Head to head · Open-weight models you run yourself · October 2026 research run
occamy-1.0 vs 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). Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
occamy-1.0Accio (Accio-Lab) · Open-weight model
C61/100#51 of 100 open-weight models
ThinkingCap (Qwen3.8 27B)BottleCap AI · Open-weight model
C61/100#50 of 100 open-weight models
Which one, for what
Best 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
Best 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.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue
Score by category
| Category | Weight this run | occamy-1.0 | ThinkingCap (Qwen3.8 27B) | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 75 | occamy-1.0 +10 |
| Risk | 20%20 | 55 | 45 | occamy-1.0 +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 74 | 53 | occamy-1.0 +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
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
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- 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)
- Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
- For files without the gate use
llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB - On vLLM use
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang--tool-call-parser qwen3_coder - Set
reasoning_effort(xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error - Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
- 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).
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Machine-readable
- This page as Markdown
/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/occamy-1-0.json·/api/v1/tools/thinkingcap-qwen3-8-27b.json - From a terminal
anchor compare occamy-1-0 thinkingcap-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "occamy-1-0", "b": "thinkingcap-qwen3-8-27b"}at/mcp, no key