Head to head · Open-weight models you run yourself · October 2026 research run
occamy-1.0 vs Qwopus3.8-27B-Flash-V2
occamy-1.0 and Qwopus3.8-27B-Flash-V2 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
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
- Risk, 55 against 50
- Agent interface, 70 against 60
- Documentation and upkeep, 74 against 56
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 Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Published from a personal account, with no organisation, security contact or safety evaluation
Score by category
| Category | Weight this run | occamy-1.0 | Qwopus3.8-27B-Flash-V2 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 55 | 50 | occamy-1.0 +5 |
| Agent interface | 10%10 | 70 | 60 | occamy-1.0 +10 |
| Documentation and upkeep | 10%10 | 74 | 56 | occamy-1.0 +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | occamy-1.0 | Qwopus3.8-27B-Flash-V2 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Accio (Accio-Lab) | Jackrong |
| 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 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-02 | 2026-09-21 |
| 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) | 2.5/5 (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.
Qwopus3.8-27B-Flash-V2
A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.
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
Qwopus3.8-27B-Flash-V2
- Quickest start is
llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja, about 17 GB, and add mmproj-F32.gguf for images - The card's tests used Q5_K_M with the bundled MTP head and
--spec-type draft-mtpin llama.cpp - The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
- For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
- reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
- Earlier reasoning stays in the context unless preserve_thinking is false
- Cap max_tokens, since a report says it loops more than V1
Questions
Which is better for AI agents, occamy-1.0 or Qwopus3.8-27B-Flash-V2?
occamy-1.0 and Qwopus3.8-27B-Flash-V2 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-qwopus3-8-27b-flash-v2.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/occamy-1-0.json·/api/v1/tools/qwopus3-8-27b-flash-v2.json - From a terminal
anchor compare occamy-1-0 qwopus3-8-27b-flash-v2(the CLI) - Over MCP
compare_tools {"a": "occamy-1-0", "b": "qwopus3-8-27b-flash-v2"}at/mcp, no key