# Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)

> Gemma 4 26B A4B IT scores 72.2 (BB) to ThinkingCap'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/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.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: 2026-10-21

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 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
- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #50 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### 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:
- Reach, 90 against 75
- Risk, 70 against 45
- Agent interface, 85 against 70
- Documentation and upkeep, 65 against 53

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for: No safety evaluation figures published

### ThinkingCap (Qwen3.8 27B) (C)

Good for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.

Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue


## Score by category

| Category | Weight | Gemma 4 26B A4B IT | ThinkingCap (Qwen3.8 27B) | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 75 | Gemma 4 26B A4B IT +15 |
| Risk | 20% (20 this run) | 70 | 45 | Gemma 4 26B A4B IT +25 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 26B A4B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 65 | 53 | Gemma 4 26B A4B IT +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.2 · BB** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 26B A4B IT | ThinkingCap (Qwen3.8 27B) |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | BottleCap AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-20 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | 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 | 3.8/5 (4) | 2.5/5 (4) |

## Verdicts

**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.

**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.

## Before you call either

### 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

### ThinkingCap (Qwen3.8 27B)

1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB
3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`
4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error
5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor

## Questions

### Which is better for AI agents, Gemma 4 26B A4B IT or ThinkingCap (Qwen3.8 27B)?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-26b-a4b-it", "b": "thinkingcap-qwen3-8-27b"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it thinkingcap-qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json

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