Ai2
AI2 / allenai/Molmo2-4B-tb

Molmo2-4B-tb

ToyBlocks high-level planner derived from Molmo2-4B and fine-tuned for block stacking. Ai2 released the weights on November 5. The roughly 4.5-billion-parameter checkpoint reads the goal and front camera and predicts the next step and target point.

01 / SUMMARY

The essential

ORIENTATIONBlock stacking planning

Primary use stated or inferred cautiously from official documentation.

CONTEXTOne 640 × 360 front image and a text goal

Maximum input capacity when published by the source.

EXITNot published

Documented maximum generation limit.

ACCESSHugging Face · downloadable checkpoint

Verified availability channels.

MODEL TYPERobotics · Multimodal

Primary functional family and verified specialties.

LICENSEApache 2.0

Declared terms for API access, model weights, or self-hosting.

02 / TECHNICAL SHEET

Limits and integration

API IDallenai/Molmo2-4B-tb
Model typeRobotics · Multimodal
Access modelOpen weights
LicenseApache 2.0
DeploymentSelf-hosted
Release05 NOV 2026
Knowledge cutoffNot published
EntranceText · Image
ExitPlan, discrete action and target point
Context windowOne 640 × 360 front image and a text goal
maximum outputNot published
ReasoningNot published
Published tools
Structured OutingsNot published
Batch processingNot published
Prompt cacheNot published
Fine-tuningNot published
Verified platformsHugging Face · PyTorch
BEST SUITED FOR

Study hierarchical planning and visual grounding on a tabletop. The model selects a discrete action, such as picking or rotating a block. A motion planner or policy such as ManiFlow-tb performs it. The two levels can be compared without mixing their results.

WORTH MONITORING

Uses the native Molmo2 format and ToyBlocks code. The card estimates about 20 GB of GPU memory in BF16. It is limited to tabletop blocks; it does not send motor commands or demonstrate safety for other robots. Check the weight license and base-model data terms.

03 / CAPABILITIES

What it can do

01

Orientation

Study hierarchical planning and visual grounding on a tabletop. The model selects a discrete action, such as picking or rotating a block. A motion planner or policy such as ManiFlow-tb performs it. The two levels can be compared without mixing their results.

02

Context

One 640 × 360 front image and a text goal · Not published

03

Tools and integration

Not published

04

Access

Hugging Face · downloadable checkpoint

Input modalities
TextImage
04 / PRICES

Documented cost

ConceptWorthUnit/condition
Standard inputNo published official API rateStandard API rate
Cached inputNot publishedReading reused prefixes
Cache write or storageNot publishedThe condition varies by provider
Standard outputSelf-hosted infrastructure costMay include reasoning tokens
Batch inputNot publishedAsynchronous processing
Batch outputNot publishedAsynchronous processing

No hosted commercial API has been verified for this checkpoint. Downloadable weights do not cover storage, accelerators, memory or operating costs.

i Prices change and may depend on level, region or context length. Check the source before making a decision.

05 / EVALUATIONS

How to read the results

“

On easy simulations with seen blocks, Ai2 reports 97.5% with a motion planner and 55.5% with ManiFlow-tb over 200 episodes. The physical robot with ManiFlow-tb succeeded in 6 of 10 trials. These are full systems with different executors and do not measure the planner alone.

Inferama only highlights a “best result” when the metric, test set, configuration, and date allow for an equivalent comparison. The supplier's figures are presented as claims from its own source.

06 / VERSIONS

Chronology

Molmo2-4B-tb

Version added to Inferama's verified catalog.

07 / SOURCES

Traceability