# Qwen3 235B A22B

A high-performance open-weight model in Alibaba Cloud's Qwen3 line. A 235-billion-parameter mixture-of-experts (MoE) architecture where only 22 billion parameters activate per forward pass, achieving results comparable to several major closed-source models on coding, mathematics, and general reasoning benchmarks.

- **Model ID:** `alibaba/qwen-3-235b`
- **Type:** chat
- **Providers:** deepinfra, vertex
- **Context window:** 262,144
- **Maximum output tokens:** 16,384
- **Pricing:** $0.09/1M input tokens, $0.55/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/qwen-3-235b

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'alibaba/qwen-3-235b',
  prompt: 'Why is the sky blue?'
})
```

## About

Qwen3 235B A22B is the largest MoE in the Qwen3 family. Its MoE design routes each token through 8 of 128 expert layers, activating only 22 billion of the full 235 billion parameters. This sparsity keeps serving costs proportional to the activated parameter count while the full parameter space retains the breadth of knowledge needed for performance competitive with large proprietary models.

The model covers 119 languages and dialects.

On benchmark evaluations, Qwen3 235B A22B achieves results competitive with other strong reasoning models across coding, mathematics, and general capability assessments. Like all Qwen3 models, it supports a hybrid reasoning system: thinking mode activates extended computation for step-by-step problem solving, while non-thinking mode produces immediate responses when latency matters more than deliberation. You can configure a thinking budget per request to tune the cost-quality tradeoff dynamically.

The model supports tool calling and Model Context Protocol (MCP), making it suitable for multi-step workflows where the model orchestrates external tools or APIs. Alibaba Cloud recommends pairing it with the Qwen-Agent framework for complex agentic pipelines.

## What to consider

For applications with strict data sovereignty requirements, provider selection through AI Gateway lets you target infrastructure in specific regions without modifying your application's API calls.

## When to use

### Best For

- **Complex reasoning tasks:** When a problem genuinely requires the highest reasoning ceiling in the Qwen3 family, such as graduate-level mathematics, complex logical deduction chains, or multi-step proofs, this model is designed for it
- **Competitive coding challenges and complex software engineering:** The model's benchmark results in coding tasks place it alongside large proprietary models, making it appropriate for problems that smaller models consistently struggle with
- **Enterprise agentic pipelines:** With strong tool-calling capabilities and MCP support, Qwen3 235B A22B can serve as the planning and reasoning backbone for multi-step automated workflows where reliability matters
- **Multilingual content at scale:** Covering 119 languages, this model suits global-facing products that need high-quality output across many language families without running separate per-language models
- **Research and evaluation baselines:** As the largest-capability open-weight model in the Qwen3 Apache 2.0-licensed line, it's a useful reference point for capability assessments

### Consider Alternatives When

- **Latency is a binding constraint:** Larger models take longer to generate tokens. For interactive applications where response time outweighs peak quality, smaller Qwen3 variants or the 30B-A3B MoE model will serve users better
- **Cost per query needs to be minimized:** The 235B model carries higher inference costs than smaller family members. If most queries are routine instruction-following tasks, a smaller model will deliver adequate quality at lower cost
- **Lighter-weight alternatives suffice:** For teams that don't need the full capability ceiling of a 235B model, smaller dense variants like Qwen3-14B or Qwen3-32B handle many production workloads at lower per-token cost

## Best for

- **Complex reasoning tasks:** When a problem genuinely requires the highest reasoning ceiling in the Qwen3 family, such as graduate-level mathematics, complex logical deduction chains, or multi-step proofs, this model is designed for it
- **Competitive coding challenges and complex software engineering:** The model's benchmark results in coding tasks place it alongside large proprietary models, making it appropriate for problems that smaller models consistently struggle with
- **Enterprise agentic pipelines:** With strong tool-calling capabilities and MCP support, Qwen3 235B A22B can serve as the planning and reasoning backbone for multi-step automated workflows where reliability matters
- **Multilingual content at scale:** Covering 119 languages, this model suits global-facing products that need high-quality output across many language families without running separate per-language models
- **Research and evaluation baselines:** As the largest-capability open-weight model in the Qwen3 Apache 2.0-licensed line, it's a useful reference point for capability assessments

## Consider alternatives

- **Latency is a binding constraint:** Larger models take longer to generate tokens. For interactive applications where response time outweighs peak quality, smaller Qwen3 variants or the 30B-A3B MoE model will serve users better
- **Cost per query needs to be minimized:** The 235B model carries higher inference costs than smaller family members. If most queries are routine instruction-following tasks, a smaller model will deliver adequate quality at lower cost
- **Lighter-weight alternatives suffice:** For teams that don't need the full capability ceiling of a 235B model, smaller dense variants like Qwen3-14B or Qwen3-32B handle many production workloads at lower per-token cost

## Frequently asked questions

### Why does a 235B model only activate 22B parameters per inference?

The mixture-of-experts (MoE) architecture routes each token through eight of 128 specialized expert layers rather than the full parameter set. Inference compute scales with the activated parameters (22B), not the total (235B). This keeps a 235B-capacity model economically viable to serve while retaining the broad knowledge encoded in its full parameter space.

### How does Qwen3 235B A22B perform against other large language models on benchmarks?

Alibaba Cloud's benchmarks show competitive results against other strong reasoning models on coding, mathematics, and general capability evaluations. Specific numbers vary by benchmark.

### What distinguishes the 235B MoE model from Qwen3-32B in the same family?

Qwen3-32B is a dense model where all 32 billion parameters activate every inference, while Qwen3 235B A22B is a sparse MoE with 10 times the total capacity but slightly fewer active parameters than the 32B. The 235B variant reaches higher benchmark ceilings on the hardest tasks while the 32B offers a simpler deployment profile.

### Can I control how much reasoning compute the model uses per request?

Yes. Thinking mode and non-thinking mode are both available, and you can configure a thinking budget per request. Smaller budgets reduce latency and cost; larger budgets allow the model to work through more complex reasoning steps before responding.

### What languages does this model support?

Qwen3 235B A22B supports 119 languages and dialects across Indo-European, Sino-Tibetan, Afro-Asiatic, Austronesian, Dravidian, Turkic, and other language families.

### Is Zero Data Retention available for this model?

Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

## Links

- [Model page](https://vercel.com/ai-gateway/models/qwen-3-235b)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://deepinfra.com/Qwen/Qwen3-235B-A22B-Instruct-2507)
- [Provider pricing](https://deepinfra.com/pricing)