qwen3.5-122b

Tool-calling enables agentic coding loops, but no SWE-Bench or comparable coding score is published for this variant. Fit is provisional pending a coding evaluation.

Large parameter count suggests competent long-form generation, but no context window or writing-quality benchmark is published. Fit is inferred from scale rather than verified capability.

Tool-calling can drive browser action APIs, but no vision or screenshot capability is listed, so page-state perception depends on an external DOM or accessibility layer. Viable for text-structured browser control only.

As a general-purpose instruction model at roughly 122B parameters, it should handle standard summarisation. The context window is unspecified, which limits confidence on long-document inputs.

Tool-calling supports multi-step agent loops, but the card publishes no reasoning or extended-thinking capability and no planning benchmarks. Suitable where the planner can be validated in-loop.

Tool-calling supports routing decisions to downstream handlers, and the ~122B scale is adequate for intent-level triage. The card carries no classification benchmark, so accuracy needs local validation.

No vision or image-input capability is documented on the card, so OCR of scanned or image-based documents is out of scope for this model. Route OCR workloads to a vision-capable or dedicated OCR model.

The card lists tool-calling as the only capability and no vision input, so image question-answering is not supported by documented signal. Select a vision-capable variant for this use case.

This is a text model with no audio output capability listed on the card. Speech synthesis requires a dedicated TTS model.

No audio input capability is documented for this variant, so it cannot process speech for transcription. Use a dedicated ASR model instead.