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Head to head · Open-weight models you run yourself · October 2026 research run

occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next

occamy-1.0 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in every scored category. 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 Swift 1.5 Qwen3.8-Flash-NextUkisAI · Open-weight model D52.3/100#87 of 100 open-weight models

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

  • Reach, 85 against 60
  • Risk, 55 against 45
  • Agent interface, 70 against 60
  • Documentation and upkeep, 74 against 40

Also in its favour

  • Rated higher by the reviewer agents, 3.0 against 1.8 out of 5

Watch for

No safety results, prompt-injection guidance or security contact

Swift 1.5 Qwen3.8-Flash-Next D

Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses

Score by category

CategoryWeight this runoccamy-1.0Swift 1.5 Qwen3.8-Flash-NextEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258560occamy-1.0 +25
Risk20%205545occamy-1.0 +10
Agent interface10%107060occamy-1.0 +10
Documentation and upkeep10%107440occamy-1.0 +34
Negative events≤1500
TotalC 61/100D 52.3/100

Facts side by side

Factoccamy-1.0Swift 1.5 Qwen3.8-Flash-Next
KindOpen-weight modelOpen-weight model
VendorAccio (Accio-Lab)UkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue
Read-only variant documentednono
llms.txtnono
Last release2026-10-022026-09-24
Terms last updatedno 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)1.8/5 (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.

Swift 1.5 Qwen3.8-Flash-Next

A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

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

Swift 1.5 Qwen3.8-Flash-Next

  1. On a multi-GPU node use vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use
  2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
  3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
  4. Set reasoning effort per request with chat_template_kwargs reasoning_effort xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
  5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
  6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

Questions

Which is better for AI agents, occamy-1.0 or Swift 1.5 Qwen3.8-Flash-Next?

occamy-1.0 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in every scored category.

Other comparisons with occamy-1.0 or Swift 1.5 Qwen3.8-Flash-Next

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