Qwen / Alibaba•Rank #13

Qwen3.8 Max

Proprietarygeneralreasoningcodingagentopen-source

Leaderboard Score

55.8

Scale: 0 - 100

Throughput Speed

39 tps

Context Window

API Pricing / 1M

$2.00

Parameter Scale

2.4T

Verified AI Radar & Benchmarks•8 Evaluated Metrics

Capability Radar & Benchmark Scores

Multi-Axis Capability Radar

Comparing Qwen3.8 Max against Frontier Top 10 & Open-Source average

0 - 100 Scale

Qwen3.8 Max

Frontier Top 10 Avg

Open-Source Avg

Performance Vectors

Where Qwen3.8 Max Leads

Coding & SWETop 10: 79.1% • OS: 47.6%

79.4%+0.3% vs Top 10

Reasoning & MathTop 10: 74.6% • OS: 46.6%

62.8%-11.8% vs Top 10

Agent & Tool AutomationTop 10: 71.2% • OS: 31.9%

56.2%-15.0% vs Top 10

Terminal & CLI ExecutionTop 10: 80.4% • OS: 47.6%

86.6%+6.2% vs Top 10

MMLU & Knowledge QATop 10: 50% • OS: 86.7%

69.8%+19.8% vs Top 10

Cyber & Security ExploitsTop 10: 87.8% • OS: 80.6%

53.0%-34.8% vs Top 10

All Verified Benchmark Evaluations

Direct Pass@1 and resolution rates across standardized evaluations.

OverallCodingReasoningAgenticTerminal

Featured Compute Platform

Run Benchmarks on Dedicated AI Clusters

Zero-cold-start inference endpoints with dedicated GPU instances.

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Executive Analysis & Verdict

Qwen3.8 Max by Alibaba is an open-source 2.4T MoE model scoring 55.8 overall with #1 rankings on PaperBench (93.0%), OSWorld-Verified (86.1%), and Visual Reasoning.

Key Strengths & Highlights

•Top rank on PaperBench (93.0%) and OSWorld-Verified (86.1%)
•Top rank on Visual Reasoning (BabyVision 91.3%, CharXiv 93.5%, ERQA 77.8%, LVBench 81.8%)
•Exceptional coding and frontend performance on QwenReactBench (1724 Elo) and FrontierSWE (73.5%)

Limitations & Considerations

•Requires high VRAM infrastructure for local 2.4T MoE deployment

Recommended Workflows

Scientific research reproduction, autonomous OS computer use, visual reasoning, and enterprise frontend synthesis

Architectural Overview & Capability Profile

Qwen3.8 Max is developed by Qwen as an advanced foundation model engineered for production enterprise applications, reasoning workflows, and autonomous problem-solving. On the comprehensive RankLLMs composite evaluation index, Qwen3.8 Max registers an overall benchmark rating of 55.8 / 100.

Operating under a Proprietary license structure, Qwen3.8 Max is served via high-throughput managed API endpoints designed for low-latency production queries.

Benchmark Performance & Real-World Utility

In standardized laboratory and real-world evaluation suites, Qwen3.8 Max demonstrates calibrated proficiency across key performance vectors:

SWE-bench Verified Software Engineering (79.4%): Measures multi-file GitHub issue resolution. The model demonstrates practical capability in localizing source code bugs, synthesizing functional patches, and passing rigorous integration test matrices. For evaluation methodology, consult our SWE-bench Pro guide.
GPQA Diamond Scientific Reasoning (62.8%): Assesses graduate-level domain problem solving across physics, chemistry, and biology, minimizing hallucinations in multi-step deductive arguments. See our Humanity’s Last Exam benchmark analysis.
MATH-500 Competition Mathematics (58.5%): Evaluates formal mathematical problem solving and symbolic derivation, providing foundational logic for algorithmic computing.
Terminal-Bench Execution (86.6%): Reflects proficiency in interactive command-line environments, shell tool use, and automated DevOps scripting.
Code Arena Preference Rating (1724 Elo): Validates human developer satisfaction in blind side-by-side coding evaluations against contemporary alternatives.

Inference Latency & Cost Architecture

Token Context Window: 1.0M tokens, allowing large documents, multi-file codebases, and long conversation memories to be processed in a single context window.
Inference Speed: Reference throughput clocks at approximately 39 tokens/second, enabling responsive streaming in developer tools and interactive interfaces.
Commercial Pricing: API consumption is billed at $2.00 per 1M input tokens and $6.00 per 1M output tokens, offering predictable scaling for high-volume enterprise traffic.

Strengths, Limitations & Ideal Workflows

Primary Strengths

Top rank on PaperBench (93.0%) and OSWorld-Verified (86.1%)
Top rank on Visual Reasoning (BabyVision 91.3%, CharXiv 93.5%, ERQA 77.8%, LVBench 81.8%)
Exceptional coding and frontend performance on QwenReactBench (1724 Elo) and FrontierSWE (73.5%)

Known Considerations

Requires high VRAM infrastructure for local 2.4T MoE deployment

Recommended Use Cases

Scientific research reproduction, autonomous OS computer use, visual reasoning, and enterprise frontend synthesis.

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Frequently Asked Questions About Qwen3.8 Max

What is Qwen3.8 Max and who developed it?↓

Qwen3.8 Max is a proprietary large language model developed by Qwen / Alibaba. It ranks #13 on the RankLLMs composite leaderboard, ahead of 86% of the 85 models we track, with an overall score of 55.8 / 100.

Can Qwen3.8 Max handle very long documents and codebases?↓

Qwen3.8 Max supports a 1.0M-token input context window and generates about 39 tps tokens per second in our reference measurements and which is enough to hold entire repositories, long transcripts, or multi-document research sets in a single session..

Why does Qwen3.8 Max cost more than most alternatives?↓

Qwen3.8 Max sits in the premium tier at $2.00 per 1M tokens - cheaper than only 78% of priced models we track. That pricing buys frontier-tier benchmark results; the cost-per-solved-task math in our best-coding-LLM guide shows when it pays off.

What is Qwen3.8 Max best at?↓

Qwen3.8 Max is strongest in Top rank on PaperBench (93.0%) and OSWorld-Verified (86.1%); Top rank on Visual Reasoning (BabyVision 91.3%, CharXiv 93.5%, ERQA 77.8%, LVBench 81.8%); Exceptional coding and frontend performance on QwenReactBench (1724 Elo) and FrontierSWE (73.5%). Full pillar-by-pillar numbers are in the benchmark tables above.

What should you watch out for with Qwen3.8 Max?↓

Known trade-offs include: Requires high VRAM infrastructure for local 2.4T MoE deployment. Weigh these against the strengths above for your specific workload.