache read$0.05 /M
StepFun logo
Step 3.7 Flash
StepFun · text, image, video → text

step-3.7-flash is stepfun's flagship inference model, designed for high-complexity tasks that require deep reasoning and fast execution. It excels at decomposing multi-step problems, performing tool calls, and maintaining consistency across massive datasets. It is the preferred choice for complex workloads such as long-context agents, advanced software engineering, and end-to-end research automation.

Input$0.22 /M
Output$1.32 /M
Cache read$0.044 /M

Pricing & Specifications

Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.

grok-4-1-fast-non-reasoning
Step 3.7 Flash
Input /M
$0.2
$0.22
Output /M
$0.5
$1.32
Cache read /M
$0.05
$0.044
Context length
1,000,000
256,000
Max output
0
0
Time to First Token
0.7 s
1.4 s
Throughput
72.1 tok/s
32.6 tok/s
Modalities
textimage
textimagevideo
Supported Parameters
toolsfunction callingstructured outputs
API Formats
Released
November 19, 2025
May 29, 2026

Promotional prices show the discounted rate; see each model page for promotion windows.

Activity Past 30 Days

Daily traffic served through AIHubMix — how demand for each model is trending.

grok-4-1-fast-non-reasoningstep-3.7-flash

Tokens / day

-

Requests / day

-

Performance Past 3 Days

Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.

grok-4-1-fast-non-reasoningstep-3.7-flash

Throughput (tok/s)

-

TTFT (s)

-

Uptime (%)

-

Cost calculator

Estimate your monthly bill for the same workload on each model.

grok-4-1-fast-non-reasoning
$19.50 /mo
Step 3.7 Flash
$33.00 /mo

Monthly = daily × 30. Discounted rates applied where a promotion is active.

FAQ

Which is cheaper: grok-4-1-fast-non-reasoning, Step 3.7 Flash?

grok-4-1-fast-non-reasoning: $0.5/M output tokens; Step 3.7 Flash: $1.32/M. Use the cost calculator above to estimate your own workload.

Which responds faster?

grok-4-1-fast-non-reasoning: 0.7s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.

How large is each context window?

grok-4-1-fast-non-reasoning accepts 1,000,000 and Step 3.7 Flash accepts 256,000 input tokens.

Which one generates tokens faster?

grok-4-1-fast-non-reasoning at 72.1 tok/s and Step 3.7 Flash at 32.6 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.

What inputs and capabilities does each model support?

grok-4-1-fast-non-reasoning accepts text and image input and supports tool calling, function calling and structured outputs; Step 3.7 Flash accepts text, image and video input.

Can I call grok-4-1-fast-non-reasoning and Step 3.7 Flash with the same API key?

Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.

Popular comparisons

Related model match-ups readers also look at.