Head-to-Head Model Comparison

Qwen3.7 Max vs Qwen3.8 Max Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Qwen3.7 MaxProfile

Qwen3.8 Max PreviewProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.7 Max

Autonomes Agenten-Modell

Qwen3.7 Max von Alibaba ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 1M Kontextfenster, 80 Token/s Durchsatz, einem Intelligenz-Index von 92/100 und $1.8/$7.2 pro 1M Token.

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

Open-Weights-Basismodell

Qwen3.8 Max Preview von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 1M Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 93/100 und kostenlosem Open-Weight

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

Wichtigste Unterschiede auf einen Blick

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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

1M Kontextfenster und bis zu 32K maximale Output-Token (SWE-bench Verified 73.5% · GPQA Diamond 78.5%)
Hohe Generierungsgeschwindigkeit von 80 Token/Sekunde bei nur 580 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($1.8/$7.2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Qwen3.8 Max Preview · Strengths & Key Advantages

1M Kontextfenster und bis zu 32K maximale Output-Token (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 680 ms Erstlatenz (TTFT)
Offene Modellgewichte (2.4T total (95B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Qwen3.7 Max (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen3.8 Max Preview (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3.8 Max Preview führt bei komplexem Reasoning und Coding, Qwen3.7 Max liefert den höheren Token-Durchsatz und Qwen3.8 Max Preview ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen3.7 Max oder Qwen3.8 Max Preview?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3.8 Max Preview vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen3.7 Max und Qwen3.8 Max Preview?+

Qwen3.7 Max kostet $1.80 / $7.20 pro 1M Input-/Output-Tokens, Qwen3.8 Max Preview liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Qwen3.8 Max Preview günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Qwen3.7 Max bietet 1M Tokens Kontext gegenüber 1M Tokens bei Qwen3.8 Max Preview. Bei der Generierungsgeschwindigkeit liegt Qwen3.7 Max vorn.

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

Beliebige Modelle wechseln & vergleichen

Qwen3.7 Max

Qwen3.8 Max Preview

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Qwen3.7 Max · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.8 Max Preview · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Qwen3.7 Max vs Qwen3.8 Max Preview: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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