Qwen develops the Qwen3.5 122B A10B, a chat model capable of streaming, function calling, extended thinking, and structured output. It is genuinely best at handling complex, high-context conversations, as evidenced by its large context window of 262,144 tokens.
Input
Output
Context
262K
Max Output
-
Parameters
125.1B
Input Modalities
Output Modalities
Loading capabilities…
The same model in other encodings or serving configurations. Requesting qwen3-5-122b-a10b lets routing pick among them; the ids below pin one build.
qwen3-5-122b-a10b:fp4quantizedEstimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
The creator's other models in the catalog, with their context, size and license where published.
Put this model beside its alternatives on the same evidence, or go back to the full catalog.
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3.5 122B A10B advertises a context window of 262,144 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Qwen 3.5 122B A10B is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Qwen 3.5 122B A10B accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
Qwen 3.5 122B A10B has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Qwen 3.5 122B A10B is shown as not available rather than estimated.
Yes. Qwen 3.5 122B A10B is served on the managed pool through the OpenAI-compatible endpoint as qwen3-5-122b-a10b, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 121.4 GB at INT8 for the weights and a default context, from the catalog's 125.1B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 22, 2026
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