Head-to-Head Model Comparison

Step 3.7 Flash vs Qwen3.6 27B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Inferensi Super Cepat

Step 3.7 Flash dari StepFun adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 256K, kecepatan 160 token/detik, indeks kecerdasan 76/100, serta ak

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

AI Multimodal Asli

Qwen3.6 27B dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 262K, kecepatan 92 token/detik, indeks kecerdasan 77/100, serta akses grat

Intelligence Index77 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

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%

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 256K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 73% · Arena Elo 1275)
Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 250 ms
Bobot terbuka (Confidential / 70B MoE) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Qwen3.6 27B · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 58% · MMLU-Pro 75.5%)
Kecepatan generasi tinggi 92 token/detik dengan latensi token pertama (TTFT) hanya 390 ms
Bobot terbuka (27B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Step 3.7 Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.6 27B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.6 27B unggul dalam penalaran dan pemrograman, Step 3.7 Flash memberikan kecepatan generasi token lebih tinggi, dan Step 3.7 Flash lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Step 3.7 Flash atau Qwen3.6 27B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.6 27B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Step 3.7 Flash vs Qwen3.6 27B?+

Step 3.7 Flash mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3.6 27B sebesar $0.00 / $0.00. Secara keseluruhan, Step 3.7 Flash lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Step 3.7 Flash mendukung konteks 256K token dibandingkan 262.1K token pada Qwen3.6 27B, sementara Step 3.7 Flash unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Step 3.7 Flash

Qwen3.6 27B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Step 3.7 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.6 27B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Step 3.7 Flash vs Qwen3.6 27B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

Related reading