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

occamy-1.0 vs Olmo 3 7B Think

occamy-1.0 scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. 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

  • Reach, 85 against 75
  • Agent interface, 70 against 35
  • Documentation and upkeep, 74 against 52

Watch for

No safety results, prompt-injection guidance or security contact

Olmo 3 7B Think D

Best for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand

Ahead on

  • Risk, 75 against 55

Watch for

Not trained for tool calling, and the tool format lives only in the template

Score by category

CategoryWeight this runoccamy-1.0Olmo 3 7B ThinkEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258575occamy-1.0 +10
Risk20%205575Olmo 3 7B Think +20
Agent interface10%107035occamy-1.0 +35
Documentation and upkeep10%107452occamy-1.0 +22
Negative events≤1500
TotalC 61/100D 52.3/100

Facts side by side

Factoccamy-1.0Olmo 3 7B Think
KindOpen-weight modelOpen-weight model
VendorAccio (Accio-Lab)Ai2 (Allen Institute for AI)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0, with Ai2's Responsible Use Guidelines named on the card
Read-only variant documentednono
llms.txtnono
Last release2026-10-022026-06-25
Terms last updatedno document linked
Privacy policy last updatedno 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)2.8/5 (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.

Olmo 3 7B Think

A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

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

Olmo 3 7B Think

  1. Quickest start is ollama run olmo-3:7b-think, about 4.5 GB
  2. With vLLM, vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend
  3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
  4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
  5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
  6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
  7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
  8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

Questions

Which is better for AI agents, occamy-1.0 or Olmo 3 7B Think?

occamy-1.0 scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk.

Other comparisons with occamy-1.0 or Olmo 3 7B Think

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