Head-to-Head Model Comparison

Step 3.7 Flash vs Qwen3.6 27B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Step 3.7 FlashProfile

Qwen3.6 27BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

StepFun

Step 3.7 Flash

Inferenza Ultra-Rapida

Step 3.7 Flash di StepFun è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 256K, velocità di 160 token/s, indice di intelligenza di 76/100 e tariff

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen3.6 27B

IA Multimodale Nativa

Qwen3.6 27B di Alibaba è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 262K, velocità di 92 token/s, indice di intelligenza di 77/100 e t

Intelligence Index77 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Qwen3.6 27B has a larger context window (262K vs 256K tokens).
Step 3.7 Flash is faster at 160 tok/s vs 92 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Step 3.7 Flash	Qwen3.6 27B
Intelligence Index Composite reasoning & knowledge score (0-100)	76	77
Provider	StepFun	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	160 tok/s	92 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	N/A	58%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Step 3.7 Flash

+74% faster generation

Context

Qwen3.6 27B

262.1K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Step 3.7 FlashQwen3.6 27B

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs58%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

73%vs75.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1275ptsvs1295pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs92 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Step 3.7 Flash0.25s

Qwen3.6 27B0.39s

Est. End-to-End Response (500 tokens)TTFT + generation time

Step 3.7 Flash3.38s

Qwen3.6 27B5.82s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Step 3.7 Flash Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.6 27B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Step 3.7 Flash · Strengths & Key Advantages

Finestra di contesto da 256K con capacità massima di output fino a 8K token (MMLU-Pro 73% · Arena Elo 1275)
Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 250 ms
Pesi aperti (Confidential / 70B MoE) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Qwen3.6 27B · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 8K token (GPQA Diamond 58% · MMLU-Pro 75.5%)
Throughput elevato di 92 token/secondo con latenza al primo token (TTFT) di soli 390 ms
Pesi aperti (27B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Step 3.7 Flash	Qwen3.6 27B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	12K	14K
Reasoning Tokens / Task Thinking tokens burned per task	2K	3K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Step 3.7 Flash	Qwen3.6 27B
Official API Model ID (SDK)	step-3-7-flash	qwen3-6-27b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-05	2026-04
Knowledge Cutoff	March 2026	February 2026
Architecture	High-Velocity Open Reasoning MoE	Refined Dense Multimodal Transformer
Parameters	Confidential / 70B MoE	27B
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text, vision
Output Modalities	text	text

Step 3.7 Flash vs Qwen3.6 27B: Risposte Rapide (FAQ)

Which is more intelligent, Step 3.7 Flash or Qwen3.6 27B?

Qwen3.6 27B is more intelligent. It scores 77 compared with 76 on the composite Intelligence Index.

Which model is faster?

Step 3.7 Flash is faster, generating 160 tokens per second compared with 92 tokens per second.

Which model has lower latency?

Step 3.7 Flash has lower latency, with a time to first token of 0.25s compared with 0.39s.

Which model has a larger context window?

Qwen3.6 27B has a larger context window, supporting 262.1K tokens compared with 256K tokens.

Verdetto

Sia Step 3.7 Flash (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) sia Qwen3.6 27B (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3.6 27B guida nei benchmark di ragionamento e programmazione, Step 3.7 Flash garantisce una generazione più rapida e Step 3.7 Flash offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Step 3.7 Flash o Qwen3.6 27B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3.6 27B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Step 3.7 Flash e Qwen3.6 27B?+

Step 3.7 Flash costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen3.6 27B costa $0.00 / $0.00. Nel costo combinato, Step 3.7 Flash risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Step 3.7 Flash supporta 256K token di contesto contro 262.1K token di Qwen3.6 27B, mentre Step 3.7 Flash offre una velocità di generazione superiore.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Step 3.7 Flash vs Qwen3.6 27B

Cambia e Confronta Qualsiasi Modello

Step 3.7 Flash

Qwen3.6 27B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Step 3.7 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.6 27B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Step 3.7 Flash vs Qwen3.6 27B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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