# InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB > NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb - Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md (~8,050 tokens) - Slim: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.min.md (~580 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-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 NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in every scored category. Both do open-weight models you run yourself. - InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 51.2/100, rank #97 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.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 ### InternVL3.5-GPT-OSS-20B-A4B-Preview (D) Good for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview. Watch for: No quantised build from OpenGVLab, so about 51 GB at BF16 ### 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 55 - Agent interface, 85 against 45 - Documentation and upkeep, 65 against 28 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 ## Score by category | Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +30 | | Risk | 20% (20 this run) | 55 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Agent interface | 10% (10 this run) | 45 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 | | Documentation and upkeep | 10% (10 this run) | 28 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +37 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **51.2 · D** | **61 · C** | | ## Facts side by side | Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | NVIDIA Nemotron 3.5 Bolt E4B 4GB | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenGVLab (Shanghai AI Laboratory) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-09-08 | 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 | 1.8/5 (4) | 4/5 (4) | ## Verdicts **InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results. **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 ### InternVL3.5-GPT-OSS-20B-A4B-Preview 1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM 2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative 3. Use Transformers 4.55.0 or later, as the card requires for this size 4. For thinking, send the card's thinking system prompt and parse the reasoning out of the tags, with temperature 0.6 and sampling on as the card advises 5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says ### 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, InternVL3.5-GPT-OSS-20B-A4B-Preview or NVIDIA Nemotron 3.5 Bolt E4B 4GB? NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "nemotron-3-5-bolt-e4b-4gb"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview nemotron-3-5-bolt-e4b-4gb` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json ## Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or NVIDIA Nemotron 3.5 Bolt E4B 4GB - [Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.md) - 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How: https://www.anchorterminal.com/agents/#mcp