# DeepSeek V4.1 Flash vs Nanbeige4.2-3B > DeepSeek V4.1 Flash and Nanbeige4.2-3B score within a point of each other for open-weight models you run yourself, 61.5 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.md (~10,300 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 DeepSeek V4.1 Flash and Nanbeige4.2-3B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). Nanbeige4.2-3B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - DeepSeek V4.1 Flash: grade C, 61.5/100, rank #35 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json - Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### DeepSeek V4.1 Flash (C) Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine ### Nanbeige4.2-3B (C) Good for: Local agents that need tool calling and a long context on a laptop or a small GPU Ahead on: - Reach, 85 against 60 - Risk, 55 against 50 - Agent interface, 85 against 75 - Documentation and upkeep, 72 against 48 Watch for: Its own architecture: Transformers loads it only with the repository's code ## Why the edge ### Reach: Nanbeige4.2-3B +25 - DeepSeek V4.1 Flash (60): Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one at 4-bit or better. Four runtimes run it in a released version (5 each), vLLM 0.33.0, SGLang 0.5.23, which maps the V4.1 architecture name onto its V4 model, llama.cpp from b11870 (25 October 2026), which added the deepseek41 architecture and its converter, and Ollama 0.40.14, which builds llama.cpp b11963. Transformers 5.22.1 and LM Studio don't (0 each). The release itself is one quantised build, FP8 with FP4 experts (10). MIT with nothing added earns the full terms line (15), the repositories aren't gated (10), and Hugging Face maps three live inference providers for the update (5). 0 + 20 + 10 + 15 + 10 + 5 = 60. - Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85. ### Documentation and upkeep: Nanbeige4.2-3B +24 - DeepSeek V4.1 Flash (48): The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description with no cut-off, which caps it at 5. The files changed on 16 and 17 October and DeepSeek's deepseek-recipe 0.2.0 changed the V4.1 prompt encoding on 20 October, inside 30 days (20). None of the 40 most recent questions across both repositories had a DeepSeek reply (0). Every release keeps its own repository and the update names what it replaces (10). File health loses 5 for an open, unanswered pull request saying the September template trails the encoding changes. 8 + 5 + 20 + 0 + 10 + 5 = 48. - Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72. ### Agent interface: Nanbeige4.2-3B +10 - DeepSeek V4.1 Flash (75): There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and ships its own parser in encoding.py and deepseek-recipe, but names no parser in a runtime (10, read as for DeepSeek-V3.2 and V4-Flash-0731), and one block can carry several calls (20). deepseek-recipe documents JSON Schema output via response_format (15). Thinking turns off in chat mode and takes an effort budget from 1 to 100, with reasoning returned in think tags (10). The 1M context was trained in (10). - Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85. ### Risk: Nanbeige4.2-3B +5 - DeepSeek V4.1 Flash (50): The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja template. The files sit in DeepSeek's own organisation with a hash for every file. Nothing is published on safety and there's no guidance for agent use, so those two lines score 0. The security programme earns 5 for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories. The MIT licence carries no use policy, threshold or revocation, so licence risk keeps all 15. - Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55. ## Score by category | Category | Weight | DeepSeek V4.1 Flash | Nanbeige4.2-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 85 | Nanbeige4.2-3B +25 | | Risk | 20% (20 this run) | 50 | 55 | Nanbeige4.2-3B +5 | | Agent interface | 10% (10 this run) | 75 | 85 | Nanbeige4.2-3B +10 | | Documentation and upkeep | 10% (10 this run) | 48 | 72 | Nanbeige4.2-3B +24 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61.5 · C** | **61 · C** | | ## Facts side by side | Fact | DeepSeek V4.1 Flash | Nanbeige4.2-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | Nanbeige LLM Lab (BOSS Zhipin) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-17 | 2026-09-11 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.8/5 (4) | none | ## How the scores have moved ### DeepSeek V4.1 Flash - +2.5 on 4 November 2026: product - -3.8 on 21 October 2026 (B to C): checklist ### Nanbeige4.2-3B No score change since it was listed. ## Verdicts **DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs and runs in vLLM, SGLang and llama.cpp. The main risks are prompts built from DeepSeek's code instead of a template, no published safety results and no DeepSeek replies. **Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF. ## Before you call either ### DeepSeek V4.1 Flash 1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive 2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command 3. In llama.cpp, convert with convert_hf_to_gguf.py from b11870 or later, which added the deepseek41 architecture on 25 October 2026; no GGUF is published by DeepSeek 4. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding 5. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4 6. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75 7. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends 8. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update ### Nanbeige4.2-3B 1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige` 2. Pass `tools` in the request. The card recommends `tool_call_format="xml"` 3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat 4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card 5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's 6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it ## Questions ### Which is better for AI agents, DeepSeek V4.1 Flash or Nanbeige4.2-3B? DeepSeek V4.1 Flash and Nanbeige4.2-3B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). Nanbeige4.2-3B leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "nanbeige-4-2-3b"}`. 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