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

Hy-Memory-30B-A3B vs SmolLM3-3B

Hy-Memory-30B-A3B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk, agent interface 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 4969 open weights comparisons

Which one, for what

Hy-Memory-30B-A3B C

Best for Agents with an external memory framework that want a local model for the write path on a 24 GB card

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Not a general assistant or a tool caller

SmolLM3-3B C

Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device

Ahead on

  • Risk, 65 against 60
  • Agent interface, 80 against 65
  • Documentation and upkeep, 74 against 68

Watch for

65,536 tokens trained; 131,072 needs a YaRN edit to config.json

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 SmolLM3-3B +15

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.

SmolLM3-3B 80

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN.

Documentation and upkeep SmolLM3-3B +6

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.

SmolLM3-3B 74

Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply.

Risk SmolLM3-3B +5

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.

SmolLM3-3B 65

File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent.

Score by category

CategoryWeight this runHy-Memory-30B-A3BSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%206065SmolLM3-3B +5
Agent interface10%106580SmolLM3-3B +15
Documentation and upkeep10%106874SmolLM3-3B +6
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactHy-Memory-30B-A3BSmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorTencent (Hy team)Hugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-162025-09-10
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3.3/5 (4)

How the scores have moved

Verdicts

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.

SmolLM3-3B

A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.

Before you call either

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

SmolLM3-3B

  1. Start it with vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text
  2. Pass chat_template_kwargs with enable_thinking false for short tasks, since thinking is on by default; a /think or /no_think flag in the system prompt overrides the kwarg
  3. Put earlier tool calls in the assistant message content, since the template ignores a tool_calls field when it renders history
  4. Add the YaRN rope_scaling block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
  5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
  6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
  7. Validate JSON output yourself or use the runtime's guided decoding

Questions

Which is better for AI agents, Hy-Memory-30B-A3B or SmolLM3-3B?

Hy-Memory-30B-A3B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk, agent interface and documentation and upkeep.

Other comparisons with Hy-Memory-30B-A3B or SmolLM3-3B

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