Head-to-Head Model Comparison

Qwen3.7 Max vs Qwen3.8 Max Preview

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.

Qwen3.7 MaxProfile

Qwen3.8 Max PreviewProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.7 Max

Model Agen Otonom & Alat

Qwen3.7 Max dari Alibaba adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 1M, kecepatan 80 token/detik, indeks kecerdasan 92/100, serta

Intelligence Index92 / 100

Input Token:

$1.80 / 1M

Output Token:

$7.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

73.5%

Alibaba

Qwen3.8 Max Preview

Model Dasar Bobot Terbuka

Qwen3.8 Max Preview dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 60 token/detik, indeks kecerdasan 93/100, serta akses g

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

Perbedaan Utama Sekilas

Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
Qwen3.7 Max is faster at 80 tok/s vs 60 tok/s.
Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 73.5%).

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

Feature	Qwen3.7 Max	Qwen3.8 Max Preview
Intelligence Index Composite reasoning & knowledge score (0-100)	92	93
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$1.80 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$7.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.21 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	32K	32K
Throughput Speed Tokens generated per second	80 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	73.5%	75.2%
GPQA Diamond Graduate-level reasoning	78.5%	79.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.18 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Qwen3.8 Max Preview

+1.7% SWE-bench lead

Cost

Qwen3.8 Max Preview

1.0x cheaper monthly runtime

Speed

Qwen3.7 Max

+33% faster generation

Context

Tied

1M tokens window

Skor Benchmark Terverifikasi

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

Qwen3.7 MaxQwen3.8 Max Preview

SWE-bench Verified (Autonomous Coding)

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

73.5%vs75.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

78.5%vs79.5%

MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs89%

LMSYS Chatbot Arena Elo

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

1470ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs77%

AIME (Advanced Math Competition)

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

86%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs60 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

Qwen3.7 Max0.58s

Qwen3.8 Max Preview0.68s

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

Qwen3.7 Max6.83s

Qwen3.8 Max Preview9.01s

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)

Qwen3.7 Max Monthly API Bill

$9.45 / month

$1.8/1M in · $7.2/1M out

Qwen3.8 Max Preview Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.8 Max Preview saves approximately $9.45 / month (100%) under this workload.1.0x Savings

Qwen3.7 Max · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 73.5% · GPQA Diamond 78.5%)
Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
Skema harga API kompetitif (tarif $1.8/$7.2 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Qwen3.8 Max Preview · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 680 ms
Bobot terbuka (2.4T total (95B 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	Qwen3.7 Max	Qwen3.8 Max Preview
Cost per Task Estimated USD to finish one task	$0.38	$0.00
Output Tokens / Task Tokens generated per task	32K	35K
Reasoning Tokens / Task Thinking tokens burned per task	14K	15K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3.7 Max	Qwen3.8 Max Preview
Official API Model ID (SDK)	qwen3-7-max-instruct	qwen3-8-max-preview-instruct
API Protocol	Alibaba REST API (OpenAI-Compatible)	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-05	2026-07
Knowledge Cutoff	April 2026	April 2026
Architecture	Next-Generation Deep Frontier Reasoning Transformer	Colossus-Scale Open Sparse Mixture-of-Experts
Parameters	Confidential	2.4T total (95B active MoE)
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($1.8/1M in) across full 1000K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.18/1M tokens	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	API Prompt Engineering & RAG Distillation	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	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16)
Safety & Frontier Classification	Alibaba Production Safety & Alignment Verified	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text, vision
Output Modalities	text	text

Qwen3.7 Max vs Qwen3.8 Max Preview: Jawaban Cepat (FAQ)

Which is more intelligent, Qwen3.7 Max or Qwen3.8 Max Preview?

Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 92 on the composite Intelligence Index.

Which model is faster?

Qwen3.7 Max is faster, generating 80 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $1.21 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.7 Max has lower latency, with a time to first token of 0.58s compared with 0.68s.

Kesimpulan

Baik Qwen3.7 Max (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.8 Max Preview (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.8 Max Preview unggul dalam penalaran dan pemrograman, Qwen3.7 Max memberikan kecepatan generasi token lebih tinggi, dan Qwen3.8 Max Preview lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3.7 Max atau Qwen3.8 Max Preview?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.8 Max Preview memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3.7 Max vs Qwen3.8 Max Preview?+

Qwen3.7 Max mengenakan biaya $1.80 / $7.20 per 1M token input/output, sedangkan Qwen3.8 Max Preview sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3.8 Max Preview lebih hemat biaya.

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

Qwen3.7 Max mendukung konteks 1M token dibandingkan 1M token pada Qwen3.8 Max Preview, sementara Qwen3.7 Max 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)

Qwen3.7 Max vs Qwen3.8 Max Preview

Ganti & Bandingkan Model Apa Saja

Qwen3.7 Max

Qwen3.8 Max Preview

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen3.7 Max · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.8 Max Preview · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen3.7 Max vs Qwen3.8 Max Preview: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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