Qwen3.8 Max
alibaba/qwen3-8-max出品方： Alibaba (Qwen) · 系列: qwen · 发布 2026-08-03
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding100
- Tool calling40/40
- Structured output20/20
- Reasoning10/10
- Context window (100K → 1M)20/20
- Provider availability10/10
Agents100
- Tool calling35/35
- Structured output25/25
- Reasoning15/15
- Output token limit15/15
- Provider availability10/10
JSON / structured output87
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume7/20
Cost efficiency47
- Headline price (log-scaled)42/95
- Has prompt-cache pricing5/5
Long context90
- Context window (100K → 2M)80/90
- Has published price for full window10/10
Vision100
- Accepts image input50/50
- Context window (10K → 1M)30/30
- Has published price10/10
- Provider availability10/10
Production-readiness94
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $10.73 $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $21.45 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $5.78 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $18.15 $0.02 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $22.77 $0.02 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 deepinfra · Qwen/Qwen3.8-Max
最便宜的渠道： alibaba-token-plan · Unknown 输入 + Unknown 输出
在 13 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| OpenRouter openrouter | qwen/qwen3.8-max | $2.00 | $6.00 | 1M | 2026-08-03 |
| Vercel AI Gateway vercel | alibaba/qwen3.8-max | $2.00 | $6.00 | 1M | 2026-08-02 |
| Deep Infra deepinfra | Qwen/Qwen3.8-Max | $1.65 | $4.95 | 256K | 2026-08-03 |
| CrossModel crossmodel | qwen/qwen3.8-max | $1.88 | $5.63 | 1M | 2026-08-03 |
| OpenCode Go opencode-go | qwen3.8-max | $2.00 | $6.00 | 1M | 2026-08-03 |
| NanoGPT nano-gpt | qwen3.8-max | $2.00 | $6.00 | 991K | 2026-08-03 |
| LLM Gateway llmgateway | qwen3.8-max | $2.00 | $6.00 | 1M | 2026-07-19 |
| EmpirioLabs AI empiriolabs | qwen3-8-max | $2.00 | $6.00 | 1M | 2026-08-03 |
| Alibaba Token Plan alibaba-token-plan | qwen3.8-max | Unknown | Unknown | 1M | 2026-08-03 |
| Kilo Gateway kilo | qwen/qwen3.8-max | $2.00 | $6.00 | 1M | 2026-08-03 |
| Merge Gateway merge-gateway | qwen/qwen3.8-max | $2.00 | $6.00 | 1M | 2026-08-03 |
| Charm Hyper hyper | qwen3.8-max | $2.00 | $6.00 | 1M | 2026-07-19 |
| Alibaba Token Plan (China) alibaba-token-plan-cn | qwen3.8-max | Unknown | Unknown | 1M | 2026-08-03 |
各渠道数据存在不一致
- context_window varies: 1000000, 256000, 991000
- release_date varies (span 15d): 2026-07-19, 2026-08-02, 2026-08-03
- modalities varies across offerings
各服务商对此模型的报告值存在差异。上方「核心数据」使用代表性服务商的值；逐项请以下表为准。
Frequently asked questions
How much does Qwen3.8 Max cost?
Qwen3.8 Max costs $1.65 per 1M input tokens and $4.95 per 1M output tokens, sourced from deepinfra. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen3.8 Max?
Qwen3.8 Max has a context window of 1M tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen3.8 Max support tool calling?
Yes. Qwen3.8 Max supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Qwen3.8 Max support structured output / JSON mode?
Yes. Qwen3.8 Max supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Qwen3.8 Max accept image input?
Yes. Qwen3.8 Max accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Alibaba (Qwen)'s docs for the exact rule.
Is Qwen3.8 Max open-weight?
No. Qwen3.8 Max is a proprietary model — only Alibaba (Qwen) (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Qwen3.8 Max?
If Qwen3.8 Max doesn't fit, consider Qwen3.5 397B-A17B, Qwen3.7 Max, Qwen3.6 35B-A3B. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Alibaba (Qwen) models
- Qwen3.5 397B-A17B$0.60 in / $3.60 out
- Qwen3.7 Max$2.50 in / $7.50 out
- Qwen3.6 35B-A3B$0.25 in / $1.49 out
- Qwen3.6 27B$0.60 in / $3.60 out
- Qwen3 32B$0.70 in / $2.80 out
Capability lists this model is in
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.