What other models does Alibaba offer?

Qwen3 Max is one model in Alibaba's Qwen lineup, which spans from lightweight flash-tier models through large-scale open-weight reasoning models to proprietary multimodal flagships. Here are some other models Alibaba supports:

• Qwen3.7 Max: Qwen3.7 Max is the current proprietary flagship from Alibaba, released May 2026, scoring 57 on the Artificial Analysis Intelligence Index and ranking in the top 10 of over 150 evaluated models, with a 1M-token context window and output speed of 182.7 tokens per second at $2.50 per 1M input and $7.50 per 1M output

• Qwen3.6 35B A3B: Qwen3.6 35B A3B is an open-weight MoE reasoning model that scores 43 on the Intelligence Index and ranks second among its class, delivering near-flagship reasoning performance at $0.248 per 1M input and $1.485 per 1M output under an Apache 2.0 license

• Qwen3.5 397B A17B: Qwen3.5 397B A17B is the largest open-weight model in the Qwen3.5 generation, with 397 billion total parameters, a 262k-token context window, and support for text and image inputs, suited for teams that want self-hosted large-scale reasoning

• Qwen3 Coder Next: Qwen3 Coder Next is Alibaba's open-weight coding-specialized model at 79.7B total parameters and a 256k-token context window, optimized for code generation and agentic programming tasks at $0.35 per 1M input and $1.20 per 1M output

• Qwen3.5 Omni Plus: Qwen3.5 Omni Plus is a fully multimodal model accepting text, image, speech, and video as input and producing text and speech as output, with a 256k-token context window, designed for voice-enabled and video understanding workflows at $0.40 per 1M input and $4.80 per 1M output

How does Qwen3 Max differ from Alibaba's other models?

Qwen3 Max is a proprietary non-reasoning model positioned at the upper-mid tier of Alibaba's hosted lineup, offering strong general-purpose performance without the extended thinking mode available in the Qwen3 reasoning variants.

• Pricing: At $1.655 per 1M input and $7.225 per 1M output, Qwen3 Max is one of Alibaba's more expensive proprietary offerings. Qwen3.6 35B A3B costs roughly one-seventh as much on input ($0.248), and Qwen3.5 Omni Flash comes in at $0.10 input, making Qwen3 Max appropriate for tasks where output quality justifies the premium

• Intelligence Index: Qwen3 Max ranks 31st of 71 non-reasoning models on the Artificial Analysis Intelligence Index, placing it above the median but below the newer Qwen3.7 Max (score: 57) and Qwen3.6 35B A3B (score: 43), which is notable given the significant price difference

• Speed: At 64.2 tokens per second, Qwen3 Max is faster than the median (58.7 t/s) but slower than the high-throughput Qwen3.7 Max (182.7 t/s) and Qwen3.6 35B A3B (172.6 t/s), making it a mid-tier choice for latency-sensitive applications

• Context window: Qwen3 Max offers a 262k-token context window, sufficient for most document processing tasks, though it falls short of the 1M-token windows available on Qwen3.6 Plus and Qwen3.7 Max for applications requiring very long context ingestion

• Modalities: Qwen3 Max is text-in, text-out only. It does not support image, video, or audio input, unlike the Qwen3.5 Omni series or Qwen3.6 Plus, which limits its applicability to text-based workflows

Qwen3 Max is best suited for text-focused general-purpose applications where a hosted proprietary endpoint is preferred over open-weight deployment, but it should be evaluated carefully against the newer Qwen3.6 and Qwen3.7 tiers given their improved performance-per-dollar.

What models should I consider using alongside Qwen3 Max?

No single model is optimal for every task. Here are models worth pairing with Qwen3 Max depending on what your product needs:

• Qwen3.7 Max (Alibaba): For tasks requiring the highest available reasoning quality within Alibaba's ecosystem, Qwen3.7 Max's score of 57 on the Intelligence Index and 1M-token context window make it the appropriate choice when Qwen3 Max's benchmark position is insufficient for complex analytical tasks

• Claude Sonnet 4.5 (Anthropic): When instruction adherence, structured output generation, or low hallucination rates on nuanced document tasks are the primary concern, Claude Sonnet 4.5 provides strong cross-provider redundancy and well-documented reliability for production workloads

• GPT-4.1 Mini (OpenAI): For high-volume, lower-complexity text tasks where Qwen3 Max's cost tier is difficult to justify, GPT-4.1 Mini offers broad regional availability and competitive quality at a lower blended rate, useful for classification, extraction, or summarization at scale

• Gemini 2.0 Flash (Google): When requests include image, video, or audio content that Qwen3 Max cannot handle, routing those inputs to Gemini 2.0 Flash adds multimodal coverage without restructuring the rest of the pipeline

• Qwen3.6 35B A3B (Alibaba): For cost-sensitive tasks where Qwen3 Max's higher price tier is difficult to justify, Qwen3.6 35B A3B delivers a higher Intelligence Index score at roughly one-sixth the blended cost per token, and is worth routing to as a primary or fallback within the Alibaba tier

What are the challenges of using Qwen3 Max in my product?

Like any production LLM, Qwen3 Max comes with tradeoffs worth planning for:

• Price-to-performance positioning: Qwen3 Max ranks 31st of 71 models on the Artificial Analysis Intelligence Index while being one of the more expensive options in Alibaba's lineup. Newer Alibaba models like Qwen3.6 35B A3B and Qwen3.7 Max offer higher benchmark scores at lower or comparable cost, which teams should weigh during model selection

• No reasoning mode: Unlike Qwen3.6 35B A3B and Qwen3.5 397B, Qwen3 Max does not include an extended thinking or reasoning mode. Tasks requiring multi-step logical reasoning, complex math, or structured problem decomposition may produce lower-quality outputs than reasoning-capable siblings

• Text-only input: Qwen3 Max does not support image, video, or audio inputs. Applications that occasionally receive multimodal content must implement routing logic to redirect those requests to a different model, adding pipeline complexity

• Verbosity: Qwen3 Max generated 13M tokens during the Artificial Analysis evaluation against a median of 7.9M, meaning outputs tend to be longer than necessary. Verbose outputs increase per-request output costs and may require post-processing to trim responses for user-facing applications

• Cost at scale: As request volume grows, token costs compound quickly without active cost management. At $7.225 per 1M output tokens, high-throughput deployments using Qwen3 Max can accumulate costs substantially faster than lower-cost alternatives within the same Alibaba model family

Why should I use Merge Gateway to route LLM requests with Qwen3 Max and every other model?

Using Qwen3 Max through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• Cost governance: Set hard or soft project budgets so Qwen3 Max spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers, giving you precise visibility before costs compound

• One API, every provider: Access Qwen3 Max and every other major LLM through a single endpoint and API key. Swap the model string to change providers without modifying application code

• Intelligent routing and automatic failover: Merge routes around Alibaba outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40 to 60% without touching your application code

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches Alibaba. Enforce per-project model and region policies without adding that logic to your application

How can I start routing requests to Qwen3 Max via Merge Gateway?

Getting Qwen3 Max running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For Qwen3 Max, the model string is alibaba/qwen3-max. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming Qwen3 Max as primary with one fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.