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.

Alibaba Cloud

Qwen3 235B A22B Thinking 2507

Qwen3 235B A22B Thinking 2507 is Alibaba Cloud's 235B MoE model configured for extended chain-of-thought reasoning, combining 235 billion total parameters with always-on deliberative reasoning for demanding inference tasks.

View API reference

Input and output price: Prices from: Input $0.23, Output $2.30, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

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

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Qwen3 235B A22B Thinking 2507 by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Qwen3 235B A22B Thinking 2507

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Novita AI

Legal:Terms•Privacy

131K

33K

1.0 s

93 tps

$0.98/M

$3.95/M

—

07/25/2025

DeepInfra

Legal:Terms•Privacy

262K

0.3 s

24 tps

$0.23/M

$2.30/M

Read$0.20/M

—

07/25/2025

Alibaba Cloud

Legal:Terms•Privacy

131K

33K

0.5 s

130 tps

$0.40/M

$4/M

—

07/25/2025

Getting started

Call Qwen3 235B A22B Thinking 2507 through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Qwen3 235B A22B Thinking 2507 request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `alibaba/qwen3-235b-a22b-thinking`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Qwen3 235B A22B Thinking 2507 supports up to `262,114` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 262K-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK alibaba provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['novita', 'deepinfra'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-235b-a22b-thinking',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Alibaba Cloud

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

alibaba/qwen3.8-max-0902

991K

4.0 s

50 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

09/01/2026

alibaba/qwen3.8-flash

991K

1.1 s

96 tps

$0.16/M

$0.47/M

Read$0.02/M

Write$0.20/M

—

08/26/2026

alibaba/qwen3.8-27b

0.6 s

133 tps

$0.10/M

$0.40/M

Read$0.01/M

Write$0.63/M

—

08/14/2026

alibaba/qwen3.8-max

2.5 s

160 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

08/02/2026

alibaba/qwen3.7-flash

991K

2.5 s

140 tps

$0.03/M+2 more

$0.13/M+2 more

Read$0.006/M

Write$0.04/M

—

07/28/2026

alibaba/qwen3.7-plus

2.0 s

58 tps

$0.40/M+1 more

$1.60/M+1 more

Read$0.08/M

Write$0.50/M

—

06/02/2026

Copy link to headingAbout Qwen3 235B A22B Thinking 2507

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.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: Provider selection may affect time-to-first-token for reasoning models, since longer thinking traces amplify any latency differences between providers.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Qwen3 235B A22B Thinking 2507

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 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

Copy link to headingConclusion

Qwen3 235B A22B Thinking 2507 is built for the class of tasks where getting the right answer justifies spending more tokens on reasoning. The MoE architecture makes it more economical to sustain long thinking traces than a dense model of comparable total scale, and the reasoning capability is built into the model rather than being a prompting trick. AI Gateway wraps the model with automated failover across Novita AI, DeepInfra, Alibaba Cloud and a unified API surface.

Copy link to headingFrequently 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.

Your use is subject to Alibaba Cloud's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

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Qwen3 235B A22B Thinking 2507

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Image input

Tool calling

Copy link to headingMore models by Alibaba Cloud

Copy link to headingAbout Qwen3 235B A22B Thinking 2507

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Qwen3 235B A22B Thinking 2507

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions