Qwen3.8 27B scores 52 on Artificial Analysis

Open weights model Released August 2026 Qwen3.8 27B Intelligence, Performance & Price Analysis Model summary Intelligence Speed Cost Verbosity Comparison Summary 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). Technical specifications This page shows the reasoning version of this model. A non-reasoning variant may also exist. Supports: text and image Supports: text 135 models in this class 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 Speed Cost per Task Intelligence Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index 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 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. Open Weights 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 Agentic real-world work tasks, (Elo-500)/2000 Agentic tool use Agentic coding & terminal use Coding Reasoning & knowledge Scientific reasoning Physics reasoning Knowledge 1 - hallucination rate 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 Intelligence Evaluation Relevance While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases. Artificial Analysis Intelligence Index 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 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 Intelligence Index Comparisons Intelligence Index vs. Cost per Intelligence Index Task Cost per Intelligence Index Task 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 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 Output Tokens per Intelligence Index Task 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 Cost per Intelligence Index Task 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 to Run Artificial Analysis Intelligence Index 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 Cache Hit 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 for RAG Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data. Context Window 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 Total Parameters 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. Active Parameters at Inference Time 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 When was Qwen3.8 27B released? Qwen3.8 27B was released on August 14, 2026. Who created Qwen3.8 27B? Qwen3.8 27B was created by Alibaba. How intelligent is Qwen3.8 27B? 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). How verbose is Qwen3.8 27B? 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). Is Qwen3.8 27B a reasoning model? 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. What input modalities does Qwen3.8 27B support? Qwen3.8 27B supports text and image input. What output modalities does Qwen3.8 27B support? Qwen3.8 27B supports text output. Can Qwen3.8 27B process images? Yes, Qwen3.8 27B supports image input and can analyze, describe, and answer questions about images. Is Qwen3.8 27B multimodal? Yes, Qwen3.8 27B is multimodal. It can process text and image input and generate text output. What is the context window of Qwen3.8 27B? 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. Is Qwen3.8 27B open source? Yes, Qwen3.8 27B is open weights. The model weights are publicly available and can be downloaded for self-hosting. How many parameters does Qwen3.8 27B have? Qwen3.8 27B has 27 billion parameters. What is the license for Qwen3.8 27B? Qwen3.8 27B is released under the Apache 2.0 license. This license allows commercial use. View license How does Qwen3.8 27B perform on benchmarks? 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. Is Qwen3.8 27B available via API? Qwen3.8 27B is an open weights model that can be self-hosted. View providers Where can I use Qwen3.8 27B? Qwen3.8 27B is an open weights model that can be downloaded and self-hosted. Compare providers