Alibaba's compact open-weight flagship — a dense 27B vision-language model with tunable reasoning, agentic coding strength, and a 256K context window.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-qwen3-8-27b",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
Qwen 3.8 27B is Alibaba's compact flagship from the Qwen3.8 generation — a dense 27-billion-parameter vision-language model released under Apache 2.0 in August 2026. It handles coding, agentic tasks, reasoning, and image understanding, with a flexible thinking mode and a 256K-token context window on Venice.
On Venice, Qwen 3.8 27B runs inside a trusted execution environment with end-to-end encryption and zero retention — your prompts are never stored, profiled, or used for training. Because the weights are open under Apache 2.0, you get sovereignty over the model itself: self-host it, or run it permissionless through Venice's API with no subscription gate. It is not positioned as an uncensored model, but the privacy pipeline is the strictest tier Venice offers.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-qwen3-8-27b",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-qwen3-8-27b",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="e2ee-qwen3-8-27b",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Set reasoning_effort low for chat and summarization, high for hard coding or multi-step agent runs.
Use preserve_thinking in multi-turn agent loops so the model's reasoning context carries forward instead of restarting.
Paste screenshots or UI mocks as image input when debugging — the vision encoder reads them natively.
Define your tools explicitly via function calling and let the model plan terminal tasks step by step.
Keep long system prompts byte-identical across calls to hit the $0.05/1M cached-input rate.
Cap single responses at 8K output tokens — split large refactors into sequential, scoped passes.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| Qwen 3.8 27B | Private agentic coding & vision | 262K tokens | Yes | $0.45 in · $3.20 out / 1M |
| Claude Sonnet 4.6 | Polished general-purpose work | 1M tokens | No | $3.60 in · $18 out / 1M |
| Gemini 3.8 Flash | Fast multimodal at scale | 1M tokens | No | $0.94 in · $4.69 out / 1M |
| Google Gemma 4 31B Instruct | Cheapest open-weights option | 256K tokens | Yes | $0.12 in · $0.36 out / 1M |
| Qwen 3.7 Plus | Long-context Qwen on a budget | 1M tokens | No | $0.50 in · $2 out / 1M |
Pick Qwen 3.8 27B when you want open-weight, vision-capable coding and agentic muscle inside a private, zero-retention pipeline — at a fraction of closed-model pricing.
Pricing
Billed per token on Venice: $0.47 per 1M input tokens and $3.53 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass e2ee-qwen3-8-27b as the model and send the request.
FAQ
Qwen 3.8 27B is Alibaba's compact flagship from the Qwen3.8 open-model generation: a dense 27-billion-parameter vision-language model released under Apache 2.0 in August 2026. It combines coding and agentic strength with native image understanding, tunable reasoning depth, and a 256K-token context window on Venice.
Venice bills Qwen 3.8 27B per token: $0.47 per 1M input tokens and $3.53 per 1M output tokens, with cached input at just $0.05 per 1M. There is no subscription required — you pay only for what you use.
Yes. Qwen 3.8 27B ships open weights under the Apache 2.0 license, so you can self-host it, fine-tune it, and build commercial products on it. On Venice you can also use it as a managed, private endpoint without touching any infrastructure.
Yes. Qwen 3.8 27B is a native vision-language model with a built-in vision encoder, so it accepts image input — useful for screenshot review, UI analysis, and diagram understanding. On Venice the exposed capability is image input; the model card also describes video understanding, but that is not exposed on Venice.
Yes. On Venice, Qwen 3.8 27B supports function calling for agentic workflows, web search for grounded answers, and code-optimized inference. Its model card specifically highlights stronger autonomous planning and better handling of environment feedback for multi-step tasks.
Qwen 3.8 27B is the pick for open-weight, privacy-first coding and agentic work at $3.53 per 1M output tokens. Claude Sonnet 4.6 offers a 1M-token context and closed-model polish, but costs about five times more per output token and cannot be self-hosted. Choose Qwen for sovereignty and cost; choose Sonnet for maximum-context general-purpose polish.
It runs on Venice's strictest privacy tier: inference is isolated in a trusted execution environment, traffic is end-to-end encrypted, and prompts are never stored, profiled, or used for training — zero retention. You can use it in the Venice app or via the API without building a personal usage history.
No — Venice does not classify Qwen 3.8 27B as an uncensored model. It is a standard aligned assistant model. If uncensored output is your priority, check Venice's dedicated uncensored model tier instead.
On Venice, Qwen 3.8 27B runs with a 256K-token context window and an 8K-token output cap. The model card describes RoPE scaling up to 1M tokens, but that extended length is a hosted-only feature and is not offered on Venice.
One key, free to start, no credit card.