Qwen3.5 Plus — multimodal chat (text/image/video), 1M context, strong coding + agent capability.
Qwen3.5-Plus is a large language model (LLM) from the Qwen series developed by Alibaba Cloud's Qwen team. It supports a context window of 1,048,576 tokens and a maximum output of 65,536 tokens. Input…
Based on its design, Qwen3.5-Plus can perform a wide range of language and multimodal tasks. Text tasks include summarization, question answering, translation, code generation, and reasoning over long documents. With image and video input, it can describe visual content, answer questions about images, or analyze video footage. The large context makes it particularly effective at tasks that require scanning large volumes of text, such as legal discovery, scientific literature review, or multi-turn dialogues. The model is also capable of following complex instructions across diverse domains.
If your use case involves only short text prompts (e.g., few hundred tokens) and does not require multimodal input, a smaller model such as Qwen2.5-7B or a similarly compact LLM may be more cost-effective. The 1M context and large parameter count of Qwen3.5-Plus incur higher per-token pricing and slower inference relative to smaller alternatives. Also, if you do not need the maximum output length of 65k tokens, a cheaper model with shorter output limits could suffice. Evaluate the minimum context length and modality requirements of your task before selecting this model.
Yes, the model accepts image and video as input modalities. This allows it to understand visual scenes, read text in images, or analyze videos. The exact method of passing video (e.g., as a stream of frames, a single keyframe, or a compressed video file) is not specified in the provided facts. Users should consult OrcaRouter's API documentation for the required input format. Like many multimodal LLMs, processing video may consume a significant number of tokens per frame, so careful management of the context window is necessary to avoid truncation.
The provided facts do not include information about tool use or function calling. Typically, many Qwen models support such features through the OpenAI-compatible API, but this cannot be confirmed for Qwen3.5-Plus from the given data. Developers should test the model with tool calling schemas to determine compatibility. If tool use is essential, consider using a model where that capability is explicitly documented. OrcaRouter's API supports the standard OpenAI parameters, so you may attempt to use function_call or tools in your request.
No benchmark scores are provided in the given facts for Qwen3.5-Plus. Without specific performance numbers (e.g., MMLU, HumanEval, or multimodal benchmarks), it is not possible to objectively compare its accuracy or reasoning ability to other models. Users should run their own evaluations on representative tasks to gauge performance. Based on the Qwen lineage, earlier models have shown competitive results; however, this specific version’s scores are not publicized in the available data. Refer to Alibaba Cloud's official Qwen releases for potential benchmark results.
Latency and throughput are not specified in the provided facts. In general, larger models with a 1M context window are heavier to compute, especially if the full context is used. Generation speed will depend on the output length, the number of visual tokens, and the underlying infrastructure. Using OrcaRouter, you may experience lower latency with smaller batch sizes and by limiting the context to only what is necessary. Streaming (chat.completions with stream=true) can also reduce perceived latency as tokens are returned incrementally.
The primary strength of Qwen3.5-Plus is its large context window of 1,048,576 tokens, which allows it to handle very long documents and conversations without information loss. Its multimodal support (text, image, video) broadens the range of inputs it can process. The max output of 65,536 tokens is also generous, enabling generation of lengthy summaries, reports, or code. These features make it a good candidate for tasks that require both heavy context and visual understanding in a single model call.
Without specific benchmark data, its exact performance relative to other LLMs is unknown. Large context windows can lead to increased computational cost and latency. The model may also have difficulties with very long contexts due to the “lost-in-the-middle” phenomenon common in many LLMs. Additionally, the provided data does not mention whether the model supports languages other than English; its multilingual capability is uncertain. Finally, pricing information is not given, so users must factor in the cost of processing many tokens.
Specific per-token or per-request pricing for Qwen3.5-Plus is not included in the provided facts. Typically, LLM providers charge based on the number of input and output tokens, and sometimes attach a surcharge for image or video processing. To obtain current rates, you should consult OrcaRouter's pricing page or contact their sales team. The pricing for this model will likely be higher than for smaller Qwen variants due to its larger context and multimodal capacity. Always verify costs before integrating.
When using a 1M context window, costs can accumulate rapidly if you fill the entire context with tokens. For tasks that can be accomplished with a shorter context (e.g., 32k tokens), you may be overpaying by using this model. Similarly, processing many images or a long video will consume many input tokens. The max output of 65,536 tokens also means generation can be costly if you produce long responses. Consider using a smaller model for simple tasks and reserving Qwen3.5-Plus for scenarios that genuinely need the large context and multimodal input.
The provided facts do not mention any caching or discounts for repeated tokens on Qwen3.5-Plus. Some API providers offer prompt caching that reduces the cost for identical prefix tokens across multiple calls. OrcaRouter may or may not support such a feature. To find out, refer to the OrcaRouter documentation or contact support. If caching is available, it could significantly lower costs for use cases like multi-turn conversations with a common system prompt or repeated context.
Qwen3.5-Plus is accessed through OrcaRouter's OpenAI-compatible API. Set the base URL to https://api.orcarouter.ai/v1. Use the model id "qwen/qwen3.5-plus". Authentication is typically done via an API key in the Authorization header (e.g., "Bearer YOUR_API_KEY"). For a chat completion request, send a POST to /chat/completions with a JSON body containing the "model" field set to the model id, and a "messages" array following OpenAI's format. Example: {"model": "qwen/qwen3.5-plus", "messages": [{"role": "user", "content": "Hello"}]}.
OrcaRouter supports standard OpenAI parameters including "messages", "max_tokens", "temperature", "top_p", "frequency_penalty", "presence_penalty", "stop", and "stream". Because the model supports image and video input, you may also pass multimodal content in the "content" field as an array of objects with "type":"text" and "type":"image_url" (or similar). The exact schema for video is not defined in the provided data. Refer to OrcaRouter's API docs for the complete parameter list. Note that "max_tokens" must not exceed the model's max output of 65,536 tokens.
To switch from a different model to Qwen3.5-Plus, update the "model" field in your API request from your previous model id (e.g., "gpt-4" or "qwen2.5-72b") to "qwen/qwen3.5-plus". Ensure your code can handle the larger context and multimodal input if you intend to use those features. If you were using a model that supported tool calls or parallel function calling, test those features with Qwen3.5-Plus to ensure compatibility. Also, adjust your token limits if your previous model had a smaller max output (set max_tokens appropriately).
Yes, streaming is supported via the standard OpenAI API parameter: set "stream": true in your request. This returns tokens as they are generated, reducing perceived latency. The response will be a stream of Server-Sent Events (SSE). Each event contains a delta of the next part of the message. This works identically to OpenAI's streaming mode. For multimodal inputs, the first chunk may have a slight delay while the model processes images or video. OrcaRouter's API follows the same streaming format as OpenAI, so existing streaming code can be reused with the new model id.
Qwen3.5-Plus is a newer iteration in the Qwen series. The provided facts do not include specific performance improvements over Qwen2.5, but typically newer versions add longer context support and refined training. Qwen2.5 models generally have context windows up to 128k tokens, whereas Qwen3.5-Plus offers 1M. Also, Qwen3.5-Plus explicitly lists video as an input modality, which may not be available in older Qwen2.5 variants. If you do not need the larger context or video input, a Qwen2.5 model may be more cost-efficient and faster.
Models like Gemini 1.5 Pro (1M tokens), Claude 3.5 Sonnet (200k), and GPT-4 Turbo (128k) also offer long contexts. Qwen3.5-Plus matches the 1M token context of Gemini 1.5 Pro and exceeds most others. The addition of video input is also relatively rare among LLMs. However, without benchmark data, it is difficult to compare accuracy, reasoning, or coding ability. Pricing and latency also vary by provider. Users should evaluate on their specific tasks. OrcaRouter provides access to multiple models, making it easy to switch and compare.
You would choose this model if your use case requires both a very long context (above 256k tokens) and multimodal input (text, image, video) in a single model. For example, analyzing hours of video with accompanying transcripts, or reading an entire book with embedded diagrams. If your task is purely text with a short context, a cheaper and faster alternative (e.g., Qwen2.5-7B or GPT-4o-mini) is more suitable. Also, if you need to output more than 16k tokens, the 65k max output of Qwen3.5-Plus may be advantageous.
The provided facts do not include details about data handling or privacy for Qwen3.5-Plus. When using OrcaRouter, you should review their privacy policy and terms of service to understand how data is processed, stored, or logged. As with any third-party API, avoid sending sensitive personal information unless you have confirmed the provider's security certifications (e.g., SOC 2, GDPR compliance). The model itself is hosted on infrastructure managed by OrcaRouter and Alibaba Cloud, and typical API providers retain data only temporarily for service delivery.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="qwen/qwen3.5-plus",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)enable_searchenable_thinkinginclude_reasoninglogprobsmax_tokensnparallel_tool_callspresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstreamstream_optionstemperaturethinking_budgettool_choicetoolstop_ktop_logprobstop_p| Tier | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| ≤ 256K | $0.400 | $2.40 |
| ≤ 1.0M | $0.500 | $3.00 |
| Tier selected by input token count of each request | ||
Estimate based on list price
Tiered pricing — this estimate uses base-tier rates.
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/qwen/qwen3.5-plusOpen @misc{orcarouter_qwen3_5_plus,
title = {qwen/qwen3.5-plus API},
author = {qwen},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/qwen/qwen3.5-plus}
}qwen. (n.d.). qwen/qwen3.5-plus API. OrcaRouter. https://www.orcarouter.ai/models/qwen/qwen3.5-plus