Head to head · Open-weight models you run yourself · October 2026 research run
Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507
Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 2 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and agent interface. 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 with an external memory framework that want a local model for the write path on a 24 GB card
Ahead on
- Risk, 60 against 55
- Documentation and upkeep, 68 against 26
Watch for
Not a general assistant or a tool caller
Best for Agents that want an instruct model with tool calling on one 24 GB GPU or a 32 GB Mac, with a plain licence and a 262,144-token context.
Ahead on
- Reach, 90 against 85
- Agent interface, 75 against 65
Watch for
No safety results, prompt-injection guidance or security contact
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.
Documentation and upkeep Hy-Memory-30B-A3B +42
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.
Qwen3-30B-A3B-Instruct-2507 26
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files and card last changed on 17 September 2025 (Hub lastModified on 4 November), and QwenLM/Qwen3's commits since are documentation only (the newest 8 January 2026). Responsiveness 0, counted by script on 4 November, with 32 discussions, 23 not pull requests (the newest 18 June 2026), and the only reply from a Qwen organisation member is Haihao's on discussion 3 (30 July 2025), who isn't counted as the publisher's staff (the ehartford and danielhanchen reading). Version policy 5, the older Qwen3-30B-A3B stays in its own repository without a note. File health 5, the files agree with the card but reports about the 1M mode wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26.
Agent interface Qwen3-30B-A3B-Instruct-2507 +10
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.
Qwen3-30B-A3B-Instruct-2507 75
Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native, with the 1M mode not counted. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.
Reach Qwen3-30B-A3B-Instruct-2507 +5
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.
Qwen3-30B-A3B-Instruct-2507 90
Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, and Ollama's library and LM Studio's catalogue carry it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, Featherless AI on Hugging Face plus DekaLLM and SiliconFlow on OpenRouter serve it. On 4 November both repositories were still ungated with no file changed since 17 September 2025, and Featherless AI was still live in the Hub's provider mapping (OpenRouter's providers per 21 October). 25 + 25 + 10 + 15 + 10 + 5 = 90.
Risk Hy-Memory-30B-A3B +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.
Qwen3-30B-A3B-Instruct-2507 55
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, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 whose only change was the copyright year before release. 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. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | Hy-Memory-30B-A3B | Qwen3-30B-A3B-Instruct-2507 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 90 | Qwen3-30B-A3B-Instruct-2507 +5 |
| Risk | 20%20 | 60 | 55 | Hy-Memory-30B-A3B +5 |
| Agent interface | 10%10 | 65 | 75 | Qwen3-30B-A3B-Instruct-2507 +10 |
| Documentation and upkeep | 10%10 | 68 | 26 | Hy-Memory-30B-A3B +42 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.6/100 |
Facts side by side
| Fact | Hy-Memory-30B-A3B | Qwen3-30B-A3B-Instruct-2507 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy 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 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-16 | 2025-09-17 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (4) |
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.
Qwen3-30B-A3B-Instruct-2507
A general-purpose instruct model with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and providers host it. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published.
Before you call either
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
Qwen3-30B-A3B-Instruct-2507
- Quickest start is
ollama run qwen3:30b-instruct, 19 GB. Plainqwen3:30bandqwen3:30b-a3bare the Thinking-2507 edition, not this one - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes, 61 GB of weights fits the GPU share - Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
- Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
- Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
- Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, Hy-Memory-30B-A3B or Qwen3-30B-A3B-Instruct-2507?
Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 2 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and agent interface.
Other comparisons with Hy-Memory-30B-A3B or Qwen3-30B-A3B-Instruct-2507
- Agents-A1-4B vs Hy-Memory-30B-A3B
- Agents-A1-4B vs Qwen3-30B-A3B-Instruct-2507
- Apertus v1.5 8B vs Hy-Memory-30B-A3B
- Apertus v1.5 8B vs Qwen3-30B-A3B-Instruct-2507
- Apodex-1.1-mini vs Hy-Memory-30B-A3B
- Apodex-1.1-mini vs Qwen3-30B-A3B-Instruct-2507
- Arete-27B vs Hy-Memory-30B-A3B
- Arete-27B vs Qwen3-30B-A3B-Instruct-2507
- BigBang-v1 vs Hy-Memory-30B-A3B
- BigBang-v1 vs Qwen3-30B-A3B-Instruct-2507
- Carbon-X vs Hy-Memory-30B-A3B
- Carbon-X vs Qwen3-30B-A3B-Instruct-2507
- Cosmos-Reason2-2B vs Hy-Memory-30B-A3B
- Cosmos-Reason2-2B vs Qwen3-30B-A3B-Instruct-2507
- Darwin-36B-Opus vs Hy-Memory-30B-A3B
- Darwin-36B-Opus vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V3.2 vs Hy-Memory-30B-A3B
- DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B
- DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V4-Flash-0731 vs Hy-Memory-30B-A3B
- DeepSeek-V4-Flash-0731 vs Qwen3-30B-A3B-Instruct-2507
- DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B
- DiffusionGemma 26B A4B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 12B IT vs Hy-Memory-30B-A3B
- Gemma 4 12B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 26B A4B IT vs Hy-Memory-30B-A3B
- Gemma 4 26B A4B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 31B IT vs Hy-Memory-30B-A3B
- Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 E2B IT vs Hy-Memory-30B-A3B
- Gemma 4 E2B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 E4B IT vs Hy-Memory-30B-A3B
- Gemma 4 E4B IT vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.5-Air vs Hy-Memory-30B-A3B
- GLM-4.5-Air vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.7-Flash vs Hy-Memory-30B-A3B
- GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507
- GLM-5.3-Flash vs Hy-Memory-30B-A3B
- GLM-5.3-Flash vs Qwen3-30B-A3B-Instruct-2507
- GLM-5.3 vs Hy-Memory-30B-A3B
- GLM-5.3 vs Qwen3-30B-A3B-Instruct-2507
- gpt-oss-120b vs Hy-Memory-30B-A3B
- gpt-oss-120b vs Qwen3-30B-A3B-Instruct-2507
- gpt-oss-20b vs Hy-Memory-30B-A3B
- gpt-oss-20b vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 3B vs Hy-Memory-30B-A3B
- Granite 4.2 3B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 8B vs Hy-Memory-30B-A3B
- Granite 4.2 8B vs Qwen3-30B-A3B-Instruct-2507
- 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 Qwen3-4B-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
- Hy3 vs Qwen3-30B-A3B-Instruct-2507
- Inkling-Small vs Qwen3-30B-A3B-Instruct-2507
- Inkling vs Qwen3-30B-A3B-Instruct-2507
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-30B-A3B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507
- Kimi K3 vs Qwen3-30B-A3B-Instruct-2507
- Kolibri-1 vs Qwen3-30B-A3B-Instruct-2507
- Kronos vs Qwen3-30B-A3B-Instruct-2507
- Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna XS 2.1 vs Qwen3-30B-A3B-Instruct-2507
- LBS-27B vs Qwen3-30B-A3B-Instruct-2507
- LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-Audio-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-Omni-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-VL-2B vs Qwen3-30B-A3B-Instruct-2507
- Ling-3.0-tiny vs Qwen3-30B-A3B-Instruct-2507
- Ling-3.1-flash-1019 vs Qwen3-30B-A3B-Instruct-2507
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-V 4.7 1B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-V 4.7 35B-A3B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM5-2B vs Qwen3-30B-A3B-Instruct-2507
- MiniMax-M2.5 vs Qwen3-30B-A3B-Instruct-2507
- MiniMax-M2.7 vs Qwen3-30B-A3B-Instruct-2507
- MiniMax-M3.1-Flash vs Qwen3-30B-A3B-Instruct-2507
- Mistral Large 4 vs Qwen3-30B-A3B-Instruct-2507
- Muse Glimmer 30B vs Qwen3-30B-A3B-Instruct-2507
- Nanbeige4.3-33B vs Qwen3-30B-A3B-Instruct-2507
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Nano 30B A3B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Nano 4B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Super 120B A12B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Ultra 550B A55B vs Qwen3-30B-A3B-Instruct-2507
- occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507
- Olmo 3 7B Think vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-35B-A3B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507
- Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-4B-Instruct-2507
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-0.8B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-122B-A10B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-2B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-397B-A17B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-4B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-9B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-35B-A3B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-27B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-Flash-Next
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-Next
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-2B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-32B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-4B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-8B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2
- Qwen3-30B-A3B-Instruct-2507 vs Recursive Flint
- Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview
- Qwen3-30B-A3B-Instruct-2507 vs SmolLM3-3B
- Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)
- Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/hy-memory-30b-a3b.json·/api/v1/tools/qwen3-30b-a3b-instruct-2507.json - From a terminal
anchor compare hy-memory-30b-a3b qwen3-30b-a3b-instruct-2507(the CLI) - Over MCP
compare_tools {"a": "hy-memory-30b-a3b", "b": "qwen3-30b-a3b-instruct-2507"}at/mcp, no key