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

Apertus v1.5 8B vs occamy-1.0

Apertus v1.5 8B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach. 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

Apertus v1.5 8B C

Best for Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on

  • Risk, 75 against 55
  • Agent interface, 85 against 70
  • Documentation and upkeep, 84 against 74

Watch for

No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

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 40

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

CategoryWeight this runApertus v1.5 8Boccamy-1.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254085occamy-1.0 +45
Risk20%207555Apertus v1.5 8B +20
Agent interface10%108570Apertus v1.5 8B +15
Documentation and upkeep10%108474Apertus v1.5 8B +10
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactApertus v1.5 8Boccamy-1.0
KindOpen-weight modelOpen-weight model
VendorSwiss AI Initiative (EPFL and ETH Zurich)Accio (Accio-Lab)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gateApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-142026-10-02
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 reviews2.8/5 (4)3/5 (4)

Verdicts

Apertus v1.5 8B

A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

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.

Before you call either

Apertus v1.5 8B

  1. Accept the terms on the card and set HF_TOKEN before downloading
  2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
  3. Serve with --chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus for tools, and leave thinking off when you need tools
  4. For thinking, set --reasoning-parser apertus and --default-chat-template-kwargs.enable_thinking true, and budget max tokens generously
  5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
  6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
  7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

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

Questions

Which is better for AI agents, Apertus v1.5 8B or occamy-1.0?

Apertus v1.5 8B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach.

Other comparisons with Apertus v1.5 8B or occamy-1.0

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