Qwen3 VL 235B A22B (Reasoning)

Given the high output token price, focus on generating concise and direct responses. Employ prompt engineering techniques to explicitly instruct the model to be brief, avoid unnecessary preamble, and stick to essential information.

• Use directives like "Be concise," "Summarize briefly," or "Provide only the answer."
• Implement post-processing to trim redundant phrases or expand on key points only when necessary.
• Test different prompt variations to find the sweet spot between output quality and length.

As shown in the provider analysis, Fireworks offers significantly lower costs and better performance across the board. Prioritize using Fireworks for production workloads unless specific regional or enterprise requirements dictate otherwise.

• Benchmark providers regularly as pricing and performance can change.
• Consider multi-cloud strategies if a secondary provider offers a compelling niche advantage.
• Negotiate custom pricing with providers for high-volume usage if applicable.

For tasks that don't require real-time interaction, batching multiple requests can sometimes lead to better cost efficiency or throughput, depending on the provider's API design and pricing tiers.

• Group similar requests together to reduce API call overhead.
• Utilize asynchronous processing for non-critical tasks to manage load and cost.
• Monitor provider-specific batching recommendations and limits.

The 262k context window is a powerful feature, but filling it unnecessarily will increase input token costs. Only include information that is directly relevant to the current task.

• Employ retrieval-augmented generation (RAG) to dynamically fetch and inject only pertinent context.
• Summarize previous turns in a conversation history rather than sending the full transcript.
• Pre-process documents to extract key sections before feeding them to the model.

Implement robust monitoring and logging for your model usage. Track input/output token counts, API calls, and associated costs to identify areas for optimization.

• Set up alerts for unusual spikes in token usage or costs.
• Analyze usage patterns to understand which parts of your application are most expensive.
• Use cost management tools provided by your cloud provider or third-party solutions.

Its primary strength lies in its exceptional intelligence and reasoning capabilities, scoring 54 on the Artificial Analysis Intelligence Index, significantly above average. It also supports multimodal inputs (text and image) and boasts a very large 262k token context window, making it ideal for complex analytical tasks.

While highly intelligent, it is a premium model with higher-than-average input token prices ($0.70/M) and significantly expensive output token prices ($8.40/M). For highly cost-sensitive applications, careful optimization of output length and provider selection (e.g., Fireworks) is crucial, or alternative models might be considered.

Based on our benchmarks, Fireworks consistently offers the best performance across latency (0.67s), output speed (49 t/s), and overall cost-effectiveness ($0.39/M blended price). Alibaba Cloud is the direct owner but has higher costs and latency.

With a 262k token context window, Qwen3 VL 235B A22B (Reasoning) offers one of the largest available, allowing it to process and maintain context over extremely long documents or complex conversations. This is a significant advantage for applications requiring deep understanding of extensive information.

The model supports both text and image inputs, making it a true multimodal model. It generates text outputs, enabling it to describe images, answer questions based on visual information, or combine text and visual data for complex reasoning tasks.

Its output speed of 44 tokens/second is slightly below the average but still robust. Combined with a very low Time to First Token (TTFT) of 0.67s from providers like Fireworks, it can be suitable for many real-time applications where initial responsiveness is key, provided the total output length is managed.

The '(Reasoning)' tag indicates that this variant of the Qwen3 VL model is specifically optimized or fine-tuned for tasks requiring advanced logical inference, problem-solving, and complex analytical capabilities, making it particularly strong in areas like scientific analysis, legal review, or strategic planning.