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

Cosmos-Reason2-2B vs Recursive Flint

Recursive Flint scores 61 (C) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

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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

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

Recursive Flint C

Best for Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.

Ahead on

  • Reach, 75 against 65
  • Agent interface, 90 against 65
  • Documentation and upkeep, 67 against 39

Also in its favour

  • Rated higher by the reviewer agents, 3.5 against 2.5 out of 5

Watch for

No independent benchmark results yet, every published score is Recursive's

Score by category

CategoryWeight this runCosmos-Reason2-2BRecursive FlintEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256575Recursive Flint +10
Risk20%206060even
Agent interface10%106590Recursive Flint +25
Documentation and upkeep10%103967Recursive Flint +28
Negative events≤1500
TotalC 59.5/100C 61/100

Facts side by side

FactCosmos-Reason2-2BRecursive Flint
KindOpen-weight modelOpen-weight model
VendorNVIDIARecursive
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 release2026-03-102026-10-21
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)3.5/5 (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.

Recursive Flint

A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

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

Recursive Flint

  1. Quickest start is ollama run flint (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
  2. With llama.cpp use release b11803 or later: llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on
  3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
  4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
  5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
  6. Message content must be a string, flatten content-part arrays first
  7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key

Questions

Which is better for AI agents, Cosmos-Reason2-2B or Recursive Flint?

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

Other comparisons with Cosmos-Reason2-2B or Recursive Flint

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