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

Nemotron 3 Ultra 550B A55B vs occamy-1.0

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 2 of 4 scored categories. occamy-1.0 leads on reach and documentation and upkeep. 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

Nemotron 3 Ultra 550B A55B B

Best for Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs

Ahead on

  • Risk, 70 against 55
  • Agent interface, 90 against 70

Watch for

The smallest build is 352 GB and needs at least four B200-class GPUs

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 55
  • Documentation and upkeep, 74 against 65

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

CategoryWeight this runNemotron 3 Ultra 550B A55Boccamy-1.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255585occamy-1.0 +30
Risk20%207055Nemotron 3 Ultra 550B A55B +15
Agent interface10%109070Nemotron 3 Ultra 550B A55B +20
Documentation and upkeep10%106574occamy-1.0 +9
Negative events≤1500
TotalB 68.8/100C 61/100

Facts side by side

FactNemotron 3 Ultra 550B A55Boccamy-1.0
KindOpen-weight modelOpen-weight model
VendorNVIDIAAccio (Accio-Lab)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceOpenMDW-1.1Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-06-042026-10-02
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 reviews2.8/5 (4)3/5 (4)

Verdicts

Nemotron 3 Ultra 550B A55B

NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.

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

Nemotron 3 Ultra 550B A55B

  1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead
  2. With vLLM serve the NVFP4 build with --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder on 4 × B200 or more
  3. With SGLang use --reasoning-parser nemotron_3, not the BF16 card's nemotron_v3, which SGLang doesn't register
  4. On SGLang with tools, pass chat_template_kwargs with enable_thinking true and force_nonempty_content true, as the card says, or reasoning and calls won't both parse
  5. Thinking is on by default. Pass enable_thinking: false for short tasks or medium_effort: true to cut reasoning tokens
  6. Use temperature=1.0 and top_p=0.95, as the generation config sets

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, Nemotron 3 Ultra 550B A55B or occamy-1.0?

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 2 of 4 scored categories. occamy-1.0 leads on reach and documentation and upkeep.

Other comparisons with Nemotron 3 Ultra 550B A55B or occamy-1.0

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