Qwen3.5 Flash — multimodal chat (text/image/video) optimized for cost, 1M context.
Qwen3.5 Flash is a multimodal language model from the Qwen family, designed for fast inference and low cost. It accepts text, image, and video inputs and generates text responses. Its context window…
Qwen3.5 Flash accepts text, images (including multiple images per request), and video inputs. For video, the model can ingest frames or entire video files up to the context limit. It processes these modalities together in a single API call, allowing tasks such as describing a video scene, answering questions about an image, or combining text instructions with visual content. The exact video length supported depends on frame extraction and token budgeting within the 1M context window.
The model excels in tasks that require large context and multimodal input. Examples include summarizing long video transcripts, extracting structured data from scanned documents with images, and building conversational agents that reference visual context. It is also effective for high-throughput batch processing, where the low cost per token (especially input) makes it economical for millions of queries. For simple text-only tasks, a cheaper text-only model may be even more cost-efficient.
For tasks that are purely text-based and do not require the multimodal capability, a smaller or text-only model with lower pricing may be more cost-effective. Qwen3.5 Flash's strength lies in its multimodal and long-context abilities; if your application only needs short text completions, models like text-davinci or smaller Qwen variants may save money. Similarly, if you do not need the full 1M context, a model with a smaller window might reduce latency and cost.
The 'Flash' designation indicates that this model trades some benchmark accuracy for faster inference and lower computational cost. Compared to Qwen's larger models (e.g., Qwen3.5-72B), it uses a more efficient architecture with fewer parameters. Benchmark scores are published by Qwen but not provided in this catalog entry. In practice, it performs competitively on multimodal understanding tasks while maintaining lower latency per request, making it suitable for real-time applications.
Latency depends on input size, output length, and server load. Because Qwen3.5 Flash is designed for speed, it generally returns responses faster than larger models like Qwen3.5-72B for equivalent token counts. The exact tokens-per-second figures are not provided in the catalog. Users can test performance through OrcaRouter's endpoint to measure real-world latency for their specific use case. The 1M context window may introduce processing overhead for very long inputs.
Strengths include multimodal input, very large context, 65K output tokens, and low cost per token. The model handles long documents and videos well. Limitations: it is not the most accurate on complex reasoning or math tasks compared to larger frontier models. It may also struggle with nuanced multi-step instructions. For tasks where state-of-the-art output quality is critical, consider a larger Qwen or GPT-4o-class model. The 'Flash' architecture prioritizes throughput and cost over peak accuracy.
Pricing is $0.10 per 1 million input tokens (text, image, or video tokens) and $0.40 per 1 million output tokens. These rates are billed at the provider's rate with zero markup by OrcaRouter. There are no additional fees for the API gateway. This pricing is passed through directly, meaning the end user pays the same as if calling the provider's own API, without any OrcaRouter surcharge. Token counting follows standard tokenization for each modality.
The input token price ($0.10/1M) is very competitive among multimodal models. For example, many large multimodal models charge $2-10 per million input tokens. The output price ($0.40/1M) is also low. However, because the model has a 1M context window, processing very long inputs can result in high total cost despite the low per-token rate. Users should balance context length against the number of requests. For short inputs, smaller models may offer even lower absolute cost.
The catalog entry does not specify whether caching is available for Qwen3.5 Flash on OrcaRouter. Users should consult OrcaRouter's documentation for details on prompt caching or response caching features. If caching is not supported, repeated identical inputs will incur full token costs each time. For batch processing, consider deduplicating inputs or using a local cache to reduce API calls. The pricing remains at provider rates with no markup regardless of caching status.
Use the OpenAI-compatible endpoint at https://api.orcarouter.ai/v1. Set the model parameter to "qwen/qwen3.5-flash" in your request. Provide an API key from OrcaRouter. The request format matches OpenAI's chat completions API, supporting roles (system, user, assistant) and multimodal content using the "content" array with "type": "image_url" or "type": "text". For video, you may need to extract frames and pass them as images. Refer to OrcaRouter documentation for exact video handling guidelines.
Standard OpenAI-compatible parameters: temperature, top_p, max_tokens (up to 65536), stop sequences, presence_penalty, frequency_penalty, and seed. The max_tokens parameter is capped at 65536 to match the model's max output. The model supports streaming via stream: true. You can also set user IDs for tracking. For multimodal requests, include the image or video content within the user message. The model accepts up to the context window of 1,048,576 tokens total across all turns.
If you already use OpenAI's Python SDK, change the base_url to https://api.orcarouter.ai/v1 and update the model name to "qwen/qwen3.5-flash". Authentication uses an OrcaRouter API key instead of an OpenAI key. The request and response structures are identical. For migration from other providers, use the chat completions format. Ensure your system prompts and multimodal content are formatted as per OpenAI conventions. No code changes beyond the endpoint and model name are required.
Yes, the OrcaRouter API supports system messages, user messages, and assistant messages in a multi-turn conversation. The full conversation history counts against the 1,048,576 token context window. The model processes multimodal content in user messages. For video, you can send multiple frames as images in a single user message. The model maintains context across turns, so you can have extended interactions with long histories, provided total tokens remain under the limit.
Qwen3.5 Flash is a smaller, faster, and cheaper alternative to larger Qwen models like Qwen3.5-72B or Qwen3.5-32B. It sacrifices some benchmark accuracy for lower latency and cost. It shares the same multimodal input support and large context window (1M) as its larger siblings. For tasks that require state-of-the-art reasoning, the larger models are preferred. For high-volume or real-time applications where speed and cost matter more, Flash is the better choice.
GPT-4o offers higher accuracy on many reasoning and multimodal benchmarks but at a significantly higher price (typically $2.50 per million input tokens for GPT-4o and $0.15 for GPT-4o mini). Qwen3.5 Flash is cheaper ($0.10 input) and has a larger context window (1M vs 128K for GPT-4o). Its output token limit (65K) also exceeds GPT-4o mini (16K). For cost-sensitive applications with very long inputs, Qwen3.5 Flash may be more economical while still providing good multimodal understanding.
Claude 3 Haiku and Gemini 1.5 Flash are similar 'flash' models. Claude 3 Haiku is text-only and priced at $0.25 per million input tokens. Gemini 1.5 Flash supports multimodal input and has a 1M context window, priced at $0.075 per million input tokens. Qwen3.5 Flash's multimodal support and 1M context place it in a similar tier, with output pricing ($0.40/1M) higher than Gemini Flash ($0.30/1M) but lower than Haiku ($1.25/1M). Benchmark performance varies by task.
OrcaRouter hosts open-source models like Llama 3.1 8B and Mistral 7B. Qwen3.5 Flash is a proprietary model, but it competes on cost and capability. Open-source models often have lower or no per-token cost (you pay only for compute), but they may require more engineering to set up. Qwen3.5 Flash offers a managed API with zero markup, a 1M context window, and multimodal support that most open-source models lack. It is a good middle ground between open-source flexibility and proprietary quality.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="qwen/qwen3.5-flash",
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| Input / 1M tokens | $0.100 |
| Output / 1M tokens | $0.400 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
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GET /api/public/models/qwen/qwen3.5-flashOpen @misc{orcarouter_qwen3_5_flash,
title = {qwen/qwen3.5-flash API},
author = {qwen},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/qwen/qwen3.5-flash}
}qwen. (n.d.). qwen/qwen3.5-flash API. OrcaRouter. https://www.orcarouter.ai/models/qwen/qwen3.5-flash