Head-to-Head Model Comparison

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B

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.

Qwen3.5 397B-A17BProfile

Qwen3.5 122B-A10BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 397B-A17B

Model Dasar Bobot Terbuka

Qwen3.5 397B-A17B dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 64 token/detik, indeks kecerdasan 86/100, serta akses g

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

58%

Alibaba

Qwen3.5 122B-A10B

AI Multimodal Asli

Qwen3.5 122B-A10B dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 262K, kecepatan 78 token/detik, indeks kecerdasan 82/100, serta akse

Intelligence Index82 / 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.
Qwen3.5 122B-A10B is faster at 78 tok/s vs 64 tok/s.

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

Feature	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Intelligence Index Composite reasoning & knowledge score (0-100)	86	82
Provider	Alibaba	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	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	64 tok/s	78 tok/s
SWE-bench Verified Real-world software engineering	58%	N/A
GPQA Diamond Graduate-level reasoning	70%	65.5%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3.5 122B-A10B

+22% faster generation

Context

Tied

262.1K tokens window

Skor Benchmark Terverifikasi

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

Qwen3.5 397B-A17BQwen3.5 122B-A10B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

58%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs65.5%

MMLU-Pro (Multi-task Language Understanding)

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

83.5%vs80.5%

LMSYS Chatbot Arena Elo

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

1385ptsvs1350pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

64 tok/s vs78 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

Qwen3.5 397B-A17B0.65s

Qwen3.5 122B-A10B0.51s

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

Qwen3.5 397B-A17B8.46s

Qwen3.5 122B-A10B6.92s

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)

Qwen3.5 397B-A17B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.5 122B-A10B 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

Qwen3.5 397B-A17B · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 58% · GPQA Diamond 70%)
Kecepatan generasi tinggi 64 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
Bobot terbuka (397B total (17B 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

Qwen3.5 122B-A10B · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (GPQA Diamond 65.5% · MMLU-Pro 80.5%)
Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
Bobot terbuka (122B total (10B 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

Token Use & Task Economics

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

Metric	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	22K	18K
Reasoning Tokens / Task Thinking tokens burned per task	8K	5K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B: Jawaban Cepat (FAQ)

Which is more intelligent, Qwen3.5 397B-A17B or Qwen3.5 122B-A10B?

Qwen3.5 397B-A17B is more intelligent. It scores 86 compared with 82 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 122B-A10B is faster, generating 78 tokens per second compared with 64 tokens per second.

Which model has lower latency?

Qwen3.5 122B-A10B has lower latency, with a time to first token of 0.51s compared with 0.65s.

Kesimpulan

Baik Qwen3.5 397B-A17B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.5 122B-A10B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.5 397B-A17B unggul dalam penalaran dan pemrograman, Qwen3.5 122B-A10B memberikan kecepatan generasi token lebih tinggi, dan Qwen3.5 397B-A17B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3.5 397B-A17B atau Qwen3.5 122B-A10B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.5 397B-A17B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B?+

Qwen3.5 397B-A17B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3.5 122B-A10B sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3.5 397B-A17B lebih hemat biaya.

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

Qwen3.5 397B-A17B mendukung konteks 262.1K token dibandingkan 262.1K token pada Qwen3.5 122B-A10B, sementara Qwen3.5 122B-A10B 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)

Feature	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Official API Model ID (SDK)	qwen3-5-397b-a17b-instruct	qwen3-5-122b-a10b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2026-02
Knowledge Cutoff	December 2025	December 2025
Architecture	Next-Gen Multimodal Sparse Mixture-of-Experts	Efficient Open Multimodal MoE Transformer
Parameters	397B total (17B active MoE)	122B total (10B active MoE)
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	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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) · Vision (~258–765 tokens per image tile)	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) · 246 GB VRAM (4-bit Quantized) · 854 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 262 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, vision	text, vision
Output Modalities	text	text

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B

Ganti & Bandingkan Model Apa Saja

Qwen3.5 397B-A17B

Qwen3.5 122B-A10B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen3.5 397B-A17B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.5 122B-A10B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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