Qwen 3 235B A22B Thinking 2507
Alibaba's flagship open-weight thinking MoE — 235B total, 22B active, reasoning-only mode with tool use and 262K native context.
Overview
What is Qwen 3 235B A22B Thinking 2507
Qwen 3 235B A22B Thinking 2507 is an open-weight, thinking-only Mixture-of-Experts language model from Alibaba's Qwen team. It activates 22 billion of its 235 billion parameters per forward pass, natively supports 262K context, and is optimized for deep reasoning, mathematics, coding, and tool use.
Running it privately on Venice
On Venice, Qwen 3 235B A22B Thinking 2507 runs privately with zero retention of your prompts or outputs. It ships with default reasoning enabled, plus native support for tool use, web search, and structured JSON output — all without the surveillance or usage profiling typical of Big-Tech inference APIs.
Assessment
Strengths and limitations
- State-of-the-art reasoning performance among open-source thinking models, with enforced step-by-step reasoning for logic, mathematics, science, and coding.
- Strong tool use, function calling, web search, and structured JSON output support on Venice, making it ideal for agentic workflows.
- Open weights under Apache 2.0 enable self-hosting, fine-tuning, and full model sovereignty.
- Efficient MoE architecture activates only 22B parameters per forward pass, delivering high capability without full dense-model inference cost.
- Default reasoning mode ensures deep deliberation on complex prompts rather than shallow, immediate responses.
- Thinking-only mode: the model cannot disable reasoning, so even simple queries incur longer thinking chains and higher output token costs.
- Venice caps context at 128K tokens and max output at 16.384K tokens, below the native 262K context and high-output design.
- FP8 quantization on Venice trades a small amount of precision for inference efficiency compared to full-precision runs.
- Not uncensored: safety alignment is present, and the model may refuse certain sensitive or restricted requests.
- Recommended primarily for highly complex reasoning tasks; for casual chat or quick answers, a smaller non-reasoning model is often more efficient.
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 25, 2025
- Architecture
- Mixture-of-Experts (MoE), 94 layers
- Parameters
- 235B total / 22B activated per forward pass
- Experts
- 128 total / 8 activated
- Attention
- Grouped-query attention (64 Q / 4 KV heads)
- Open weights
- Yes — Apache 2.0
- Context window
- 128K tokens
- Max output
- 16.384K tokens
- Capabilities
- Function calling, Reasoning, 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-thinking-2507",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.45 per 1M input tokens and $3.50 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 | Context window | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| Qwen 3 235B A22B Thinking 2507 | 128K tokens | Yes | $0.45 in · $3.50 out / 1M | Deep reasoning & tool use |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M | Efficient coding & inference |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M | Long-document analysis |
| Claude Sonnet 4.6 | 1M tokens | No | $3.60 in · $18 out / 1M | General closed-model performance |
The most capable open-source thinking variant in the Qwen3 series, with enforced reasoning, tool use, web search, and structured output.
Use cases
What it is good for
- 01Advanced mathematics, scientific reasoning, and competitive programming requiring step-by-step derivation.
- 02Long-document analysis and academic research leveraging its extensive context capacity.
- 03Agentic automation with tool use, function calling, and structured JSON output for pipelines and integrations.
- 04Multilingual reasoning and code generation across complex software engineering tasks.
- 05Deep research workflows that benefit from web search grounding and extended reasoning chains.
Prompting
Getting better results
Budget extra output tokens: the model always generates a reasoning chain, so token consumption is higher than non-thinking models.
Use explicit tool schemas and JSON output formats when building agents to take advantage of native structured-output support.
For long-context tasks, place the most important instructions near the beginning or end of the prompt to maximize attention.
Reserve this model for complex problems; for simple Q&A or creative writing, a general-purpose model will be faster and cheaper.
Version history
Base Qwen3 MoE model.
Thinking-only update with scaled reasoning depth.
FAQ
Frequently asked questions
It is an open-weight, thinking-only Mixture-of-Experts language model developed by Alibaba's Qwen team. It activates 22 billion of its 235 billion parameters per forward pass and is optimized for deep reasoning, mathematics, coding, and tool use.
Venice charges $0.45 per 1 million input tokens and $3.50 per 1 million output tokens. There is no subscription required; you pay per token with credits.
Yes. The model weights are released under the Apache 2.0 license, allowing self-hosting, fine-tuning, and audit. On Venice it runs as open weights with FP8 quantization.
Yes. On Venice it supports function calling, structured JSON output, and web search, making it suitable for agentic workflows and automated pipelines.
Qwen 3 Thinking is a dedicated reasoning model with enforced step-by-step thinking and 235B total parameters, while DeepSeek V3.2 is a general-purpose MoE model with lower per-token pricing. Qwen 3 Thinking excels at complex math and logic; DeepSeek V3.2 is more cost-efficient for everyday coding and chat.
Yes. Venice runs it under a private, zero-retention tier — your prompts and outputs are not stored, profiled, or used for model training.
This model generates lengthy reasoning chains by default, which increases output token counts. The higher output price reflects the compute cost of producing deep reasoning and long-form responses.
No. This variant supports only thinking mode. The chat template automatically enforces reasoning, so every response will include a step-by-step chain of thought.
Run Qwen 3 235B A22B Thinking 2507 privately
No prompt logging. No data used for training.