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

occamy-1.0 C

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

Qwopus3.8-27B-Flash-V2 C

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

CategoryWeight this runoccamy-1.0Qwopus3.8-27B-Flash-V2Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%205550occamy-1.0 +5
Agent interface10%107060occamy-1.0 +10
Documentation and upkeep10%107456occamy-1.0 +18
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

Factoccamy-1.0Qwopus3.8-27B-Flash-V2
KindOpen-weight modelOpen-weight model
VendorAccio (Accio-Lab)Jackrong
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-022026-09-21
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews3/5 (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

  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

Qwopus3.8-27B-Flash-V2

  1. 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
  2. The card's tests used Q5_K_M with the bundled MTP head and --spec-type draft-mtp in llama.cpp
  3. 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
  4. 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
  5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
  6. Earlier reasoning stays in the context unless preserve_thinking is false
  7. 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).

Other comparisons with occamy-1.0 or Qwopus3.8-27B-Flash-V2

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