# gpt-oss-120b vs Hy-Memory-30B-A3B > gpt-oss-120b scores 70.2 (BB) to Hy-Memory-30B-A3B's 61 (C) 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-hy-memory-30b-a3b - Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy-memory-30b-a3b.md (~9,650 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 Hy-Memory-30B-A3B's 61 (C), and leads in 2 of 4 scored categories. Hy-Memory-30B-A3B leads on reach and documentation and upkeep. 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 - Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-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: - Risk, 95 against 60 - Agent interface, 90 against 65 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 ### Hy-Memory-30B-A3B (C) Good for: Agents with an external memory framework that want a local model for the write path on a 24 GB card Ahead on: - Reach, 85 against 75 - Documentation and upkeep, 68 against 45 Watch for: Not a general assistant or a tool caller ## Why the edge ### Risk: gpt-oss-120b +35 - 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. - Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60. ### Agent interface: gpt-oss-120b +25 - 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. - Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65. ### Documentation and upkeep: Hy-Memory-30B-A3B +23 - gpt-oss-120b (45): The model card earns 20, with all seven items in OpenAI's arXiv card. Training data earns 10, a general description with a cut-off. Last change earns 5, since the last change to code that runs the model was the Metal reference on 1 November 2025, a year before this check, and the files date from August 2025. It drops to 0 after 1 November 2026 unless the files, template or reference code change. Responsiveness earns 0, with no OpenAI reply in the 40 latest discussions. Version policy earns 5 as a first release. File health earns 5, the files agree but template and tokenizer reports wait without a reply. - Hy-Memory-30B-A3B (68): Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68. ### Reach: Hy-Memory-30B-A3B +10 - 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. - Hy-Memory-30B-A3B (85): Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85. ## Score by category | Category | Weight | gpt-oss-120b | Hy-Memory-30B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 85 | Hy-Memory-30B-A3B +10 | | Risk | 20% (20 this run) | 95 | 60 | gpt-oss-120b +35 | | Agent interface | 10% (10 this run) | 90 | 65 | gpt-oss-120b +25 | | Documentation and upkeep | 10% (10 this run) | 45 | 68 | Hy-Memory-30B-A3B +23 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **70.2 · BB** | **61 · C** | | ## Facts side by side | Fact | gpt-oss-120b | Hy-Memory-30B-A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenAI | Tencent (Hy 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 | 2026-10-16 | | 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. **Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them. ## 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 ### Hy-Memory-30B-A3B 1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user 2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does 3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does 4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented 5. Treat what it stores as unverified. It keeps what a conversation says, true or not ## Questions ### Which is better for AI agents, gpt-oss-120b or Hy-Memory-30B-A3B? gpt-oss-120b scores 70.2 (BB) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 2 of 4 scored categories. Hy-Memory-30B-A3B leads on reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy-memory-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "hy-memory-30b-a3b"}`. 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