Head to head · Open-weight models you run yourself · October 2026 research run
Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B
Laguna XS 2.1 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
Best for Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.
Ahead on
- Reach, 80 against 55
- Agent interface, 85 against 50
- Documentation and upkeep, 46 against 41
Watch for
No reply from Poolside to any discussion, including reports about the tokenizer and remote code
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 Laguna XS 2.1 +35
Laguna XS 2.1 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.
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 Laguna XS 2.1 +25
Laguna XS 2.1 80
Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.
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.
Documentation and upkeep Laguna XS 2.1 +5
Laguna XS 2.1 46
Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 10, the linked report describes sources and filtering and Poolside's S 2.1 post gives the shared November 2025 cut-off. Last change 15, 19 August 2026, 76 days back. Responsiveness 0, Poolside answered none of the 6 discussions. Version policy 5, XS.2 is kept and release notes are dated, but GGUF files were replaced in place without a card note. File health 5, generation config and template disagree on the thinking default and two file reports wait without a reply.
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.
Score by category
| Category | Weight this run | Laguna XS 2.1 | Qwen-AgentWorld-35B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 55 | Laguna XS 2.1 +25 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 50 | Laguna XS 2.1 +35 |
| Documentation and upkeep | 10%10 | 46 | 41 | Laguna XS 2.1 +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 52.1/100 |
Facts side by side
| Fact | Laguna XS 2.1 | Qwen-AgentWorld-35B-A3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-06-25 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
Qwen-AgentWorld-35B-A3B
No score change since it was listed.
Verdicts
Laguna XS 2.1
A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.
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
Laguna XS 2.1
- Quickest start is
ollama run laguna-xs-2.1, 20 GB of files, the same Q4_K_M as Poolside's GGUF - On our reference machine use
llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB) - With vLLM use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on a build with vllm-project/vllm#47311, or--tool-call-parser glm47on older builds - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
Qwen-AgentWorld-35B-A3B
- 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
- With vLLM pass
--language-model-only, or startup fails on the missing vision weights - Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends
- Keep at least 128K of context, since the simulation depends on the history
- Thinking is on by default and comes back through the qwen3 reasoning parser
- A simulated observation is a prediction. Check anything that matters against the real environment
Questions
Which is better for AI agents, Laguna XS 2.1 or Qwen-AgentWorld-35B-A3B?
Laguna XS 2.1 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 Laguna XS 2.1 or Qwen-AgentWorld-35B-A3B
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- Laguna M.1 vs Laguna XS 2.1
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- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
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- Laguna XS 2.1 vs SenseNova-Loop-9B-Preview
- Laguna XS 2.1 vs SmolLM3-3B
- Laguna XS 2.1 vs Spark-X2.6-4B
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- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B
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- Laguna XS 2.1 vs WeVisOne-VL-2B
- Laguna XS 2.1 vs WeVisOne-VL-4B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs Qwen-AgentWorld-35B-A3B
- LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B
- LFM3-1.7B vs Qwen-AgentWorld-35B-A3B
- LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B
- Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B
- Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B
- LongCat-2.5 vs Qwen-AgentWorld-35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B
- MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B
- MiniCPM-o 4.5 vs Qwen-AgentWorld-35B-A3B
- MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B
- MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B
- MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B
- MiniMax-M2.5 vs Qwen-AgentWorld-35B-A3B
- MiniMax-M2.7 vs Qwen-AgentWorld-35B-A3B
- MiniMax-M3.1-Flash vs Qwen-AgentWorld-35B-A3B
- Mistral Large 4 vs Qwen-AgentWorld-35B-A3B
- Muse Glimmer 30B vs Qwen-AgentWorld-35B-A3B
- Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B
- Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B
- Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Super 120B A12B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Ultra 550B A55B vs Qwen-AgentWorld-35B-A3B
- North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B
- occamy-1.0 vs Qwen-AgentWorld-35B-A3B
- Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-35B-A3B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-397B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B
- OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B
- Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507
- Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-4B
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B
- Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B
- Qwen-AgentWorld-35B-A3B vs Qwen3.6-35B-A3B
- Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B
- Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next
- Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next
- Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2
- Qwen-AgentWorld-35B-A3B vs Recursive Flint
- Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview
- Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview
- Qwen-AgentWorld-35B-A3B vs SmolLM3-3B
- Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B
- Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash
- Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-xs-2-1.json·/api/v1/tools/qwen-agentworld-35b-a3b.json - From a terminal
anchor compare laguna-xs-2-1 qwen-agentworld-35b-a3b(the CLI) - Over MCP
compare_tools {"a": "laguna-xs-2-1", "b": "qwen-agentworld-35b-a3b"}at/mcp, no key