Qwen 3 235B A22B Instruct 2507
Alibaba's updated 235B-parameter MoE language model with 22B active params, optimized for reasoning, coding, and long-context instruction following.
Overview
What is Qwen 3 235B A22B Instruct 2507
Qwen 3 235B A22B Instruct 2507 is an updated open-weights mixture-of-experts language model from Alibaba's Qwen team, released in July 2025. It activates 22 billion parameters per forward pass from 235 billion total, optimized for reasoning, coding, math, and long-context instruction following in non-thinking mode.
Running it privately on Venice
On Venice, Qwen 3 235B A22B Instruct 2507 runs with zero retention of your prompts and supports tool use, web search, and structured JSON output. It is served as open weights in FP8 quantization, giving you permissionless access to a frontier MoE model without Big-Tech surveillance or training-data profiling.
Assessment
Strengths and limitations
- Extremely efficient MoE architecture: 235B parameters with only 22B activated per token, delivering frontier reasoning at a fraction of the inference cost of dense models.
- Strong reasoning, coding, and math performance: benchmarks place it competitively against proprietary models on math, code, and alignment evaluations.
- Native 256K context window (262,144 tokens) for deep document analysis and large-codebase comprehension.
- Open weights under Apache 2.0, enabling self-hosting, fine-tuning, and full model sovereignty.
- Supports tool use, web search, and structured JSON output for agentic workflows on Venice.
- Multilingual with expanded long-tail knowledge coverage across many languages.
- Non-thinking mode only: it does not generate extended reasoning chains, which may limit performance on tasks that benefit from explicit step-by-step deliberation.
- No vision or multimodal support; it is strictly a text-generation model.
- While open weights, it is not uncensored and carries standard safety alignment that may refuse certain requests.
- Venice serves the model in FP8 quantization, which trades a small amount of precision for efficiency compared to the full-precision variant.
- Reviews note it can struggle with basic classification and tabular data tasks relative to its strengths in reasoning and long-context work.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Alibaba Cloud / Qwen Team
- Released
- July 21, 2025
- Modality
- Text generation
- Architecture
- Mixture-of-Experts (MoE), 128 experts / 8 activated
- Parameters
- 235B total / 22B activated per forward pass
- Open weights
- Yes — Apache 2.0
- Context window
- 128K tokens
- Max output
- 16.384K tokens
- Capabilities
- Function calling, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Apr 2025
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-235b-a22b-instruct-2507",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.15 per 1M input tokens and $0.75 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Price (Venice) | Context window | Open weights | Best for |
|---|---|---|---|---|
| Qwen 3 235B A22B Instruct 2507 | $0.15 in · $0.75 out / 1M | 128K tokens | Yes | Reasoning, coding & long context |
| DeepSeek V3.2 | $0.33 in · $0.48 out / 1M | 160K tokens | Yes | Agentic coding & inference |
| Kimi K2.6 | $0.75 in · $3.50 out / 1M | 256K tokens | Yes | Long-context reasoning |
| Claude Opus 4.8 | $6 in · $30 out / 1M | 1M tokens | No | Maximum proprietary capability |
Updated MoE flagship with 22B active params — strong math, code, and alignment performance at a fraction of rival pricing.
Use cases
What it is good for
- 01High-volume coding assistance and automated code review where MoE efficiency keeps costs low.
- 02Deep document analysis, legal discovery, and research synthesis leveraging its 256K native context window.
- 03Agentic workflows that require tool calling, web search, and structured JSON output.
- 04Multilingual content generation and translation across long-tail languages.
- 05Mathematical and scientific reasoning tasks where benchmark scores show strong capability.
Prompting
Getting better results
Be explicit and direct in prompts — the model excels at instruction following when tasks are clearly defined.
Use the structured JSON output capability by supplying a strict schema in your prompt for reliable data extraction.
Feed entire documents or large codebases into the context window rather than chunking; it handles 256K tokens natively.
For coding tasks, include test cases or expected output formats to steer the model toward correct implementations.
Version history
Base non-thinking variant.
Current updated instruct version with improved reasoning and alignment.
FAQ
Frequently asked questions
It is an open-weights mixture-of-experts language model released by Alibaba's Qwen team in July 2025. It has 235 billion total parameters with 22 billion activated per token, optimized for reasoning, coding, math, and long-context instruction following in non-thinking mode.
On Venice, it costs $0.15 per 1 million input tokens and $0.75 per 1 million output tokens. You pay only for the tokens you use, with no subscription required.
Yes. The model weights are released under the Apache 2.0 license on Hugging Face, enabling self-hosting and fine-tuning. On Venice, it is served as open weights with FP8 quantization.
Yes. On Venice, the model supports function calling, web search, and structured JSON output, making it suitable for agentic workflows that interact with external tools and APIs.
Qwen 3 235B A22B Instruct 2507 is cheaper on input and excels at math and alignment, while DeepSeek V3.2 offers a slightly larger context window and lower output pricing. Both are open-weights MoE models, so the best choice depends on your specific workload and cost structure.
The model natively supports 262,144 tokens (256K). On Venice, it is available with a 128K token context window and up to 16,384 tokens of max output per generation.
Venice offers a free tier that lets you try the model without a credit card. Beyond the free tier, usage is billed per token at the stated rates.
No. While it runs privately on Venice with zero prompt retention, the model itself carries standard safety alignment and is not marketed as uncensored. It may refuse certain harmful or restricted requests.
No. This is a text-only model. It does not accept images, audio, or video.
Run Qwen 3 235B A22B Instruct 2507 privately
No prompt logging. No data used for training.