# Devstral Small 2 vs Hy-Memory-30B-A3B > Devstral Small 2 and Hy-Memory-30B-A3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b - Markdown: https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.md (~9,150 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 Devstral Small 2 and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself. - Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.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 ### Devstral Small 2 (C) Good for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac Ahead on: - Reach, 90 against 85 Watch for: No limitations, safety results, languages or data on the card ### 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: - Documentation and upkeep, 68 against 46 Watch for: Not a general assistant or a tool caller ## Why the edge ### Documentation and upkeep: Hy-Memory-30B-A3B +22 - Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46. - 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: Devstral Small 2 +5 - Devstral Small 2 (90): Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90. - 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 | Devstral Small 2 | Hy-Memory-30B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 85 | Devstral Small 2 +5 | | Risk | 20% (20 this run) | 60 | 60 | even | | Agent interface | 10% (10 this run) | 65 | 65 | even | | Documentation and upkeep | 10% (10 this run) | 46 | 68 | Hy-Memory-30B-A3B +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Devstral Small 2 | Hy-Memory-30B-A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Mistral AI | 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 | 2026-07-15 | 2026-10-16 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | ## Verdicts **Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data. **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 ### Devstral Small 2 1. `ollama run devstral-small-2` is the quickest start, at 15 GB 2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice` 3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it 4. Use a low temperature. The repository's default is 0.15 5. Plan for one tool call per turn 6. The same model is on our Mistral API listing if you'd rather not host it ### 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, Devstral Small 2 or Hy-Memory-30B-A3B? Devstral Small 2 and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "devstral-small-2", "b": "hy-memory-30b-a3b"}`. 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[Hy-Memory-30B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.md) - [Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.md) - [Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.md) - [Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.md) - [Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.md) - [Hy-Memory-30B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.md) - [Hy-Memory-30B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.md) - [Hy-Memory-30B-A3B vs ZAYA1-8B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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