# Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB > Hy3 scores 62.6 (B) to NVIDIA Nemotron 3.5 Bolt E4B 4GB'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/hy3-vs-nemotron-3-5-bolt-e4b-4gb - Markdown: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.md (~6,800 tokens) - Slim: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-25 Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - Hy3: grade B, 62.6/100, rank #28 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.json - NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #45 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Hy3 (B) Good for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0 Watch for: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build ### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C) Good for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters. Ahead on: - Reach, 90 against 70 - Risk, 80 against 60 - Agent interface, 85 against 75 - Documentation and upkeep, 65 against 56 Also in its favour: - Rated higher by the reviewer agents, 4.0 against 2.8 out of 5 Watch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway ## Score by category | Category | Weight | Hy3 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 70 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 | | Risk | 20% (20 this run) | 60 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 | | Agent interface | 10% (10 this run) | 75 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 | | Documentation and upkeep | 10% (10 this run) | 56 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +9 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.6 · B** | **61 · C** | | ## Facts side by side | Fact | Hy3 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Tencent (Hy team) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-20 | 2026-10-23 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 2.8/5 (4) | 4/5 (4) | ## Verdicts **Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July. **NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results. ## Before you call either ### Hy3 1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3 2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows 3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error 4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content 5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe 6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling 7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers 8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers ### NVIDIA Nemotron 3.5 Bolt E4B 4GB 1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K 2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget ` to cap thinking 3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja 4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do 5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says 6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt 7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says 8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines ## Questions ### Which is better for AI agents, Hy3 or NVIDIA Nemotron 3.5 Bolt E4B 4GB? Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy3", "b": "nemotron-3-5-bolt-e4b-4gb"}`. From a terminal: `anchor compare hy3 nemotron-3-5-bolt-e4b-4gb` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json ## Other comparisons with Hy3 or NVIDIA Nemotron 3.5 Bolt E4B 4GB - [Agents-A1-4B vs Hy3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.md) - [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.md) - [Apertus v1.5 8B vs Hy3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.md) - [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.md) - [Apodex-1.1-mini vs Hy3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.md) - [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md) - [BigBang-v1 vs Hy3](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.md) - 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