Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 26B A4B IT vs Hy-Memory-30B-A3B
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Hy-Memory-30B-A3B's 61 (C), 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 4969 open weights comparisons
Which one, for what
Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Reach, 90 against 85
- Risk, 70 against 60
- Agent interface, 85 against 65
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No safety evaluation figures published
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
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 Gemma 4 26B A4B IT +20
Gemma 4 26B A4B IT 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.
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.
Risk Gemma 4 26B A4B IT +10
Gemma 4 26B A4B IT 70
File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.
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.
Reach Gemma 4 26B A4B IT +5
Gemma 4 26B A4B IT 90
Memory 25, Google's own 4-bit QAT GGUF is 14.4 GB, which with a fifth on top is 17.3 GB and lands in the 24 GB band, with the 1.2 GB vision projector extra for images. Runtimes 25, released support in vLLM, Transformers, llama.cpp, Ollama, SGLang and LM Studio, six of six. Builds 10, Google publishes one quantised precision (Q4_0 GGUF), since its unquantised QAT repository is BF16 and there's no FP8 build. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 5, the Gemini API serves it and four inference providers are live on Hugging Face.
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 this run | Gemma 4 26B A4B IT | Hy-Memory-30B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Gemma 4 26B A4B IT +5 |
| Risk | 20%20 | 70 | 60 | Gemma 4 26B A4B IT +10 |
| Agent interface | 10%10 | 85 | 65 | Gemma 4 26B A4B IT +20 |
| Documentation and upkeep | 10%10 | 65 | 68 | Hy-Memory-30B-A3B +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.2/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 26B A4B IT | Hy-Memory-30B-A3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | 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 | 2026-07-20 | 2026-10-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |
Verdicts
Gemma 4 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
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
Gemma 4 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Validate tool names and arguments before running them, and check any JSON yourself
Hy-Memory-30B-A3B
- Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user
- 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 - Pass
reasoning_effortas no_think inchat_template_kwargsand temperature 0, as the card's example does - Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented
- Treat what it stores as unverified. It keeps what a conversation says, true or not
Questions
Which is better for AI agents, Gemma 4 26B A4B IT or Hy-Memory-30B-A3B?
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories.
Other comparisons with Gemma 4 26B A4B IT or Hy-Memory-30B-A3B
- Agents-A1-4B vs Gemma 4 26B A4B IT
- Agents-A1-4B vs Hy-Memory-30B-A3B
- Apertus v1.5 8B vs Gemma 4 26B A4B IT
- Apertus v1.5 8B vs Hy-Memory-30B-A3B
- Apodex-1.1-mini vs Gemma 4 26B A4B IT
- Apodex-1.1-mini vs Hy-Memory-30B-A3B
- Arete-27B vs Gemma 4 26B A4B IT
- Arete-27B vs Hy-Memory-30B-A3B
- BigBang-v1 vs Gemma 4 26B A4B IT
- BigBang-v1 vs Hy-Memory-30B-A3B
- Carbon-X vs Gemma 4 26B A4B IT
- Carbon-X vs Hy-Memory-30B-A3B
- Cosmos-Reason2-2B vs Gemma 4 26B A4B IT
- Cosmos-Reason2-2B vs Hy-Memory-30B-A3B
- Darwin-36B-Opus vs Gemma 4 26B A4B IT
- Darwin-36B-Opus vs Hy-Memory-30B-A3B
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Hy-Memory-30B-A3B
- DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT
- DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B
- DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT
- DeepSeek-V4-Flash-0731 vs Hy-Memory-30B-A3B
- DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT
- DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B
- Gemma 4 12B IT vs Gemma 4 26B A4B IT
- Gemma 4 12B IT vs Hy-Memory-30B-A3B
- Gemma 4 26B A4B IT vs Gemma 4 31B IT
- Gemma 4 26B A4B IT vs Gemma 4 E2B IT
- Gemma 4 26B A4B IT vs Gemma 4 E4B IT
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs GLM-4.7-Flash
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs GLM-5.3-Flash
- Gemma 4 26B A4B IT vs gpt-oss-120b
- Gemma 4 26B A4B IT vs gpt-oss-20b
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Granite 4.2 3B
- Gemma 4 26B A4B IT vs Granite 4.2 8B
- Gemma 4 26B A4B IT vs Hy3
- Gemma 4 26B A4B IT vs Inkling
- Gemma 4 26B A4B IT vs Inkling-Small
- Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Gemma 4 26B A4B IT vs Kimi K3
- Gemma 4 26B A4B IT vs Kimi K3.1 Pro
- Gemma 4 26B A4B IT vs Kolibri-1
- Gemma 4 26B A4B IT vs Kronos
- Gemma 4 26B A4B IT vs Laguna M.1
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs Laguna XS 2.1
- Gemma 4 26B A4B IT vs LBS-27B
- Gemma 4 26B A4B IT vs LFM2.5-2.6B
- Gemma 4 26B A4B IT vs LFM3-1.7B
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs LFM3-Omni-2B
- Gemma 4 26B A4B IT vs LFM3-VL-2B
- Gemma 4 26B A4B IT vs Ling-3.0-tiny
- Gemma 4 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 26B A4B IT vs MiniCPM-o 4.5
- Gemma 4 26B A4B IT vs MiniCPM-V 4.7 1B
- Gemma 4 26B A4B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 26B A4B IT vs MiniCPM5-2B
- Gemma 4 26B A4B IT vs MiniMax-M2.5
- Gemma 4 26B A4B IT vs MiniMax-M2.7
- Gemma 4 26B A4B IT vs MiniMax-M3.1-Flash
- Gemma 4 26B A4B IT vs Mistral Large 4
- Gemma 4 26B A4B IT vs Muse Glimmer 30B
- Gemma 4 26B A4B IT vs Nanbeige4.3-33B
- Gemma 4 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano 4B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Gemma 4 26B A4B IT vs Nemotron 3 Super 120B A12B
- Gemma 4 26B A4B IT vs Nemotron 3 Ultra 550B A55B
- Gemma 4 26B A4B IT vs occamy-1.0
- Gemma 4 26B A4B IT vs Olmo 3 7B Think
- Gemma 4 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B
- Gemma 4 26B A4B IT vs Ornith-1.5-397B
- Gemma 4 26B A4B IT vs Ornith-1.5-9B
- Gemma 4 26B A4B IT vs Qwen-AgentWorld-35B-A3B
- Gemma 4 26B A4B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 26B A4B IT vs Qwen3-4B-Instruct-2507
- Gemma 4 26B A4B IT vs Qwen3.5-0.8B
- Gemma 4 26B A4B IT vs Qwen3.5-122B-A10B
- Gemma 4 26B A4B IT vs Qwen3.5-2B
- Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B
- Gemma 4 26B A4B IT vs Qwen3.5-4B
- Gemma 4 26B A4B IT vs Qwen3.5-9B
- Gemma 4 26B A4B IT vs Qwen3.6-27B
- Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B
- Gemma 4 26B A4B IT vs Qwen3.8-27B
- Gemma 4 26B A4B IT vs Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-Coder-Next
- Gemma 4 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-2B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-32B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-4B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 26B A4B IT vs Qwopus3.8-27B-Flash-V2
- Gemma 4 26B A4B IT vs Recursive Flint
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs SmolLM3-3B
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 26B A4B IT vs Baidu Unlimited-OCR-2
- Gemma 4 31B IT vs Hy-Memory-30B-A3B
- Gemma 4 E2B IT vs Hy-Memory-30B-A3B
- Gemma 4 E4B IT vs Hy-Memory-30B-A3B
- GLM-4.5-Air vs Hy-Memory-30B-A3B
- GLM-4.7-Flash vs Hy-Memory-30B-A3B
- GLM-5.3-Flash vs Hy-Memory-30B-A3B
- GLM-5.3 vs Hy-Memory-30B-A3B
- gpt-oss-120b vs Hy-Memory-30B-A3B
- gpt-oss-20b vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 3B vs Hy-Memory-30B-A3B
- Granite 4.2 8B vs Hy-Memory-30B-A3B
- Hy-Memory-30B-A3B vs Hy3
- Hy-Memory-30B-A3B vs Inkling
- Hy-Memory-30B-A3B vs Inkling-Small
- Hy-Memory-30B-A3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Hy-Memory-30B-A3B vs Kimi K3
- Hy-Memory-30B-A3B vs Kimi K3.1 Pro
- Hy-Memory-30B-A3B vs Kolibri-1
- Hy-Memory-30B-A3B vs Kronos
- Hy-Memory-30B-A3B vs Laguna M.1
- Hy-Memory-30B-A3B vs Laguna S 2.1
- Hy-Memory-30B-A3B vs Laguna XS 2.1
- Hy-Memory-30B-A3B vs LBS-27B
- Hy-Memory-30B-A3B vs LFM2.5-2.6B
- Hy-Memory-30B-A3B vs LFM3-1.7B
- Hy-Memory-30B-A3B vs LFM3-Audio-2B
- Hy-Memory-30B-A3B vs LFM3-Omni-2B
- Hy-Memory-30B-A3B vs LFM3-VL-2B
- Hy-Memory-30B-A3B vs Ling-3.0-tiny
- Hy-Memory-30B-A3B vs Ling-3.1-flash-1019
- Hy-Memory-30B-A3B vs MiMo-V2.6 Distill Qwen 9B
- Hy-Memory-30B-A3B vs MiniCPM-o 4.5
- Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B
- Hy-Memory-30B-A3B vs MiniCPM-V 4.7 35B-A3B
- Hy-Memory-30B-A3B vs MiniCPM5-2B
- Hy-Memory-30B-A3B vs MiniMax-M2.5
- Hy-Memory-30B-A3B vs MiniMax-M2.7
- Hy-Memory-30B-A3B vs MiniMax-M3.1-Flash
- Hy-Memory-30B-A3B vs Mistral Large 4
- Hy-Memory-30B-A3B vs Muse Glimmer 30B
- Hy-Memory-30B-A3B vs Nanbeige4.3-33B
- Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B
- Hy-Memory-30B-A3B vs Nemotron 3 Nano 30B A3B
- Hy-Memory-30B-A3B vs Nemotron 3 Nano 4B
- Hy-Memory-30B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Hy-Memory-30B-A3B vs Nemotron 3 Super 120B A12B
- Hy-Memory-30B-A3B vs Nemotron 3 Ultra 550B A55B
- Hy-Memory-30B-A3B vs occamy-1.0
- Hy-Memory-30B-A3B vs Olmo 3 7B Think
- Hy-Memory-30B-A3B vs Ornith-1.5-122B
- Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B
- Hy-Memory-30B-A3B vs Ornith-1.5-397B
- Hy-Memory-30B-A3B vs Ornith-1.5-9B
- Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B
- Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507
- Hy-Memory-30B-A3B vs Qwen3-4B-Instruct-2507
- Hy-Memory-30B-A3B vs Qwen3.5-0.8B
- Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B
- Hy-Memory-30B-A3B vs Qwen3.5-2B
- Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B
- Hy-Memory-30B-A3B vs Qwen3.5-4B
- Hy-Memory-30B-A3B vs Qwen3.5-9B
- Hy-Memory-30B-A3B vs Qwen3.6-27B
- Hy-Memory-30B-A3B vs Qwen3.6-35B-A3B
- Hy-Memory-30B-A3B vs Qwen3.8-27B
- Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next
- Hy-Memory-30B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-Coder-Next
- Hy-Memory-30B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-VL-2B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct
- Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct
- Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2
- Hy-Memory-30B-A3B vs Recursive Flint
- Hy-Memory-30B-A3B vs Ripple-65B Preview
- Hy-Memory-30B-A3B vs SmolLM3-3B
- Hy-Memory-30B-A3B vs Spark-X2.6-4B
- Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B
- Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)
- Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/gemma-4-26b-a4b-it-vs-hy-memory-30b-a3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-26b-a4b-it.json·/api/v1/tools/hy-memory-30b-a3b.json - From a terminal
anchor compare gemma-4-26b-a4b-it hy-memory-30b-a3b(the CLI) - Over MCP
compare_tools {"a": "gemma-4-26b-a4b-it", "b": "hy-memory-30b-a3b"}at/mcp, no key