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

Cosmos-Reason2-2B vs Nemotron 3 Ultra 550B A55B

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B 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

Cosmos-Reason2-2B C

Best for Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces

Ahead on

  • Reach, 65 against 55

Watch for

Gated behind a click-through licence that needs a Hugging Face account

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 60
  • Agent interface, 90 against 65
  • Documentation and upkeep, 65 against 39

Watch for

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

Score by category

CategoryWeight this runCosmos-Reason2-2BNemotron 3 Ultra 550B A55BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256555Cosmos-Reason2-2B +10
Risk20%206070Nemotron 3 Ultra 550B A55B +10
Agent interface10%106590Nemotron 3 Ultra 550B A55B +25
Documentation and upkeep10%103965Nemotron 3 Ultra 550B A55B +26
Negative events≤1500
TotalC 59.5/100B 68.8/100

Facts side by side

FactCosmos-Reason2-2BNemotron 3 Ultra 550B A55B
KindOpen-weight modelOpen-weight model
VendorNVIDIANVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0OpenMDW-1.1
Read-only variant documentednono
llms.txtnono
Last release2026-03-102026-06-04
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 reviews2.5/5 (4)2.8/5 (4)

Verdicts

Cosmos-Reason2-2B

A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.

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.

Before you call either

Cosmos-Reason2-2B

  1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading
  2. Serve it with vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{"video": {"num_frames": -1}}' --reasoning-parser qwen3, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises
  3. For local video or images, make a folder for them (mkdir -p media) and pass --allowed-local-media-path "$(pwd)/media", so the server can read only that folder, not everything under the directory you start it from
  4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off
  5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply
  6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself
  7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks

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

Questions

Which is better for AI agents, Cosmos-Reason2-2B or Nemotron 3 Ultra 550B A55B?

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on reach.

Other comparisons with Cosmos-Reason2-2B or Nemotron 3 Ultra 550B A55B

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