# Carbon-X vs Nanbeige4.2-3B > Carbon-X and Nanbeige4.2-3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b - Markdown: https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.md (~9,000 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 Carbon-X and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Nanbeige4.2-3B leads on agent interface. Both do open-weight models you run yourself. - Carbon-X: grade C, 61/100, rank #42 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/carbon-x.md · JSON https://www.anchorterminal.com/api/v1/tools/carbon-x.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 ### Carbon-X (C) Good for: Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model. Ahead on: - Reach, 90 against 85 - Risk, 65 against 55 Watch for: Not clinically validated and no wet-lab validation reported ### 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: - Agent interface, 85 against 35 Watch for: Its own architecture: Transformers loads it only with the repository's code ## Why the edge ### Agent interface: Nanbeige4.2-3B +50 - Carbon-X (35): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the inherited template shows the format and no parser is named. Trained tool calling 0, nothing says it was trained to call tools. Structured output 0, not documented. Reasoning control 0, thinking can be switched off but was never trained, and the card asks you to keep it off. Context 5, 40,960 tokens from the base, though Hugging Face trained at 8,192 and under 16K. Most of what the card documents for agents is Carbon-X as a tool another model calls, which this checklist doesn't read. - 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: Carbon-X +10 - Carbon-X (65): File safety 15, safetensors and GGUF only, but the documented loading path runs the repository's tokenizer.py through trust_remote_code. Provenance 15, the files are in HuggingFaceBio, which Hugging Face's GitHub repository links, with a checksum on every file. Safety evaluation 10, Hugging Face reports its own dual-use check on sequence editing in the launch post, without numbers or a harness. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, Hugging Face's security.txt is a reporting route, and public advisories couldn't be checked. - 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. ### Reach: Carbon-X +5 - Carbon-X (90): Memory 30, the publisher's Q4_K_M GGUF is 9.6 GB, or 11.6 GB with a fifth added. Runtimes 20, released llama.cpp (from b11742, which added the DNA tokenizer), vLLM, SGLang and Transformers all run it, with no Ollama or LM Studio entry. Builds 15, Hugging Face publishes GGUFs at four precisions. Terms 15, Apache 2.0 on the model and on its Qwen3-14B base. Download 10, nothing is gated. Hosted 0, no inference provider or Hugging Face API serves it. - 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. ## Score by category | Category | Weight | Carbon-X | Nanbeige4.2-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 85 | Carbon-X +5 | | Risk | 20% (20 this run) | 65 | 55 | Carbon-X +10 | | Agent interface | 10% (10 this run) | 35 | 85 | Nanbeige4.2-3B +50 | | Documentation and upkeep | 10% (10 this run) | 72 | 72 | even | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Carbon-X | Nanbeige4.2-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Hugging Face (HuggingFaceBio) | Nanbeige LLM Lab (BOSS Zhipin) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-28 | 2026-09-11 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 3/5 (4) | none | ## How the scores have moved ### Carbon-X - no change on 3 November 2026: new evidence ### Nanbeige4.2-3B No score change since it was listed. ## Verdicts **Carbon-X.** A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code. **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 ### Carbon-X 1. Quickest start is `llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{"enable_thinking": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0`, on llama.cpp b11742 or later 2. With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load 3. Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used 4. Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences 5. Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token 6. Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text 7. Cap max_tokens for next-base questions, long DNA generation is slow and degrades ### 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, Carbon-X or Nanbeige4.2-3B? Carbon-X and Nanbeige4.2-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Nanbeige4.2-3B leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "carbon-x", "b": "nanbeige-4-2-3b"}`. From a terminal: `anchor compare carbon-x nanbeige-4-2-3b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json ## Other comparisons with Carbon-X or Nanbeige4.2-3B - [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md) - [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.md) - [Apertus v1.5 8B vs Carbon-X](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.md) - [Apertus v1.5 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.md) - [Apodex-1.1-mini vs Carbon-X](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x.md) - [Apodex-1.1-mini vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.md) - [Arete-27B vs Carbon-X](https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.md) - 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