Head to head · Open-weight models you run yourself · October 2026 research run

Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B

Apodex-1.1-mini scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

Apodex-1.1-mini C

Best for Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence

Ahead on

  • Reach, 85 against 55
  • Risk, 65 against 55
  • Agent interface, 85 against 50

Watch for

No safety results, and agent guidance only from the harness

Qwen-AgentWorld-35B-A3B D

Best for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Not a general assistant, and the card doesn't describe using it to call tools

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Apodex-1.1-mini +35

Apodex-1.1-mini 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.

Qwen-AgentWorld-35B-A3B 50

Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.

Reach Apodex-1.1-mini +30

Apodex-1.1-mini 85

Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.

Qwen-AgentWorld-35B-A3B 55

Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.

Risk Apodex-1.1-mini +10

Apodex-1.1-mini 65

File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.

Qwen-AgentWorld-35B-A3B 55

File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Score by category

CategoryWeight this runApodex-1.1-miniQwen-AgentWorld-35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258555Apodex-1.1-mini +30
Risk20%206555Apodex-1.1-mini +10
Agent interface10%108550Apodex-1.1-mini +35
Documentation and upkeep10%103841Qwen-AgentWorld-35B-A3B +3
Negative events≤1500
TotalC 61/100D 52.1/100

Facts side by side

FactApodex-1.1-miniQwen-AgentWorld-35B-A3B
KindOpen-weight modelOpen-weight model
VendorApodex AIAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-08-252026-06-25
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3.3/5 (4)none

Verdicts

Apodex-1.1-mini

An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.

Qwen-AgentWorld-35B-A3B

A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.

Before you call either

Apodex-1.1-mini

  1. Serve it with vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3, or the same parsers in SGLang, so tool calls and reasoning come back parsed
  2. For llama.cpp, llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek, and the card passes the repository's chat_template.jinja with --chat-template-file
  3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks
  4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768
  5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block
  6. The template keeps all earlier reasoning in the context unless preserve_thinking is false, which grows long agent sessions
  7. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to available_tools[name](**args) before running
  9. Validate JSON output yourself, since structured output isn't documented

Qwen-AgentWorld-35B-A3B

  1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation
  2. With vLLM pass --language-model-only, or startup fails on the missing vision weights
  3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends
  4. Keep at least 128K of context, since the simulation depends on the history
  5. Thinking is on by default and comes back through the qwen3 reasoning parser
  6. A simulated observation is a prediction. Check anything that matters against the real environment

Questions

Which is better for AI agents, Apodex-1.1-mini or Qwen-AgentWorld-35B-A3B?

Apodex-1.1-mini scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.

Other comparisons with Apodex-1.1-mini or Qwen-AgentWorld-35B-A3B

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