# Carbon-X vs GLM-4.7-Flash

> GLM-4.7-Flash scores 63 (B) to Carbon-X'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/carbon-x-vs-glm-4-7-flash
- Markdown: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.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: 2026-10-23

GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- Carbon-X: grade C, 61/100, rank #38 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/carbon-x.md · JSON https://www.anchorterminal.com/api/v1/tools/carbon-x.json
- GLM-4.7-Flash: grade B, 63/100, rank #27 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3023 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 80
- Risk, 65 against 55
- Documentation and upkeep, 77 against 28

Watch for: Not clinically validated and no wet-lab validation reported

### GLM-4.7-Flash (B)

Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime

Ahead on:
- Agent interface, 85 against 35

Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January


## Score by category

| Category | Weight | Carbon-X | GLM-4.7-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 80 | Carbon-X +10 |
| Risk | 20% (20 this run) | 65 | 55 | Carbon-X +10 |
| Agent interface | 10% (10 this run) | 35 | 85 | GLM-4.7-Flash +50 |
| Documentation and upkeep | 10% (10 this run) | 77 | 28 | Carbon-X +49 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **63 · B** | |

## Facts side by side

| Fact | Carbon-X | GLM-4.7-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Hugging Face (HuggingFaceBio) | Z.ai (Zhipu AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-22 | 2026-01-29 |
| 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 | 3/5 (4) | 2.8/5 (4) |

## 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, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.

**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.

## 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

### GLM-4.7-Flash

1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`
2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build
3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default
6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

## Questions

### Which is better for AI agents, Carbon-X or GLM-4.7-Flash?

GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "carbon-x", "b": "glm-4-7-flash"}`. From a terminal: `anchor compare carbon-x glm-4-7-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json

## Other comparisons with Carbon-X or GLM-4.7-Flash

- [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md)
- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.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 GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.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 GLM-4.7-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.md)
- [BigBang-v1 vs Carbon-X](https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x.md)
- [BigBang-v1 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.md)
- [Carbon-X vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b.md)
- [Carbon-X vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus.md)
- [Carbon-X vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2.md)
- [Carbon-X vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash.md)
- [Carbon-X vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.md)
- [Carbon-X vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.md)
- [Carbon-X vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-12b-it.md)
- [Carbon-X vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.md)
- [Carbon-X vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it.md)
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- [Carbon-X vs GLM-5.3](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3.md)
- [Carbon-X vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.md)
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- [Carbon-X vs Granite 4.2 3B](https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b.md)
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