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Qwen · Model

Qwen3.8 Max (0902) pricing, context window and specs

Qwen3.8 Max (0902) costs $2 per million input tokens and $6 per million output tokens, and reads up to 1,000,000 tokens per request. By blended price it is number 12 of Qwen’s 13 current models here, counting from the cheapest.

Input / 1M
$2
Output / 1M
$6
Cached / 1M
$0.25
Context
1,000,000
Max output
131,072
Released
2026-09-03

Estimate your cost with Qwen3.8 Max (0902)Compare all Qwen modelsCount tokens for Qwen3.8 Max (0902)

Pricing

Qwen3.8 Max (0902) API pricing

Price per 1M tokensInputOutput
Standard$2$6
Cached input (read)$0.25–

Output costs 3.0× the input price, so long replies drive the bill more than long prompts. If your requests share a long fixed prefix, such as a system prompt or tool definitions, caching cuts that part to 13% of the normal input price.

Examples

What Qwen3.8 Max (0902) costs in practice

WorkloadTokens in / outRequests/dayPer requestPer month
Support chatbot50% cached1,500 / 4001,000$0.00409$124.33
RAG app20% cached6,000 / 500500$0.0129$196.19
Coding agent80% cached40,000 / 2,000200$0.036$219.00
Document summariser8,000 / 600300$0.0196$178.85

Change any number in the cost calculator.

Context

Context window

Qwen3.8 Max (0902) accepts up to 1,000,000 tokens per request, roughly 750,000 English words or 1,500 pages. That budget is shared between your prompt and the reply, and a single reply is capped at 131,072 tokens.

Filling the whole window with one prompt costs about $2.00 at list price, so for repeated long-document work, retrieval (sending only the relevant parts) or prompt caching is usually much cheaper.

Check whether your text fits Qwen3.8 Max (0902)’s window.

Features

Capabilities

Image input
Yes
Tool / function calling
Yes
Reasoning mode
Yes
Prompt caching
Yes
Structured output
Yes
Open weights
No
Accepts
text, image, video
Knowledge cutoff
Not published

As declared by the provider’s API. It shows a feature exists, not how well it works.

API

Qwen3.8 Max (0902) model ID

OpenRouter
qwen/qwen3.8-max-0902

Use the OpenRouter ID when calling it through OpenRouter; Qwen’s own API may name it differently. See the model ID reference for the providers we document.

Qwen

Where Qwen3.8 Max (0902) sits in Qwen’s line-up

Ranked by blended price (three parts input to one part output, $3 per 1M for Qwen3.8 Max (0902)), it is number 12 of Qwen’s 13 current models here, counting from the cheapest. One step down, Qwen3.8 2.4T A95B costs $3 blended, the same. Unlike Qwen3.8 Max (0902), it doesn’t accept images or video and has open weights. One step up, Qwen3.8 Max Prime costs $6 (2× as much). It has the same published limits and features as Qwen3.8 Max (0902). See every Qwen model and price

ModelInput / 1MOutput / 1MContextReleased
Qwen3.5-9B$0.10$0.15256,0002026-03-10
Qwen3.8 Flash$0.15$0.471,000,0002026-08-26
Qwen3.8 Omni Flash$0.15$0.471,000,0002026-09-21
Qwen3 Coder Next$0.12$0.80262,1442026-02-04
Qwen3.6 35B A3B$0.10$0.95262,1442026-04-27
Qwen3 VL 8B Instruct$0.25$0.75262,1442025-10-14
Qwen3.7 Plus$0.32$1.281,000,0002026-06-03
Qwen3.5-122B-A10B$0.26$2.08262,1442026-02-25
Qwen3.6 27B$0.32$3.20262,1442026-04-27
Qwen3.5 397B A17B$0.55$3.50262,1442026-02-16
Qwen3.8 2.4T A95B$2$61,048,5762026-08-12
Qwen3.8 Max (0902) (this page)$2$61,000,0002026-09-03
Qwen3.8 Max Prime$4$121,000,0002026-09-23

Qwen’s current models with a page here, cheapest first. Older and superseded models are listed on the provider page and in the comparison table.

History

Earlier versions of Qwen3.8 Max (0902)

The previous model in this line is Qwen3.7 Max, first listed on 2026-05-21. Compared with it, Qwen3.8 Max (0902)’s input price is 36% higher and its output price is 36% higher.

ModelInput / 1MOutput / 1MContextReleased
Qwen3.7 Max$1.475$4.4251,000,0002026-05-21

Earlier releases with the same name and size in our data, newest first. Dates are when our source first listed each model.

Save

Cheaper alternatives

Current models from major providers, one per provider, that keep image input, tool calling, a reasoning mode, a usable context window and output limit. Compared by blended price.

Compare

Similarly priced models

Current models from other major providers, closest blended price.

FAQ

Qwen3.8 Max (0902) questions

How much does Qwen3.8 Max (0902) cost?

Qwen3.8 Max (0902) costs $2 per million input tokens and $6 per million output tokens, with cached input at $0.25. A typical chatbot reply (1,500 tokens in, 400 out) costs about $0.0054. Use the LLM cost calculator for your own workload.

What is Qwen3.8 Max (0902)’s context window?

1,000,000 tokens, about 750,000 English words or 1,500 printed pages, shared between your prompt and the reply. A single reply can be up to 131,072 tokens.

Does Qwen3.8 Max (0902) support prompt caching and batch requests?

Yes, cached input is billed at $0.25 per million tokens (13% of the normal input price). No batch price is published for it.

What are cheaper alternatives to Qwen3.8 Max (0902)?

With the same essentials (image input and tool calling), the cheapest options are Qwen3.6 35B A3B (90% cheaper), GLM 4.6V (85% cheaper), Command A+ (80% cheaper). Cheaper doesn’t mean equivalent, so test them on your own prompts.

What is the API model ID for Qwen3.8 Max (0902)?

On OpenRouter it is qwen/qwen3.8-max-0902. Qwen’s own API may use a different name, so check its model list before you deploy. Pass the ID as the model value in each request.

What did Qwen3.8 Max (0902) replace?

The previous Qwen model in the same line is Qwen3.7 Max, first listed on 2026-05-21. Compared with it, Qwen3.8 Max (0902)’s input price is 36% higher and its output price is 36% higher. Prices are only part of the picture, so test the newer model on your own prompts before switching.

Is Qwen3.8 Max (0902) open source?

No, Qwen3.8 Max (0902) is only available through Qwen’s API and partners; its weights aren’t published.

Updated

Sources: OpenRouter models API and LiteLLM model prices and context windows, checked daily; open-weight prices are typical hosted prices. Confirm critical numbers on Qwen’s pricing page. See our methodology.