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Qwen3.8-Flash-Next Intelligence, Performance & Price Analysis
Model summary
Comparison Summary
Qwen3.8-Flash-Next is amongst the leading models in intelligence and well priced when comparing to other open weight models of similar size. It's also faster than average, however very verbose. The model supports text, image, and video input, outputs text, and has a 256k tokens context window.
Qwen3.8-Flash-Next scores 56 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 28). When evaluating the Intelligence Index, it generated 200M tokens, which is very verbose in comparison to the median of 110M.
Pricing for Qwen3.8-Flash-Next is $0.00 per 1M input tokens (competitively priced, median: $0.30) and $0.00 per 1M output tokens (competitively priced, median: $1.17).
At 74 tokens per second, Qwen3.8-Flash-Next is faster than average (65).
Technical specifications
| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |
|---|---|
| Input modality | Supports: text, image, and video |
| Output modality | Supports: text |
| Context window | 256k ~384 A4 pages of size 12 Arial font |
| Total parameters | 180B |
| Active parameters | 6B Number of parameters active per token during inference |
| License | Qwen Community License 1.0 |
| Model weights | Hugging Face |
110 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
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 commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.
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).
Speed
Measured by Output Speed (tokens per second)
Output Speed
Output Speed
Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).
Model Performance Representation
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
Time per Intelligence Index Task
Time per Intelligence Index Task
The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
Time to First Answer Token
Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.
End-to-End Response Time
Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
End-to-End Response Time
End-to-End Response Time
Seconds to receive a 500 token response. Key components:
- Input time: Time to receive the first response token
- Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
- Answer time: Time to generate 500 output tokens, based on output speed
Model Performance Representation
Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
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-Flash-Next
When was Qwen3.8-Flash-Next released?
Qwen3.8-Flash-Next was released on August 26, 2026.
Who created Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next was created by Alibaba.
How intelligent is Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next scores 56 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 28).
How fast is Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next generates output at 73.5 tokens per second (based on the median across providers serving the model), which is above average compared to other open weight models of similar size (median: 65.1 t/s).
What is the latency of Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has a time to first token (TTFT) of 2.72s (based on the median across providers serving the model), which is somewhat higher than average compared to other open weight models of similar size (median: 2.27s).
How verbose is Qwen3.8-Flash-Next?
When evaluated on the Intelligence Index, Qwen3.8-Flash-Next generated 200M output tokens, which is at the higher end compared to other open weight models of similar size (median: 110M).
Is Qwen3.8-Flash-Next a reasoning model?
Yes, Qwen3.8-Flash-Next 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-Flash-Next support?
Qwen3.8-Flash-Next supports text, image, and video input.
What output modalities does Qwen3.8-Flash-Next support?
Qwen3.8-Flash-Next supports text output.
Can Qwen3.8-Flash-Next process images?
Yes, Qwen3.8-Flash-Next supports image input and can analyze, describe, and answer questions about images.
Is Qwen3.8-Flash-Next multimodal?
Yes, Qwen3.8-Flash-Next is multimodal. It can process text, image, and video input and generate text output.
What is the context window of Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next 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-Flash-Next open source?
Yes, Qwen3.8-Flash-Next is open weights. The model weights are publicly available and can be downloaded for self-hosting.
How many parameters does Qwen3.8-Flash-Next have?
Qwen3.8-Flash-Next has 180 billion parameters (6 billion active).
What are the active parameters of Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is a Mixture of Experts (MoE) model with 180 billion total parameters, but only 6 billion active parameters are used during inference.
What is the license for Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is released under the Qwen Community License 1.0 license. Commercial use is allowed with restrictions.
How does Qwen3.8-Flash-Next perform on benchmarks?
Qwen3.8-Flash-Next achieves a score of 56 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Is Qwen3.8-Flash-Next available via API?
Qwen3.8-Flash-Next is an open weights model that can be self-hosted. View providers
Where can I use Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is an open weights model that can be downloaded and self-hosted. Compare providers