# Carbon-X vs MiniCPM-V 4.7 35B-A3B > Carbon-X and MiniCPM-V 4.7 35B-A3B 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-minicpm-v-4-7-35b-a3b - Markdown: https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.md (~8,800 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 MiniCPM-V 4.7 35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 35B-A3B 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 - MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.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: - Risk, 65 against 55 Watch for: Not clinically validated and no wet-lab validation reported ### MiniCPM-V 4.7 35B-A3B (C) Good for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence Ahead on: - Agent interface, 70 against 35 Watch for: No limitations section, safety results, agent-use guidance or security contact ## Why the edge ### Agent interface: MiniCPM-V 4.7 35B-A3B +35 - 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. - MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native. ### 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. - MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md. ## Score by category | Category | Weight | Carbon-X | MiniCPM-V 4.7 35B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 65 | 55 | Carbon-X +10 | | Agent interface | 10% (10 this run) | 35 | 70 | MiniCPM-V 4.7 35B-A3B +35 | | Documentation and upkeep | 10% (10 this run) | 72 | 68 | Carbon-X +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Carbon-X | MiniCPM-V 4.7 35B-A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Hugging Face (HuggingFaceBio) | OpenBMB | | 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 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-28 | 2026-11-03 | | 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 ### MiniCPM-V 4.7 35B-A3B 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. **MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it. ## 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 ### MiniCPM-V 4.7 35B-A3B 1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b` 2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost 3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace 4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate` 5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends 6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format 7. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, Carbon-X or MiniCPM-V 4.7 35B-A3B? Carbon-X and MiniCPM-V 4.7 35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiniCPM-V 4.7 35B-A3B leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "carbon-x", "b": "minicpm-v-4-7-35b-a3b"}`. From a terminal: `anchor compare carbon-x minicpm-v-4-7-35b-a3b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json ## Other comparisons with Carbon-X or MiniCPM-V 4.7 35B-A3B - [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md) - [Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.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 MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.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 MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-35b-a3b.md) - [Arete-27B vs Carbon-X](https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.md) - 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