Head-to-Head Model Comparison

Qwen3.5 397B-A17B vs Llama 5

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

Llama 5Profile

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%

Llama 5

Model Dasar Bobot Terbuka

Llama 5 dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 5M, kecepatan 65 token/detik, indeks kecerdasan 94/100, serta akses gratis open-weig

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Llama 5 has a larger context window (5M vs 262K tokens).
Llama 5 is faster at 65 tok/s vs 64 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 58.0%).

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

Feature	Qwen3.5 397B-A17B	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	86	94
Provider	Alibaba	Meta
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	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	16K	128K
Throughput Speed Tokens generated per second	64 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	58%	74%
GPQA Diamond Graduate-level reasoning	70%	84%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Llama 5

+16.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 5

+2% faster generation

Context

Llama 5

5M tokens window

Skor Benchmark Terverifikasi

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

Qwen3.5 397B-A17BLlama 5

SWE-bench Verified (Autonomous Coding)

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

58%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs84%

MMLU-Pro (Multi-task Language Understanding)

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

83.5%vs87%

LMSYS Chatbot Arena Elo

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

1385ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs88%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

64 tok/s vs65 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

Llama 50.85s

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

Qwen3.5 397B-A17B8.46s

Llama 58.54s

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

Llama 5 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

Llama 5 · Strengths & Key Advantages

Jendela konteks 5M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 850 ms
Bobot terbuka (600B+ total (~40-80B )) 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	Llama 5
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	22K	40K
Reasoning Tokens / Task Thinking tokens burned per task	8K	20K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

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

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

Llama 5 is more intelligent. It scores 94 compared with 86 on the composite Intelligence Index.

Which model is faster?

Llama 5 is faster, generating 65 tokens per second compared with 64 tokens per second.

Which model has lower latency?

Qwen3.5 397B-A17B has lower latency, with a time to first token of 0.65s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 262.1K tokens.

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 Llama 5 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 5 unggul dalam penalaran dan pemrograman, Llama 5 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 Llama 5?+

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

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

Qwen3.5 397B-A17B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Llama 5 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 5M token pada Llama 5, sementara Llama 5 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	Llama 5
Official API Model ID (SDK)	qwen3-5-397b-a17b-instruct	meta-llama/Llama-5
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2026-04
Knowledge Cutoff	December 2025	January 2026
Architecture	Next-Gen Multimodal Sparse Mixture-of-Experts	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	397B total (17B active MoE)	600B+ total (~40-80B active)
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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) · 372 GB VRAM (4-bit Quantized) · 1290 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, video, audio
Output Modalities	text	text

Qwen3.5 397B-A17B vs Llama 5

Ganti & Bandingkan Model Apa Saja

Qwen3.5 397B-A17B

Llama 5

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

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

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

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

Kesimpulan

Pertanyaan Umum (FAQ)

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