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

Cosmos-Reason2-2B vs Darwin-36B-Opus

Darwin-36B-Opus scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B 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

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

  • Risk, 60 against 55

Watch for

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

Darwin-36B-Opus C

Best for Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required

Ahead on

  • Reach, 80 against 65
  • Agent interface, 70 against 65
  • Documentation and upkeep, 51 against 39

Watch for

The merge method is proprietary and the current weights include an undescribed targeted refinement

Score by category

CategoryWeight this runCosmos-Reason2-2BDarwin-36B-OpusEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256580Darwin-36B-Opus +15
Risk20%206055Cosmos-Reason2-2B +5
Agent interface10%106570Darwin-36B-Opus +5
Documentation and upkeep10%103951Darwin-36B-Opus +12
Negative events≤1500
TotalC 59.5/100C 61/100

Facts side by side

FactCosmos-Reason2-2BDarwin-36B-Opus
KindOpen-weight modelOpen-weight model
VendorNVIDIAFINAL-Bench (VIDRAFT)
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 2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-03-102026-04-23
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)2.5/5 (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.

Darwin-36B-Opus

A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no 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

Darwin-36B-Opus

  1. Quickest start is llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files
  2. On the reference machine BF16 fits, vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parsers for Qwen3.6's format
  3. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
  5. generation_config.json stops only on the end-of-text token, so add <|im_end|> as a stop token if a runtime keeps generating after the turn ends
  6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
  7. Validate JSON output yourself, since structured output isn't documented

Questions

Which is better for AI agents, Cosmos-Reason2-2B or Darwin-36B-Opus?

Darwin-36B-Opus scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Cosmos-Reason2-2B leads on risk.

Other comparisons with Cosmos-Reason2-2B or Darwin-36B-Opus

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.