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On our own GPUs

Qwen3.6-35B-A3B API

Apache 2.0, 3B active per token, tuned specifically for agentic coding.

  • Context

    128K

  • Max output

    32K

  • Serving precision

    BF16

  • Licence

    Apache-2.0

Qwen3.6-35B-A3B is a mixture-of-experts model from Alibaba’s Qwen team, shipped in April 2026 with 35B total parameters and roughly 3B active per token under an Apache 2.0 licence. It is tuned explicitly for agentic coding and works with the open-source terminal agent OpenClaw. Sparse activation keeps unit cost very low, which makes it the right model to absorb the many routine steps in a coding agent while larger models handle the pivotal ones.

Open weights, deployed in facilities we build out and lease directly, operated by us. We resell nobody else’s account quota.

Specifications

Model ID
qwen3.6-35b-a3b
Published by
Alibaba Qwen
How it is served
On our own GPUs
Licence
Apache-2.0
Total parameters
35B MoE
Active parameters
3B
Context
128K
Max output
32K
Serving precision
BF16
Input modalities
Text
Released
2026-04-01

What it is good at

A 3B-active cost structure
Inference touches roughly 3B parameters, so unit cost sits far below same-generation flagships. The read-a-file, list-a-directory, run-a-command steps that dominate agent traces are the cheapest possible work to hand it.
Apache 2.0, the most permissive tier
No restrictions on commercial use, modification or redistribution, and no user-count threshold. For teams that need private deployment or their own fine-tune, this is the lowest legal overhead available.
Tuned for terminal agents
Explicitly tuned for agentic coding and known to work with open-source terminal agents like OpenClaw. Tool-call formatting stays stable, which is the part of these pipelines most likely to break.

Where it fits

  • High-frequency routine steps inside coding agents
  • Cost-sensitive bulk code review and patch generation
  • Local or private-environment development assistants
  • The cheap first hop in a multi-model routing setup

Three steps

Change one line of base_url, put the model ID below in the `model` field, and leave the rest of your code alone.

model: "qwen3.6-35b-a3b"

curl https://api.nexaix.net/v1/chat/completions \
  -H "Authorization: Bearer $NEXAIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-35b-a3b",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Summarise this contract." }
    ]
  }'

About this model

Is 35B big enough?

It depends on how you tier your work. As a standalone general model it is clearly behind the trillion-parameter flagships, but in an agent loop most steps are simple and deterministic — routing them here can drop total cost by an order of magnitude. Escalate the pivotal reasoning steps to DeepSeek V4 Pro or Kimi K3.

What is the context window?

128K, extended via YaRN. If you need a million-token window, use GLM-5.2, DeepSeek V4 or Kimi K3.

Does it support tool calling?

Yes, using the standard OpenAI tools / tool_calls format — no change to your existing code.

Also worth a look

Run your own eval on it

Take the trial credit, run the evals you already have against this model, and then decide whether it gets your production traffic.