# Apertus v1.5 8B vs Nemotron 3 Super 120B A12B > Nemotron 3 Super 120B A12B scores 66.2 (B) to Apertus v1.5 8B'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/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b - Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md (~6,700 tokens) - Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.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: 2026-10-21 Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 2 of 4 scored categories. Apertus v1.5 8B leads on risk and documentation and upkeep. Both do open-weight models you run yourself. - Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json - Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Apertus v1.5 8B (C) Good for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM. Ahead on: - Risk, 75 against 65 - Documentation and upkeep, 84 against 65 Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers ### Nemotron 3 Super 120B A12B (B) Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data Ahead on: - Reach, 75 against 40 - Agent interface, 90 against 85 Watch for: The card's serving commands pass --trust-remote-code ## Score by category | Category | Weight | Apertus v1.5 8B | Nemotron 3 Super 120B A12B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 75 | Nemotron 3 Super 120B A12B +35 | | Risk | 20% (20 this run) | 75 | 65 | Apertus v1.5 8B +10 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Super 120B A12B +5 | | Documentation and upkeep | 10% (10 this run) | 84 | 65 | Apertus v1.5 8B +19 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **66.2 · B** | | ## Facts side by side | Fact | Apertus v1.5 8B | Nemotron 3 Super 120B A12B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | NVIDIA Nemotron Open Model License | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-14 | 2026-03-11 | | 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 | 2.8/5 (4) | 2.8/5 (4) | ## Verdicts **Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on. **Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results. ## Before you call either ### Apertus v1.5 8B 1. Accept the terms on the card and set HF_TOKEN before downloading 2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet 3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools 4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously 5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames 6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32 7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent ### Nemotron 3 Super 120B A12B 1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin) 2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs 3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`) 4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says 5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks 6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content 7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) ## Questions ### Which is better for AI agents, Apertus v1.5 8B or Nemotron 3 Super 120B A12B? Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 2 of 4 scored categories. Apertus v1.5 8B leads on risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "nemotron-3-super-120b-a12b"}`. 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