# occamy-1.0 vs ThinkingCap (Qwen3.8 27B)

> occamy-1.0 and ThinkingCap 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/occamy-1-0-vs-thinkingcap-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/occamy-1-0-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

occamy-1.0 and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.

- occamy-1.0: grade C, 61/100, rank #51 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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

### occamy-1.0 (C)

Good for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.

Ahead on:
- Reach, 85 against 75
- Risk, 55 against 45
- Documentation and upkeep, 74 against 53

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for: No safety results, prompt-injection guidance or security contact

### 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 | occamy-1.0 | ThinkingCap (Qwen3.8 27B) | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 85 | 75 | occamy-1.0 +10 |
| Risk | 20% (20 this run) | 55 | 45 | occamy-1.0 +10 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 74 | 53 | occamy-1.0 +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | occamy-1.0 | ThinkingCap (Qwen3.8 27B) |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Accio (Accio-Lab) | 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 | 2026-10-02 | 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 | 3/5 (4) | 2.5/5 (4) |

## Verdicts

**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.

**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

### occamy-1.0

1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later
2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head

### 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, occamy-1.0 or ThinkingCap (Qwen3.8 27B)?

occamy-1.0 and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "occamy-1-0", "b": "thinkingcap-qwen3-8-27b"}`. From a terminal: `anchor compare occamy-1-0 thinkingcap-qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json and https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json

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- [occamy-1.0 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.md)
- [occamy-1.0 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.md)
- [occamy-1.0 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.md)
- [occamy-1.0 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.md)
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- [occamy-1.0 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.md)
- [occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.md)
- [occamy-1.0 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.md)
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- [occamy-1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md)
- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)
- [occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.md)
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- [Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.md)
- [Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.md)
- [Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.md)
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