# Qwen3 235B A22B Thinking 2507

The Qwen3-235B-A22B MoE in its reasoning-first configuration. A 235B-parameter MoE model where extended chain-of-thought processing is the default, built for tasks where deliberate, step-by-step derivation is required.

- **Model ID:** `alibaba/qwen3-235b-a22b-thinking`
- **Type:** chat
- **Providers:** novita, deepinfra, alibaba
- **Context window:** 262,114
- **Maximum output tokens:** 262,114
- **Pricing:** $0.23/1M input tokens, $2.30/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/qwen3-235b-a22b-thinking

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

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

const result = streamText({
  model: 'alibaba/qwen3-235b-a22b-thinking',
  prompt: 'Why is the sky blue?'
})
```

## About

Qwen3 235B A22B Thinking 2507 is the Qwen3-235B-A22B configured with thinking mode as the default. The base model can switch between extended reasoning and direct response per request. This variant targets applications that need deliberate, chain-of-thought processing on every query.

The underlying architecture is the same 235B MoE: 235 billion total parameters with 22 billion activated per inference step. That MoE structure makes thinking mode tractable at this scale. Because only 22 billion parameters activate per token, Qwen3 235B A22B Thinking 2507 sustains long reasoning traces without the serving costs of a fully dense 235B model generating the same sequence length.

Chain-of-thought behavior is a first-class capability rather than something coaxed out by prompting. Alibaba Cloud reports that response quality scales smoothly with the computational reasoning budget allocated, so thinking longer genuinely helps on hard problems.

For the hardest categories of tasks (competitive mathematics, multi-hop logical reasoning, complex code debugging, and structured scientific analysis), this thinking-configured variant makes fuller use of the 235B parameter capacity. Benchmark results for the underlying model are competitive with other strong reasoning models on reasoning-heavy evaluations.

## What to consider

Provider selection may affect time-to-first-token for reasoning models, since longer thinking traces amplify any latency differences between providers.

## When to use

### Best For

- **Mathematical problem solving requiring detailed derivation:** When answers need to show work, proofs, step-by-step calculations, theorem verification, the always-on thinking mode ensures the model reasons carefully before committing to an answer
- **Complex debugging and code analysis:** Tracing through multi-file codebases, identifying subtle bugs, or reasoning about race conditions and edge cases benefits from extended deliberation rather than pattern-matched output
- **Structured decision-support tasks:** Applications in legal analysis, medical information synthesis, or financial modeling that require the model to consider multiple factors and surface its reasoning process explicitly
- **Difficult multi-hop question answering:** Tasks where the final answer requires correctly executing a chain of dependent reasoning steps are where thinking models show the largest quality gains over non-thinking alternatives
- **Research assistance requiring transparent reasoning:** When users need to audit or follow the model's reasoning process, the thinking trace provides visibility into how conclusions were reached

### Consider Alternatives When

- **Response latency is critical:** Thinking mode generates substantial internal tokens before producing the final answer. For real-time conversational interfaces or latency-sensitive pipelines, the non-thinking variant or a smaller model will respond much faster
- **Most queries are simple and don't require deliberation:** Using a thinking model for routine tasks, formatting, translation, simple extraction, pays the latency and token cost of reasoning without meaningful quality benefit. The base Qwen3-235B-A22B model with thinking disabled is more appropriate for mixed workloads
- **Budget constraints are strict:** Thinking traces add tokens to every response. If your application is cost-constrained, evaluate whether the quality improvement on your specific task distribution justifies the additional token usage

## Best for

- **Mathematical problem solving requiring detailed derivation:** When answers need to show work, such as proofs, step-by-step calculations, or theorem verification, the always-on thinking mode ensures the model reasons carefully before committing to an answer
- **Complex debugging and code analysis:** Tracing through multi-file codebases, identifying subtle bugs, or reasoning about race conditions and edge cases benefits from extended deliberation rather than pattern-matched output
- **Structured decision-support tasks:** Applications in legal analysis, medical information synthesis, or financial modeling that require the model to consider multiple factors and surface its reasoning process explicitly
- **Difficult multi-hop question answering:** Tasks where the final answer requires correctly executing a chain of dependent reasoning steps are where thinking models show the largest quality gains over non-thinking alternatives
- **Research assistance requiring transparent reasoning:** When users need to audit or follow the model's reasoning process, the thinking trace provides visibility into how conclusions were reached

## Consider alternatives

- **Response latency is critical:** Thinking mode generates substantial internal tokens before producing the final answer. For real-time conversational interfaces or latency-sensitive pipelines, the non-thinking variant or a smaller model will respond much faster
- **Most queries are simple and don't require deliberation:** Using a thinking model for routine tasks, formatting, translation, simple extraction, pays the latency and token cost of reasoning without meaningful quality benefit. The base Qwen3-235B-A22B model with thinking disabled is more appropriate for mixed workloads
- **Budget constraints are strict:** Thinking traces add tokens to every response. If your application is cost-constrained, evaluate whether the quality improvement on your specific task distribution justifies the additional token usage

## Frequently asked questions

### How does this model differ from the standard Qwen3-235B-A22B listing?

This variant is specifically configured for thinking mode, extended chain-of-thought reasoning is the default behavior rather than something toggled per request. It's intended for workloads where deliberative reasoning is always desired, rather than mixed applications that need to switch modes.

### Does the thinking trace count toward the context window and output token limit?

Yes. The reasoning trace is generated within the model's context and contributes to token usage. Long thinking sequences on complex problems can be substantial, so setting appropriate thinking budgets prevents runaway token consumption. Output pricing applies to all generated tokens including the trace, depending on provider implementation.

### Why is the MoE architecture particularly useful for thinking mode?

Thinking mode generates long internal token sequences before producing the final answer. With a fully dense model, every one of those tokens would activate all parameters. The MoE design activates only 22B of 235B parameters per token, making the extended reasoning trace significantly cheaper to generate than it would be with a dense model of equivalent total capacity.

### What benchmarks has the underlying model been evaluated on?

The Qwen3-235B-A22B model was benchmarked against other strong reasoning models on coding, mathematics, and general reasoning tasks, with competitive results reported. See the Qwen3 blog at https://novita.ai/models/model-detail/qwen-qwen3-vl-235b-a22b-thinking for detailed benchmark tables.

### Can thinking mode be adjusted or turned off for specific requests?

The thinking budget can be configured per request. If you occasionally need a faster response, reducing the thinking budget will constrain the reasoning phase. Completely disabling thinking on this variant may not reflect its intended use case; the standard Qwen3-235B-A22B model is better suited for workloads that need to toggle thinking on and off.

### What languages does this model support for reasoning tasks?

The model covers 119 languages and dialects. Thinking-mode reasoning works across this multilingual coverage, though the highest benchmark performance data tends to come from English and Chinese evaluations.

### Can I configure Qwen3 235B A22B Thinking 2507 for both thinking and direct-response traffic from one integration?

This variant is configured with thinking mode as the default. For mixed workloads that need to toggle thinking on and off per request, the standard Qwen3-235B-A22B listing exposes both modes.

## Links

- [Model page](https://vercel.com/ai-gateway/models/qwen3-235b-a22b-thinking)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://novita.ai/models/model-detail/qwen-qwen3-vl-235b-a22b-thinking)
- [Provider pricing](https://deepinfra.com/pricing)


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