AIHOT 于 2026-08-17 收录了“Qwen3.8 27B 在人工分析中获得52分”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
PUBLIC SOURCE CONTENT
已抓取公开正文公开原文内容
Qwen3.8 27B - Intelligence, Performance & Price Analysis
Artificial AnalysisK
Alibaba
•Open weights model
•Released August 2026
Qwen3.8 27B Intelligence, Performance & Price Analysis
CompareAPI Provider Benchmarks
Model summary
Intelligence
#1 / 135
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
#23 / 135
160M
Output tokens from Intelligence Index
4 out of 4 units for 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
ReasoningYesThis page shows the reasoning version of this model.
A non-reasoning variant may also exist.
Input modality
Supports: text and image
Output modality
Supports: text
Context window256k~384 A4 pages of size 12 Arial font
Total parameters27B
LicenseApache 2.0
Model weightsHugging Face
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
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
Prompt Options
Intelligence
Artificial Analysis Intelligence IndexUpdatedAgentic IndexUpdated
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
29 of 610 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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 / ProprietaryReasoning / Non-ReasoningText Only / Multimodal Inputs
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
29 of 610 models
NEW
Add model from specific provider
ProprietaryOpen Weights (Commercial Use Restricted)Open Weights
Reasoning models are indicated by a lightbulb icon
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
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
CodingTool UseLong ContextMultimodalInstruction FollowingFaithfulnessWritingUser InteractionBusinessFinanceLegalMedicalSee more
19 of 23 evaluations29 of 610 models
NEW
Add model from specific provider
GDPval-AA v2
Agentic real-world work tasks, (Elo-500)/2000
𝜏³-BankingUpdated
Agentic tool use
Terminal-Bench v2.1
Agentic coding & terminal use
SciCode
Coding
Humanity's Last ExamUpdated
Reasoning & knowledge
GPQA Diamond
Scientific reasoning
CritPt
Physics reasoning
AA-Omniscience AccuracyUpdated
Knowledge
AA-Omniscience Non-Hallucination RateUpdated
1 - hallucination rate
AA-LCRUpdated
Long context reasoning
AA-Briefcase
Agentic knowledge work, Elo
AutomationBench-AA
Agentic SaaS workflows
Harvey LAB-AA
Legal agentic work, criterion pass rate
EnterpriseOps-Gym-AA
Agentic business operations
AA-AnalystAgentNew
Quantitative analysis on spreadsheets & documents
IFBench
Instruction following
APEX-Agents-AA
Long-horizon agentic tasks
ITBench-AA
Kubernetes incident root-cause analysis
MMMU-Pro
Visual reasoning
Reasoning models are indicated by a lightbulb icon
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 IndexAA-Omniscience AccuracyAA-Omniscience Hallucination Rate
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.
29 of 481 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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
Openness IndexOpenness Index ComponentsOpenness vs. Intelligence
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
19 of 306 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
Intelligence Index Comparisons
Intelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time
Intelligence Index vs. Cost per Intelligence Index Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
29 of 610 models
NEW
Most attractive quadrant
Pareto line
GoogleAnthropicZ AIDeepSeekSpaceXAIKimiNVIDIAMetaOpenAIAlibabaMistral
Reasoning models are indicated by a lightbulb icon
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 TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. 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
29 of 610 models
NEW
AnswerReasoning
Reasoning models are indicated by a lightbulb icon
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 TaskIntelligence Index vs. Cost per TaskEvaluation Breakdown
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
29 of 610 models
NEW
AnswerReasoningCache WriteCache HitInput
Reasoning models are indicated by a lightbulb icon
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.
Intelligence Index Total CostIntelligence Index vs. Total Cost
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
29 of 610 models
NEW
Add model from specific provider
OutputReasoningCache WriteCache ReadNon-Cache Input
Reasoning models are indicated by a lightbulb icon
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).
Cache Hit, Input, and Output PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input Pricing
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens)
29 of 610 models
NEW
Add model from specific provider
Cache HitInputOutput
Reasoning models are indicated by a lightbulb icon
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.
4 more notes
Context Window
Context WindowIntelligence Index vs. Context Window
Context Window
Context window: tokens limit · Higher is better
29 of 610 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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)
Total & Active ParametersIntelligence Index vs. Active ParametersIntelligence Index vs. Total Parameters
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference
29 of 610 models
NEW
Add model from specific provider
Active ParametersPassive Parameters
Reasoning models are indicated by a lightbulb icon
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
Alibaba
•Open weights model
•Released August 2026
Qwen3.8 27B Intelligence, Performance & Price Analysis
CompareAPI Provider Benchmarks
Model summary
Intelligence
#1 / 135
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
#23 / 135
160M
Output tokens from Intelligence Index
4 out of 4 units for 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
ReasoningYesThis page shows the reasoning version of this model.
A non-reasoning variant may also exist.
Input modality
Supports: text and image
Output modality
Supports: text
Context window256k~384 A4 pages of size 12 Arial font
Total parameters27B
LicenseApache 2.0
Model weightsHugging Face
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
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
Prompt Options
Intelligence
Artificial Analysis Intelligence IndexUpdatedAgentic IndexUpdated
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
29 of 610 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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 / ProprietaryReasoning / Non-ReasoningText Only / Multimodal Inputs
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
29 of 610 models
NEW
Add model from specific provider
ProprietaryOpen Weights (Commercial Use Restricted)Open Weights
Reasoning models are indicated by a lightbulb icon
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
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
CodingTool UseLong ContextMultimodalInstruction FollowingFaithfulnessWritingUser InteractionBusinessFinanceLegalMedicalSee more
19 of 23 evaluations29 of 610 models
NEW
Add model from specific provider
GDPval-AA v2
Agentic real-world work tasks, (Elo-500)/2000
𝜏³-BankingUpdated
Agentic tool use
Terminal-Bench v2.1
Agentic coding & terminal use
SciCode
Coding
Humanity's Last ExamUpdated
Reasoning & knowledge
GPQA Diamond
Scientific reasoning
CritPt
Physics reasoning
AA-Omniscience AccuracyUpdated
Knowledge
AA-Omniscience Non-Hallucination RateUpdated
1 - hallucination rate
AA-LCRUpdated
Long context reasoning
AA-Briefcase
Agentic knowledge work, Elo
AutomationBench-AA
Agentic SaaS workflows
Harvey LAB-AA
Legal agentic work, criterion pass rate
EnterpriseOps-Gym-AA
Agentic business operations
AA-AnalystAgentNew
Quantitative analysis on spreadsheets & documents
IFBench
Instruction following
APEX-Agents-AA
Long-horizon agentic tasks
ITBench-AA
Kubernetes incident root-cause analysis
MMMU-Pro
Visual reasoning
Reasoning models are indicated by a lightbulb icon
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 IndexAA-Omniscience AccuracyAA-Omniscience Hallucination Rate
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.
29 of 481 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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
Openness IndexOpenness Index ComponentsOpenness vs. Intelligence
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
19 of 306 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
Intelligence Index Comparisons
Intelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time
Intelligence Index vs. Cost per Intelligence Index Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
29 of 610 models
NEW
Most attractive quadrant
Pareto line
GoogleAnthropicZ AIDeepSeekSpaceXAIKimiNVIDIAMetaOpenAIAlibabaMistral
Reasoning models are indicated by a lightbulb icon
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 TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. 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
29 of 610 models
NEW
AnswerReasoning
Reasoning models are indicated by a lightbulb icon
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 TaskIntelligence Index vs. Cost per TaskEvaluation Breakdown
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
29 of 610 models
NEW
AnswerReasoningCache WriteCache HitInput
Reasoning models are indicated by a lightbulb icon
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.
Intelligence Index Total CostIntelligence Index vs. Total Cost
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
29 of 610 models
NEW
Add model from specific provider
OutputReasoningCache WriteCache ReadNon-Cache Input
Reasoning models are indicated by a lightbulb icon
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).
Cache Hit, Input, and Output PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input Pricing
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens)
29 of 610 models
NEW
Add model from specific provider
Cache HitInputOutput
Reasoning models are indicated by a lightbulb icon
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.
4 more notes
Context Window
Context WindowIntelligence Index vs. Context Window
Context Window
Context window: tokens limit · Higher is better
29 of 610 models
NEW
Add model from specific provider
Reasoning models are indicated by a lightbulb icon
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)
Total & Active ParametersIntelligence Index vs. Active ParametersIntelligence Index vs. Total Parameters
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference
29 of 610 models
NEW
Add model from specific provider
Active ParametersPassive Parameters
Reasoning models are indicated by a lightbulb icon
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
AIHOT 摘要
Qwen3.8 27B 在 Artificial Analysis Intelligence Index 上获得 52 分，远超同类模型中位数（9 分）。该模型支持文本和图像输入、文本输出，上下文窗口为 256k tokens，总参数 27B，采用 Apache 2.0 许可证。
为什么值得关注
Qwen3.8 27B 被放入 4B 至 40B 开源模型横向比较后，52 分对同组中位数 9 的差距为模型选型提供了直接参照。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“多模态与端侧”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：多模态与端侧。
- AIHOT 标签：
- AIHOT 判断：Qwen3.8 27B 被放入 4B 至 40B 开源模型横向比较后，52 分对同组中位数 9 的差距为模型选型提供了直接参照。
- AIHOT 评分：58；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。