Qwen3.8 2.4T A95B
Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows
- Context
- 1.0M
- Released
- 2mo ago
- Parameters
- 2.4T
- Input / 1M tokens
- $2.00
- Output / 1M tokens
- $6.00
- Cached input / 1M tokens
- $0.25
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Qwen
- Model family
- Qwen
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- August 12, 2026
- Model ID
qwen/qwen3.8-2.4t-a95b- Openness
- Open weights
- License
- qwen3.8-max
- Parameters
- 2.4T
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Tokenizer
- Qwen
- Max output tokens
- 131K
- Hugging Face
- Qwen/Qwen3.8-2.4T-A95B
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- alibaba/qwen3.8-2.4t-a95b
- Source family
- qwen
- Last updated
- 2026-08-12
- Model context
- 262K
- Model output limit
- 131K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Downloadable weights
Complete source record (JSON)
{
"id": "alibaba/qwen3.8-2.4t-a95b",
"name": "Qwen3.8 2.4T A95B",
"description": "Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows",
"family": "qwen",
"last_updated": "2026-08-12",
"attachment": false,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 262144,
"output": 131072
},
"release_date": "2026-08-12",
"open_weights": true,
"license": "qwen3.8-max",
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| alibaba | 1M | $2.00 | $6.00 | $0.25 | $2.50 | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| novita | 1M | $2.00 | $6.00 | $0.25 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| siliconflow/fp8 | 1.0M | $2.00 | $6.00 | $0.25 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| together | 1.0M | $2.00 | $6.00 | $0.25 | — | 97.13% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| venice | 262K | $2.00 | $6.00 | $0.25 | — | 95.38% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.