# Carbon-X vs Gemma 4 26B A4B IT

> Gemma 4 26B A4B IT scores 72.2 (BB) 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-gemma-4-26b-a4b-it
- Markdown: https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.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

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Carbon-X's 61 (C), and leads in 2 of 4 scored categories. Carbon-X leads on 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
- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.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:
- Documentation and upkeep, 77 against 65

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

### Gemma 4 26B A4B IT (BB)

Good for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on:
- Risk, 70 against 65
- Agent interface, 85 against 35

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 3.0 out of 5

Watch for: No safety evaluation figures published


## Score by category

| Category | Weight | Carbon-X | Gemma 4 26B A4B IT | 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 | 70 | Gemma 4 26B A4B IT +5 |
| Agent interface | 10% (10 this run) | 35 | 85 | Gemma 4 26B A4B IT +50 |
| Documentation and upkeep | 10% (10 this run) | 77 | 65 | Carbon-X +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **72.2 · BB** | |

## Facts side by side

| Fact | Carbon-X | Gemma 4 26B A4B IT |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Hugging Face (HuggingFaceBio) | Google DeepMind |
| 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 | 2026-10-22 | 2026-07-20 |
| 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) | 3.8/5 (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.

**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

## 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

### Gemma 4 26B A4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images
2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K
3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text
4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
5. Validate tool names and arguments before running them, and check any JSON yourself

## Questions

### Which is better for AI agents, Carbon-X or Gemma 4 26B A4B IT?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Carbon-X's 61 (C), and leads in 2 of 4 scored categories. Carbon-X leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "carbon-x", "b": "gemma-4-26b-a4b-it"}`. From a terminal: `anchor compare carbon-x gemma-4-26b-a4b-it`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json

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