# Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2 > Qwopus3.8-27B-Flash-V2 scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (C) 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-nano-4b-vs-qwopus3-8-27b-flash-v2 - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.md (~6,650 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.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 Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and agent interface. Both do open-weight models you run yourself. - Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json - Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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 ### Nemotron 3 Nano 4B (C) Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English Ahead on: - Reach, 90 against 85 - Risk, 65 against 50 - Agent interface, 70 against 60 Also in its favour: - Rated higher by the reviewer agents, 3.3 against 2.5 out of 5 Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository ### Qwopus3.8-27B-Flash-V2 (C) Good for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU. Watch for: Published from a personal account, with no organisation, security contact or safety evaluation ## Score by category | Category | Weight | Nemotron 3 Nano 4B | Qwopus3.8-27B-Flash-V2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 85 | Nemotron 3 Nano 4B +5 | | Risk | 20% (20 this run) | 65 | 50 | Nemotron 3 Nano 4B +15 | | Agent interface | 10% (10 this run) | 70 | 60 | Nemotron 3 Nano 4B +10 | | Documentation and upkeep | 10% (10 this run) | 55 | 56 | Qwopus3.8-27B-Flash-V2 +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.8 · C** | **61 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano 4B | Qwopus3.8-27B-Flash-V2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Jackrong | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | NVIDIA Nemotron Open Model License | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-03-16 | 2026-09-21 | | 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 | 3.3/5 (4) | 2.5/5 (4) | ## Verdicts **Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March. **Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's. ## Before you call either ### Nemotron 3 Nano 4B 1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build 2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file 3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3` 4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead 5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks 6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says 7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented ### Qwopus3.8-27B-Flash-V2 1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images 2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp 3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder 4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0 5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error 6. Earlier reasoning stays in the context unless preserve_thinking is false 7. Cap max_tokens, since a report says it loops more than V1 ## Questions ### Which is better for AI agents, Nemotron 3 Nano 4B or Qwopus3.8-27B-Flash-V2? Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-4b", "b": "qwopus3-8-27b-flash-v2"}`. 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How: https://www.anchorterminal.com/agents/#mcp