qwen3.8-max
Input price$2
Output price$6
AlibabaTextVision
qwen3.5
qwen3.5 is Alibaba's model at $0.10 input and $0.15 output per million tokens, with a 1000K context window. On a typical 3:1 input-to-output mix that blends to roughly $0.11 per million tokens, placing it in the budget band of the 79 models tracked here — cheaper than 90% of them.
Context window1000K
Input price$0.1 / 1M
Output price$0.15 / 1M
Output speed85 tok/s
Quality score91
Data updatedUpdated Aug 2026
Price verified2026-08-06 · OpenRouter
By price, qwen3.5 ranks 9th cheapest of 79 on input and 6th on output. Its quality score of 91 ranks 23rd of 79, and on value (quality score ÷ output price) it ranks 3rd of 79. Its blended rate sits 96% below the catalog average.
In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $14 per month on qwen3.5, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $18. Because output is priced 1.5× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.
A 1000K context window holds roughly 1,333 pages of A4 text (at ~750 tokens per page), ranking 26th of 79 here. That is enough to drop in an entire technical manual or several source files at once, which suits long-document summarization and codebase-wide analysis. Output runs at about 85 tok/s (53rd fastest of 79), acceptable interactively, though long responses will feel slow.
We track 8 Alibaba models, and qwen3.5's blended rate sits 81% below that provider's average. Among the models tracked here it is already one of the cheapest options at this quality level. Going the other way, deepseek-reasoner-v4 scores 92 on quality for about 383% more.
The closest model in Alibaba's line-up is qwen-turbo ($0.05 input / $0.20 output, quality 74). Against it, qwen3.5 scores 17 points higher on quality, runs 155 tok/s slower (about 65%), costs 22% more blended. When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.
Our read on it
Quality 91, a million tokens of context, $0.10/$0.15. When those numbers appear together, somebody is buying market share.
Check that you read it correctly
qwen3.5 charges $0.15 per 1M output tokens — only 50% above its own $0.10 input rate. Quality 91, 1,000K context, 85 tokens/second, text and vision, tagged for coding, reasoning and long-context.
The catalog holds three other models scoring 91: grok-4.5 at $2/$6, gemini-3.1-pro at $2/$12 and doubao-seed-2.1-pro at $0.83/$4.14. qwen3.5 undercuts their output rates by 28x to 80x. Multiples like that do not emerge from ordinary competition in a mature market. This is strategic pricing — rate traded for adoption and ecosystem position.
At this price, token cost is no longer your main risk
Durability is. The nature of strategic pricing is that it can end: promotions expire, quotas get revised, routing quality drifts. Worth knowing that this site sources qwen3.5's price from OpenRouter (verified 2026-08-06) rather than a vendor pricing page, which is a meaningful caveat when you are building a twelve-month budget on it.
The pragmatic stance is to treat it as seriously worth evaluating while keeping an exit. Validate the 91 against your own eval set rather than taking the number on faith, and make sure your abstraction layer can swap the underlying model inside a week. With that insurance in place, the price is pure upside.
Who it threatens most directly
Every budget default. gpt-4o-mini at $0.15/$0.60, quality 80 and 128K context loses every column: more expensive, lower scoring, an eighth of the context. claude-haiku-4-5 at $1/$5 and quality 82 is further behind still.
It also reaches upward. claude-sonnet-5 is $2/$10 at quality 92 — one point higher, for a blended rate forty-eight times larger. The case for Sonnet 5 becomes ecosystem maturity, tool-use reliability and vendor stability. Those are real arguments, but they now have to be made explicitly, because the assumption that a cheap model must be a worse one has expired.
Estimated monthly cost
Monthly API spend across four common workloads, next to the cheapest model we track.
| Workload | Usage assumption | This model | Cheapest model |
|---|---|---|---|
| Support chatbot | 2,000 input / 500 output tokens per turn, 50,000 turns per month | $14 | $3.00 |
| RAG document Q&A | 8,000 input / 800 output tokens per query, 20,000 queries per month | $18 | $3.84 |
| Coding assistant | 12,000 input / 2,000 output tokens per request, 10,000 requests per month | $15 | $3.20 |
| Batch summarization | 4,000 input / 400 output tokens per item, 200,000 items per month | $92 | $19 |
Derived directly from the rates on this page. Excludes prompt caching, batch discounts and free tiers.
How it compares
The alternatives worth benchmarking alongside it, from the same dataset.
| Model | Provider | Blended /1M | Quality | Price delta |
|---|---|---|---|---|
| qwen3.5 | Alibaba | $0.11 | 91 | — |
| deepseek-reasoner-v4Higher quality | DeepSeek | $0.54 | 92 | +383% |
| qwen-turboSame provider | Alibaba | $0.088 | 74 | -22% |
| qwen3.7-flashBest value overall | Alibaba | $0.055 | 82 | -51% |
Best for
CodingReasoningLong Context
Modalities
TextVision
Where it fits — and where it doesn't
Good fit
- Coding: writing, refactoring and debugging across multi-file project context.
- Reasoning: multi-step decomposition, planning and tasks that need rigorous inference.
- Long documents: a 1000K window ingests a full contract, paper or large codebase in one pass.
- High-volume, cost-sensitive batch work: low rates suit classification, tagging and summarization at scale.
- Multimodal input: handles images, screenshots and scanned documents directly.
Poor fit
- No obvious disqualifiers — still benchmark on your own data before committing.
Frequently asked questions
How much does the qwen3.5 API cost?
qwen3.5 costs $0.10 per million input tokens and $0.15 per million output tokens. On a 3:1 input-to-output mix that blends to about $0.11 per million tokens.
What does qwen3.5 cost per month?
It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $14/month, RAG document Q&A about $18/month, a coding assistant about $15/month, and batch summarization about $92/month. Plug your own token counts into the cost calculator for a tailored figure.
How large is the qwen3.5 context window?
1000K tokens — roughly 1,333 pages of A4 text at ~750 tokens per page, ranking 26th of the 79 models tracked here.
What is qwen3.5 best suited for?
Tagged use cases: coding, reasoning, long-context. Coding: writing, refactoring and debugging across multi-file project context. Less suitable: No obvious disqualifiers — still benchmark on your own data before committing.
How current is this qwen3.5 pricing?
Pricing here was last verified on 2026-08-06 against OpenRouter. llmprice.app runs a daily collection job, but providers can change rates between runs — confirm against Alibaba's official pricing page before you commit.
Estimate the real cost of this model
Bring tokens per request and monthly volume into the calculator to compare total model costs.
Related models
Continue by provider and use case.
View model
qwen3.5-plus
Input price$0.32
Output price$1.28
qwen3.8-flash
Input price$0.15
Output price$0.47
Pricing, speed and quality scores change over time. Confirm official documentation before production use.