Qwen3.8 27B scores 52 on Artificial Analysis
摘要
Artificial Analysis 对阿里开源模型 Qwen3.8 27B 的评测页。该模型在 Artificial Analysis Intelligence Index 上得分 52，远高于同类开源模型的中位数 9；评测中生成 160M 输出 token，比中位数 43M 更冗长。定价为每百万输入 / 输出 token 均 $0.00，低于同类中位数。模型支持文本和图像输入、文本输出，上下文窗口 256k token，总参数 27B，采用 Apache 2.0 许可，权重公开可下载。页面还列出其参与的多项基准（如 GDPval-AA v2、Terminal-Bench v2.1、GPQA Diamond 等），并说明其属于推理模型，存在非推理变体。
荐读理由
Qwen3.8 27B 以 Apache 2.0 开源、可自托管，智能指数 52 远超同类中位 9，且支持图像输入与 256k 上下文，可直接评估是否替换你项目里的闭源模型
原文
•
Open weights model
•
Released August 2026
Qwen3.8 27B Intelligence, Performance & Price Analysis
Model summary
Intelligence
52
Artificial Analysis Intelligence Index
4 out of 4 units for Intelligence.
Speed
N/A
Output tokens per second
Unknown out of 4 units for Speed.
Cost
In $0.00Out $0.00
N/A
Cost per Intelligence Index task
Unknown out of 4 units for Cost.
Verbosity
160M
Output tokens from Intelligence Index
4 out of 4 units for Verbosity.
Qwen3.8 27B is amongst the leading models in intelligence and well priced when comparing to other open weight models of similar size. The model supports text and image input, outputs text, and has a 256k tokens context window.
Qwen3.8 27B scores 52 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 9). When evaluating the Intelligence Index, it generated 160M tokens, which is very verbose in comparison to the median of 43M.
Pricing for Qwen3.8 27B is $0.00 per 1M input tokens (competitively priced, median: $0.04) and $0.00 per 1M output tokens (competitively priced, median: $0.15).
Yes
This page shows the reasoning version of this model.
A non-reasoning variant may also exist.
Supports: text and image
Supports: text
256k
~384 A4 pages of size 12 Arial font
| Reasoning | |
|---|---|
| Input modality | |
| Output modality | |
| Context window | |
| Total parameters | 27B |
| License | Apache 2.0 |
| Model weights | Hugging Face |
Metrics are compared against models of the same class:
Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:
Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters
Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
$1 per 1M tokens
Highlights
Intelligence
Artificial Analysis Intelligence Index · Higher is better
Speed
Output tokens per second · Higher is better
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better
Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).
Benchmarks
Intelligence Evaluations
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
Agentic real-world work tasks, (Elo-500)/2000
𝜏³-BankingUpdated
Agentic tool use
Agentic coding & terminal use
Coding
Humanity's Last ExamUpdated
Reasoning & knowledge
Scientific reasoning
Physics reasoning
AA-Omniscience AccuracyUpdated
Knowledge
AA-Omniscience Non-Hallucination RateUpdated
1 - hallucination rate
AA-LCRUpdated
Long context reasoning
Agentic knowledge work, Elo
Agentic SaaS workflows
Legal agentic work, criterion pass rate
Agentic business operations
Quantitative analysis on spreadsheets & documents
Instruction following
Long-horizon agentic tasks
Kubernetes incident root-cause analysis
Visual reasoning
Reasoning models are indicated by a lightbulb icon
While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
AA-Omniscience
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
Reasoning models are indicated by a lightbulb icon
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
Openness Index
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon
Intelligence Index Comparisons
Intelligence Index vs. Cost per Intelligence Index Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon
Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Token Use
Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon
The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).
Cost
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon
Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon
The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens)
Reasoning models are indicated by a lightbulb icon
Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.
Context Window
Context Window
Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon
Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.
Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).
Model Size (Open Weights Models Only)
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon
The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.
The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.
Frequently Asked Questions
Common questions about Qwen3.8 27B
Qwen3.8 27B was released on August 14, 2026.
Qwen3.8 27B was created by Alibaba.
Qwen3.8 27B scores 52 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 9).
When evaluated on the Intelligence Index, Qwen3.8 27B generated 160M output tokens, which is at the higher end compared to other open weight models of similar size (median: 43M).
Yes, Qwen3.8 27B is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
Qwen3.8 27B supports text and image input.
Qwen3.8 27B supports text output.
Yes, Qwen3.8 27B supports image input and can analyze, describe, and answer questions about images.
Yes, Qwen3.8 27B is multimodal. It can process text and image input and generate text output.
Qwen3.8 27B has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.
Yes, Qwen3.8 27B is open weights. The model weights are publicly available and can be downloaded for self-hosting.
Qwen3.8 27B has 27 billion parameters.
Qwen3.8 27B is released under the Apache 2.0 license. This license allows commercial use. View license
Qwen3.8 27B achieves a score of 52 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Qwen3.8 27B is an open weights model that can be self-hosted. View providers
Qwen3.8 27B is an open weights model that can be downloaded and self-hosted. Compare providers
这条对你有帮助吗？