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Models

Qwen3.5-35B-A3B

By Qwen. 36.0 billion parameters, a context window of 262,144 tokens and the licence apache-2.0.

Facts

Released
2026-02-24 the day the repository was first published on Hugging Face Hugging Face, read
Licence
apache-2.0 the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
36.0 billion counted from the safetensors weight files Hugging Face, read
Active parameters
4.7 billion estimate estimated from the expert counts and sizes in the configuration model configuration, read
Knowledge cutoff
Unknown
Context window
  • 262,144 tokens the maximum position embeddings in the model configuration model configuration, read
  • 262,144 tokens the context length OpenRouter lists OpenRouter, read
Longest output
  • 16,384 tokens the largest output OpenRouter's first provider allows OpenRouter, read
Tool calling
  • Yes the chat template in the tokenizer configuration accepts tool definitions Hugging Face, read
  • Yes OpenRouter lists tools among the supported parameters OpenRouter, read
Structured output
  • Yes OpenRouter lists structured outputs among the supported parameters OpenRouter, read
Reasoning controls
  • Yes OpenRouter lists reasoning controls OpenRouter, read
Inputs and outputs
text, image in, text out Hugging Face, read
Good for
  • Vision: Hugging Face files it under image-text-to-text Hugging Face, read
  • Reasoning: OpenRouter lists reasoning controls for it OpenRouter, read

Prices

Prices in US dollars per million tokens, as each source lists them
ProviderInputOutputContextAs ofSource
Merge Gateway direct0.057 USD0.459 USD131,072 older than 30 days; check the providermodels.dev, read
OrcaRouter direct0.057 USD0.459 USD262,144 older than 30 days; check the providermodels.dev, read
Darkbloom through OpenRouter0.080 USD0.750 USD262,144OpenRouter endpoints, read
Deep Infra direct0.140 USD1.00 USD262,144 older than 30 days; check the providermodels.dev, read
DeepInfra through OpenRouter0.140 USD1.00 USD262,144OpenRouter endpoints, read
Parasail through OpenRouter0.150 USD1.00 USD262,144OpenRouter endpoints, read
Alibaba through OpenRouter0.163 USD1.30 USD262,144OpenRouter endpoints, read
Kilo Gateway direct0.163 USD1.30 USD256,000 older than 30 days; check the providermodels.dev, read
AtlasCloud through OpenRouter0.225 USD1.80 USD262,144OpenRouter endpoints, read
NanoGPT direct0.225 USD1.80 USD260,096 older than 30 days; check the providermodels.dev, read
SiliconFlow (China) direct0.230 USD1.86 USD262,144 older than 30 days; check the providermodels.dev, read
SiliconFlow direct0.240 USD1.80 USD262,144 older than 30 days; check the providermodels.dev, read
SiliconFlow through OpenRouter0.240 USD1.80 USD262,144OpenRouter endpoints, read
CoreWeave direct0.250 USD1.25 USD262,144 older than 30 days; check the providermodels.dev, read
Mixlayer direct0.250 USD1.30 USD262,144 older than 30 days; check the providermodels.dev, read
Hugging Face direct0.250 USD2.00 USD262,144 older than 30 days; check the providermodels.dev, read
NovitaAI direct0.250 USD2.00 USD262,144 older than 30 days; check the providermodels.dev, read
Ofox direct0.290 USD1.83 USD256,000 older than 30 days; check the providermodels.dev, read
OpenRouter direct0.312 USD1.25 USD262,144 older than 30 days; check the providermodels.dev, read
Venice through OpenRouter0.312 USD1.25 USD256,000OpenRouter endpoints, read

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
IQ1_M8.8 GiB9.9 GiB10.3 GiB
IQ2_XXS9.9 GiB11.1 GiB11.6 GiB
IQ2_XS10.9 GiB12.1 GiB12.6 GiB
IQ2_S11.1 GiB12.3 GiB12.8 GiB
IQ2_M12.1 GiB13.3 GiB13.8 GiB
Q2_K12.6 GiB13.9 GiB14.3 GiB
Q2_K_L13.0 GiB14.4 GiB14.8 GiB
IQ3_XXS14.7 GiB16.1 GiB16.5 GiB
Q3_K_S15.3 GiB16.7 GiB17.2 GiB
IQ3_XS15.9 GiB17.4 GiB17.9 GiB
Q3_K_M15.9 GiB17.4 GiB17.9 GiB
Q3_K_L16.6 GiB18.0 GiB18.5 GiB
IQ3_M16.6 GiB18.1 GiB18.5 GiB
Q3_K_XL17.0 GiB18.5 GiB18.9 GiB
IQ4_XS18.3 GiB19.9 GiB20.4 GiB
IQ4_NL19.3 GiB21.0 GiB21.4 GiB
Q4_019.4 GiB21.0 GiB21.5 GiB
Q4_K_S20.0 GiB21.7 GiB22.1 GiB
Q4_K_M20.8 GiB22.4 GiB22.9 GiB
Q4_K_L21.1 GiB22.8 GiB23.3 GiB
Q4_121.3 GiB23.0 GiB23.5 GiB
Q5_K_S23.3 GiB25.2 GiB25.6 GiB
Q5_K_M24.1 GiB26.0 GiB26.5 GiB
Q5_K_L24.4 GiB26.3 GiB26.8 GiB
Q6_K28.8 GiB30.9 GiB31.4 GiB
Q6_K_L29.1 GiB31.2 GiB31.6 GiB
Q8_035.2 GiB37.6 GiB38.1 GiB
BF1666.2 GiB70.2 GiB70.6 GiB

Start it with a local runtime

  • Ollama
    ollama run hf.co/bartowski/Qwen_Qwen3.5-35B-A3B-GGUF:Q4_0
  • LM Studio
    lms get bartowski/Qwen_Qwen3.5-35B-A3B-GGUF
    lms server start
  • llama.cpp server
    llama-server -hf bartowski/Qwen_Qwen3.5-35B-A3B-GGUF:Q4_0 --jinja
  • vLLM
    vllm serve Qwen/Qwen3.5-35B-A3B
  • SGLang
    sglang serve --model-path Qwen/Qwen3.5-35B-A3B --port 30000

Use it from your harness

Set up for DeepInfra with the model Qwen/Qwen3.5-35B-A3B. Each endpoint page has the same setup for its own address.

OpenCode

Put this in opencode.json in your project folder:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "deepinfra": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "DeepInfra",
      "options": {
        "baseURL": "https://api.deepinfra.com/v1/openai",
        "apiKey": "{env:DEEPINFRA_TOKEN}"
      },
      "models": {
        "Qwen/Qwen3.5-35B-A3B": {
          "name": "Qwen3.5-35B-A3B",
          "limit": {
            "context": 262144,
            "output": 16384
          }
        }
      }
    }
  }
}
  • OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.

From OpenCode documentation, read .

Pi

Put this in ~/.pi/agent/models.json:

{
  "providers": {
    "deepinfra": {
      "baseUrl": "https://api.deepinfra.com/v1/openai",
      "api": "openai-completions",
      "apiKey": "$DEEPINFRA_TOKEN",
      "models": [
        {
          "id": "Qwen/Qwen3.5-35B-A3B"
        }
      ]
    }
  }
}
  • The apiKey field can name an environment variable as $NAME.

From Pi documentation, read .

Codex

Codex speaks only the Responses API, and DeepInfra documents no Responses address. A gateway that offers one can sit in between.

  • Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.

From Codex documentation, read .

Claude Code

Put this in ~/.claude/settings.json, or variables in your shell:

export ANTHROPIC_BASE_URL="https://api.deepinfra.com/anthropic"
export ANTHROPIC_AUTH_TOKEN="$DEEPINFRA_TOKEN"
export ANTHROPIC_MODEL="Qwen/Qwen3.5-35B-A3B"
claude
  • Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.

From Claude Code documentation, read .

Other ways to reach it

Published results

These are results other people published. Baltor did not run them and does not rank models by them.