Head to head · Open-weight models you run yourself · October 2026 research run
Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next
Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and documentation and upkeep. 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 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
- Documentation and upkeep, 41 against 36
Watch for
Not a general assistant, and the card doesn't describe using it to call tools
Best for Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.
Ahead on
- Reach, 65 against 55
- Agent interface, 70 against 50
Watch for
No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB
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 Qwen3.8-Flash-Next +20
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.
Qwen3.8-Flash-Next 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.
Reach Qwen3.8-Flash-Next +10
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.
Qwen3.8-Flash-Next 65
Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights.
Risk Qwen-AgentWorld-35B-A3B +10
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.
Qwen3.8-Flash-Next 45
File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.
Documentation and upkeep Qwen-AgentWorld-35B-A3B +5
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.
Qwen3.8-Flash-Next 36
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described beyond a token count relative to an earlier model. Last change 15, the FP8 config fix on 31 August 2026, 63 days before 2 November. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports (29 to 79, 27 August to 26 October), its only reply came in the first week. Version policy 5, a first release with nothing replaced. File health 5, the files agree with the card, but a 19 October report about the template and a pull request to fix it still wait without a reply. 11 + 0 + 15 + 0 + 5 + 5 = 36.
Score by category
| Category | Weight this run | Qwen-AgentWorld-35B-A3B | Qwen3.8-Flash-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 65 | Qwen3.8-Flash-Next +10 |
| Risk | 20%20 | 55 | 45 | Qwen-AgentWorld-35B-A3B +10 |
| Agent interface | 10%10 | 50 | 70 | Qwen3.8-Flash-Next +20 |
| Documentation and upkeep | 10%10 | 41 | 36 | Qwen-AgentWorld-35B-A3B +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 52.1/100 | C 61.8/100 |
Facts side by side
| Fact | Qwen-AgentWorld-35B-A3B | Qwen3.8-Flash-Next |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-25 | 2026-08-31 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |
Verdicts
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.
Qwen3.8-Flash-Next
A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.
Before you call either
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
Qwen3.8-Flash-Next
- On a 128 GB Mac start it with
ollama run qwen3.8-flash-next:125b-mlx(105 GB, NVFP4), on Linux the smallest Ollama build isqwen3.8-flash-next:125b-a6b-q4_K_Mat 120 GB, which won't fit a 107.4 GB GPU share - On a server,
vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does
- Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse to skip it, and read the reasoning fromreasoning_content, even though SGLang's cookbook says thinking can't be turned off reasoning_efforttakes xhigh (default), medium or low, and any other value makes the template raise an error- Send at most one system message and put it first, since the template raises an error otherwise
- Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
- vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
- Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product
Questions
Which is better for AI agents, Qwen-AgentWorld-35B-A3B or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk and documentation and upkeep.
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- Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct
- Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2
- Qwen3.8-Flash-Next vs Recursive Flint
- Qwen3.8-Flash-Next vs Ripple-65B Preview
- Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview
- Qwen3.8-Flash-Next vs SmolLM3-3B
- Qwen3.8-Flash-Next vs Spark-X2.6-4B
- Qwen3.8-Flash-Next vs Step 3.7 Flash
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2
- Qwen3.8-Flash-Next vs WeVisOne-VL-2B
- Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- Qwen3.8-Flash-Next vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen-agentworld-35b-a3b.json·/api/v1/tools/qwen3-8-flash-next.json - From a terminal
anchor compare qwen-agentworld-35b-a3b qwen3-8-flash-next(the CLI) - Over MCP
compare_tools {"a": "qwen-agentworld-35b-a3b", "b": "qwen3-8-flash-next"}at/mcp, no key