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

Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B

Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. 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

Ling-3.1-flash-1019 C

Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding

Ahead on

  • Reach, 60 against 55
  • Agent interface, 60 against 50
  • Documentation and upkeep, 56 against 41

Watch for

574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it

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

Ahead on

  • Risk, 55 against 45

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.

Documentation and upkeep Ling-3.1-flash-1019 +15

Ling-3.1-flash-1019 56

Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.

Qwen-AgentWorld-35B-A3B 41

Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.

Risk Qwen-AgentWorld-35B-A3B +10

Ling-3.1-flash-1019 45

File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.

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.

Agent interface Ling-3.1-flash-1019 +10

Ling-3.1-flash-1019 60

Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in <tools>) and how calls come back (<tool_call>name with <arg_key> and <arg_value> pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.

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 Ling-3.1-flash-1019 +5

Ling-3.1-flash-1019 60

Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.

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.

Score by category

CategoryWeight this runLing-3.1-flash-1019Qwen-AgentWorld-35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256055Ling-3.1-flash-1019 +5
Risk20%204555Qwen-AgentWorld-35B-A3B +10
Agent interface10%106050Ling-3.1-flash-1019 +10
Documentation and upkeep10%105641Ling-3.1-flash-1019 +15
Negative events≤1500
TotalC 54.8/100D 52.1/100

Facts side by side

FactLing-3.1-flash-1019Qwen-AgentWorld-35B-A3B
KindOpen-weight modelOpen-weight model
VendorinclusionAI (Ant Group)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-042026-06-25
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked

Verdicts

Ling-3.1-flash-1019

inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.

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

Ling-3.1-flash-1019

  1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
  2. With vLLM pass --trust-remote-code, --tool-call-parser ling3 and --reasoning-parser ling3
  3. Set chat_template_kwargs enable_thinking false for direct answers. Reasoning comes back in reasoning_content per SGLang's cookbook
  4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
  5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card
  6. 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, Ling-3.1-flash-1019 or Qwen-AgentWorld-35B-A3B?

Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk.

Other comparisons with Ling-3.1-flash-1019 or Qwen-AgentWorld-35B-A3B

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