# gpt-oss-120b vs Qwen-AgentWorld-35B-A3B > gpt-oss-120b scores 70.2 (BB) to Qwen-AgentWorld-35B-A3B's 52.1 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b - Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.md (~9,550 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself. - gpt-oss-120b: grade BB, 70.2/100, rank #6 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json - Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### gpt-oss-120b (BB) Good for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support Ahead on: - Reach, 75 against 55 - Risk, 95 against 55 - Agent interface, 90 against 50 Also in its favour: - Agent-ready, a grade of BB or better Watch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision ### Qwen-AgentWorld-35B-A3B (D) Good for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine Watch for: Not a general assistant, and the card doesn't describe using it to call tools ## Why the edge ### Risk: gpt-oss-120b +40 - gpt-oss-120b (95): File safety earns 25, safetensors only, with metal/model.bin a raw format and not pickle, and nothing from the repository runs on load. Provenance earns 15, files in OpenAI's linked organisation with a hash for every file. Safety evaluation earns 20, published results with SecureBio as an outside evaluator. Agent guidance earns 15 for the trained instruction hierarchy with prompt-injection results. Licence risk keeps all 15. The security programme earns 5 for OpenAI's Bugcrowd programme and disclosure policy, and the advisories half scores 0 because GitHub couldn't be checked, not because it was found missing. - 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: gpt-oss-120b +40 - gpt-oss-120b (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since OpenAI documents declarations and returned calls in harmony and tells vLLM users to parse with openai-harmony. Trained tool calling earns 10, because each call stops generation, so calls come one at a time. Structured output earns 15 for the documented Response Formats section with a JSON Schema. Reasoning control earns 10, three effort levels per request with the reasoning on the analysis channel. Context earns 10 for 131,072 trained 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: gpt-oss-120b +20 - gpt-oss-120b (75): Memory earns 10, because the smallest 4-bit build, Ollama's 65 GB gpt-oss:120b, needs about 78 GB with the fifth added, which fits the reference machine but not 64 GB. Runtimes earn the full 25 from Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 10, since OpenAI ships one precision, MXFP4. Terms earn 15 for Apache 2.0 with a policy that asks only for lawful use. Download earns 10, with no gate or account. Hosted earns 5, since 11 inference providers serve it on Hugging Face even though OpenAI's own API doesn't. - 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 | Category | Weight | gpt-oss-120b | Qwen-AgentWorld-35B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 55 | gpt-oss-120b +20 | | Risk | 20% (20 this run) | 95 | 55 | gpt-oss-120b +40 | | Agent interface | 10% (10 this run) | 90 | 50 | gpt-oss-120b +40 | | Documentation and upkeep | 10% (10 this run) | 45 | 41 | gpt-oss-120b +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **70.2 · BB** | **52.1 · D** | | ## Facts side by side | Fact | gpt-oss-120b | Qwen-AgentWorld-35B-A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenAI | 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 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-08-26 | 2026-06-25 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 3.5/5 (4) | none | ## Verdicts **gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025. **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 ### gpt-oss-120b 1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits 2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs 3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony 4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise 5. Set `Reasoning: low` in the system message for short tasks, medium is the default 6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users 7. Expect one tool call per turn and loop, don't wait for parallel calls ### 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, gpt-oss-120b or Qwen-AgentWorld-35B-A3B? gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "qwen-agentworld-35b-a3b"}`. 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How: https://www.anchorterminal.com/agents/#mcp