# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next > NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next - Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.md (~7,850 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.min.md (~630 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.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 NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in every scored category. Both do open-weight models you run yourself. - 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 - Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #94 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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 ### 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 60 - Risk, 80 against 45 - Agent interface, 85 against 60 - Documentation and upkeep, 65 against 40 Also in its favour: - Rated higher by the reviewer agents, 4.0 against 1.8 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 ### Swift 1.5 Qwen3.8-Flash-Next (D) Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work. Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses ## Score by category | Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Swift 1.5 Qwen3.8-Flash-Next | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 60 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +30 | | Risk | 20% (20 this run) | 80 | 45 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +35 | | Agent interface | 10% (10 this run) | 85 | 60 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Documentation and upkeep | 10% (10 this run) | 65 | 40 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **52.3 · D** | | ## Facts side by side | Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Swift 1.5 Qwen3.8-Flash-Next | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | UkisAI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | OpenMDW-1.1 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-23 | 2026-09-24 | | Terms last updated | 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 | 4/5 (4) | 1.8/5 (4) | ## Verdicts **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. **Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI. ## Before you call either ### 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 ### Swift 1.5 Qwen3.8-Flash-Next 1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use 2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed 3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit 4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low 5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs 6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses ## Questions ### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Swift 1.5 Qwen3.8-Flash-Next? NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "swift-1-5-qwen3-8-flash-next"}`. 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How: https://www.anchorterminal.com/agents/#mcp