Alibaba Cloud
Qwen3 — 235B MoE open-weight frontier model with hybrid thinking and 1M context
Alibaba Cloud's Qwen model family is one of the most capable open-weight model lineups available. Qwen3-235B is a 235-billion-parameter MoE model that rivals GPT-5.6 Sol on reasoning benchmarks, with open weights available for self-hosting. The 2026 lineup adds hybrid thinking mode (toggling between fast and deep chain-of-thought), a 1M-token context window on Qwen-Long, and Qwen3-VL for vision-language tasks. The API is available via Alibaba's DashScope platform.
Qwen3-235B is a frontier-class open-weight MoE model with hybrid thinking mode — matching closed frontier models on reasoning benchmarks while being fully self-hostable.
- Qwen3-235B rivals GPT-5.6 Sol on reasoning benchmarks — open-weight
- Hybrid thinking mode: toggle between fast responses and deep chain-of-thought
- 1M-token context window on Qwen-Long
- Qwen3-VL for vision-language tasks including OCR and document understanding
- Open-weight models available for self-hosting on any GPU cloud
- Competitive pricing — check alibabacloud.com for current rates
- API primarily optimised for Asian markets — latency may be higher in US/EU
- Less third-party integration support than OpenAI
- Documentation quality varies by model version
Alibaba Cloud Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
qwen3-4b-fp8 Qwen3 | Chat | budget | $0.03/ $0.03 in / out per 1M EST Jul 2026 | — | 120ms | 400 t/s | 128K | 54 | ||
qwen-turbo Qwen | Chat | budget | $0.05/ $0.20 in / out per 1M EST Jul 2026 | — | 200ms | 400 t/s | 131K | 50 | ||
qwen3-coder-30b-a3b Qwen3 Coder | Chat | efficient | $0.07/ $0.27 in / out per 1M EST Jul 2026 | — | 280ms | 200 t/s | 160K | 74 | ||
qwen2.5-7b Qwen2.5 | Chat | budget | $0.07/ $0.07 in / out per 1M EST Jul 2026 | — | 150ms | 350 t/s | 32K | 58 | ||
qwen3-vl-8b Qwen3 VL | Chat | budget | $0.08/ $0.50 in / out per 1M EST Jul 2026 | — | 200ms | 250 t/s | 131K | 65 | ||
qwen3-235b-a22b-instruct-2507 Qwen3 | Chat | advanced | $0.09/ $0.58 in / out per 1M EST Jul 2026 | — | 600ms | 110 t/s | 131K | 84 | ||
qwen3-30b-a3b-fp8 Qwen3 | Chat | efficient | $0.09/ $0.45 in / out per 1M EST Jul 2026 | — | 260ms | 210 t/s | 41K | 72 | ||
qwen3-30b-a3b Qwen3 | Chat | efficient | $0.10/ $0.40 in / out per 1M EST Jul 2026 | — | 400ms | 200 t/s | 131K | 68 | ||
qwen3-32b-fp8 Qwen3 | Chat | efficient | $0.10/ $0.45 in / out per 1M EST Jul 2026 | — | 280ms | 200 t/s | 41K | 74 | ||
qwen3-next-80b-a3b-thinking Qwen3 Next | Chat | efficient | $0.15/ $1.50 in / out per 1M EST Jul 2026 | — | 2.0s | 70 t/s | 131K | 78 | ||
qwen3-next-80b-a3b-instruct Qwen3 Next | Chat | efficient | $0.15/ $1.50 in / out per 1M EST Jul 2026 | — | 350ms | 180 t/s | 131K | 76 | ||
qwen3-32b Qwen3 | Chat | efficient | $0.20/ $0.60 in / out per 1M EST Jul 2026 | — | 500ms | 160 t/s | 131K | 70 | ||
qwen3-vl-30b-a3b-thinking Qwen3 VL | Chat | efficient | $0.20/ $1.00 in / out per 1M EST Jul 2026 | — | 1.5s | 80 t/s | 131K | 74 | ||
qwen3-vl-30b-a3b-instruct Qwen3 VL | Chat | efficient | $0.20/ $0.70 in / out per 1M EST Jul 2026 | — | 400ms | 160 t/s | 131K | 72 | ||
qwen3-coder-next Qwen3 Coder | Chat | efficient | $0.20/ $1.50 in / out per 1M EST Jul 2026 | — | 400ms | 150 t/s | 262K | 80 | ||
qwen3-235b-a22b-fp8 Qwen3 | Chat | efficient | $0.20/ $0.80 in / out per 1M EST Jul 2026 | — | 500ms | 130 t/s | 41K | 82 | ||
qwen3.5-flash Qwen3.5 | Chat | efficient | $0.25/ $2.00 in / out per 1M EST Jul 2026 | — | 250ms | 220 t/s | 1.0M | 78 | ||
qwen3.5-35b-a3b Qwen3.5 | Chat | efficient | $0.25/ $2.00 in / out per 1M EST Jul 2026 | — | 300ms | 190 t/s | 262K | 78 | ||
qwen-mt-plus Qwen MT | Chat | efficient | $0.25/ $0.75 in / out per 1M EST Jul 2026 | — | 300ms | 180 t/s | 16K | 68 | ||
qwen3.5-27b Qwen3.5 | Chat | efficient | $0.30/ $2.40 in / out per 1M EST Jul 2026 | — | 280ms | 200 t/s | 262K | 76 | ||
qwen3-vl-235b-a22b-instruct Qwen3 VL | Chat | advanced | $0.30/ $1.50 in / out per 1M EST Jul 2026 | — | 700ms | 110 t/s | 131K | 82 | ||
qwen3-235b-a22b-thinking-2507 Qwen3 | Chat | advanced | $0.30/ $3.00 in / out per 1M EST Jul 2026 | — | 3.5s | 45 t/s | 131K | 86 | ||
qwen3-coder-480b-a35b Qwen3 Coder | Chat | advanced | $0.38/ $1.55 in / out per 1M EST Jul 2026 | — | 600ms | 100 t/s | 262K | 86 | ||
qwen-plus Qwen | Chat | efficient | $0.40/ $1.20 in / out per 1M EST Jul 2026 | $0.10 | 400ms | 200 t/s | 131K | 65 | ||
qwen2.5-72b-instruct Qwen2.5 | Chat | efficient | $0.40/ $1.20 in / out per 1M EST Jul 2026 | — | 600ms | 130 t/s | 131K | 66 | ||
qwen2.5-vl-72b-instruct Qwen2.5 | Chat | efficient | $0.40/ $1.20 in / out per 1M EST Jul 2026 | — | 700ms | 110 t/s | 131K | 68 | ||
qwen3.5-plus Qwen3.5 | Chat | advanced | $0.40/ $2.40 in / out per 1M EST Jul 2026 | — | 400ms | 160 t/s | 1.0M | 84 | ||
qwen3.5-122b-a10b Qwen3.5 | Chat | advanced | $0.40/ $3.20 in / out per 1M EST Jul 2026 | — | 450ms | 140 t/s | 262K | 84 | ||
qwen3-235b-a22b Qwen3 | Chat | frontier | $0.60/ $2.40 in / out per 1M EST Jul 2026 | $0.15 | 1.2s | 55 t/s | 131K | 83 | ||
qwen3.5-397b-a17b Qwen3.5 | Chat | frontier | $0.60/ $3.60 in / out per 1M EST Jul 2026 | — | 600ms | 100 t/s | 262K | 88 | ||
qwen3-vl-235b-a22b-thinking Qwen3 VL | Chat | advanced | $0.98/ $3.95 in / out per 1M EST Jul 2026 | — | 3.0s | 50 t/s | 131K | 84 | ||
qwen-max Qwen | Chat | advanced | $1.60/ $6.40 in / out per 1M EST Jul 2026 | $0.40 | 700ms | 100 t/s | 131K | 76 |
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Alibaba Cloud is a proprietary AI lab that develops and hosts its own large language models. Alibaba Cloud's Qwen model family is one of the most capable open-weight model lineups available. Qwen3-235B is a 235-billion-parameter MoE model that rivals GPT-5.6 Sol on reasoning benchmarks, with open weights available for self-hosting. The 2026 lineup adds hybrid thinking mode (toggling between fast and deep chain-of-thought), a 1M-token context window on Qwen-Long, and Qwen3-VL for vision-language tasks. The API is available via Alibaba's DashScope platform. Common use cases include Coding, Reasoning, Multilingual, Vision. Headquartered in Hangzhou, China, founded 2009 (AI division 2023). All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Alibaba Cloud vs other LLM providers
Alibaba Cloud competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Qwen3-235B is a frontier-class open-weight MoE model with hybrid thinking mode — matching closed frontier models on reasoning benchmarks while being fully self-hostable. Use the LLM provider comparison tool to see Alibaba Cloud token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Alibaba Cloud?
Alibaba Cloud stands out because: Qwen3-235B is a frontier-class open-weight MoE model with hybrid thinking mode — matching closed frontier models on reasoning benchmarks while being fully self-hostable.. The API currently offers 32 models, spanning 2 frontier, 8 advanced, 18 efficient, 4 budget tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Alibaba Cloud token pricing
Alibaba Cloud charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.03/1M tokens. Prices shown are sourced from Alibaba Cloud's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Alibaba Cloud context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Alibaba Cloud's largest context window is 1.0M tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Alibaba Cloud makes sense
Managed inference APIs like Alibaba Cloud eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Alibaba Cloud API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Alibaba Cloud token price data is available in the LLM price history charts.
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