Cosmos Reason 2 2B vs 8B: the size chooser, answered

Pick by hardware first, accuracy second. On the smallest Jetson class the 8B is not an option at all, and the 2B fits only in a quantized build. From a 24 GB card up to a 32 GB card or better, the 8B leads every published category total, so take it when scores matter. If you call an API instead of hosting, the market splits for you: NVIDIA's hosted tier carries the 8B only, at no charge inside request limits, and this platform carries the 2B. The license is identical on both sizes, so it breaks no ties.

Every number below names its owner. Most come from NVIDIA's own pages, read on 27 October 2026: the model cards, the code repository, the official docs, the Jetson AI Lab tutorial, and one benchmark table the publisher posted in April 2026. Where nothing was published, the cell is a dash. There are more dashes here than you may expect. Knowing what NVIDIA never printed is half the choice.

What people actually ask

Two demand signals shape this query. First, search completions. The strings "cosmos reason 2 8b", "cosmos reason 2 2b", "cosmos reason2 8b", and "cosmos reason2 2b" all completed live in the suggest box, read on 26 October 2026. So did "cosmos reason 2.5", a version string with no product behind it yet. Second, the question Google attaches to this family. The captured form reads: "Are you planning to deploy this on edge hardware (like Jetson) or run it in a cloud/server environment, and what specific video or robotic task are you working on?" The research census of 25 October 2026 recorded the same question more plainly: "How to deploy Cosmos-Reason2-2B on edge hardware or Jetson?"

Both signals ask the same thing. Which size fits my hardware, and what is each size good for. Five real questions sit under that. Does the 8B need a datacenter card. Does the 2B still reason well enough. Which size fits a Jetson board. What does each size cost to call. And is one of them about to be replaced. The rest of this page answers each in order.

The chooser in one table

Read the table as a decision aid. The last row is the one people get wrong. No size choice changes your rights. Both sizes ship under the same license terms, and both allow commercial use.

Same family, same day, same license

Cosmos Reason 2 is NVIDIA's reasoning vision-language family for physical AI. The official documentation describes an open model that understands space, time, and basic physics, and can plan what an embodied agent does next. Images and video go in. Text comes out, with boxes, points, and timestamps on request.

The two sizes arrived on the same day. The code repository news list dates both the 2B and the 8B to December 19, 2025. A third size, the 32B, followed on April 29, 2026. This page compares the first two.

Three rows deserve a comment. The parameter counts are exact, from the released file data on the model host, read 27 October 2026. The 8B carries 3.6 times the parameters of the 2B. Downloads are the market vote: the small size pulls about 6.6 times the monthly downloads of the large one. And the window row kills a common guess. The native window is the same on both sizes, up to 256K input tokens. What differs is what each host enforces. That table comes later.

One more identity fact matters. The repository banner says this code line is no longer under active development, and future work moved to Cosmos 3, as of June 1, 2026. Its weights stay downloadable. The launch post links a "try it" page for the 8B that now forwards to a newer model. Neither size is abandoned. Neither is the frontier any more. Buy the size that fits the job, not the one with the bigger number.

What four times the size buys

Both sizes are post-trained from two Qwen3-VL instruct models, and they keep that design. The ladder between them, read from the base model configs on 27 October 2026:

Depth and width roughly double. The small key-and-value memory does not. In plain words: the 8B can hold more of a scene in mind and reason over more of it at once. The 2B does the same kind of thinking with less room. Neither size gets a different vision front end or a different license. The trade is room versus cost, all the way down.

The published scores, both columns

NVIDIA published one cross-size table. It is dated April 28, 2026, and it sits on the 32B model card. Each Cosmos size is scored against the base model it was built from. The category totals:

The 8B wins every category total, and the post-training helps both sizes against their own bases. That is the publisher's story, and it holds.

Now the honest rows, from the same table. No ranking page prints these:

Read those five rows twice. The small size is not strictly worse. It wins one driving judgment row outright. It loses badly on physics in video. And on two tasks per size, the plain base model beat the model NVIDIA post-trained: the 8B under its base on BlinkSpatial and Where2Place, the 2B under its base on BlinkSpatial and LingoQA. These are publisher-run numbers on publisher-named suites. Treat them as direction, then test on your own scenes. If a page hands you one number for "accuracy", it has flattened a table that does not flatten.

Memory, quantization, and the edge split

The memory story has three layers, and mixing them is how bad advice spreads. Each number below names its owner.

The official floors cover the tested full-precision paths. Above them sit the FP8 files the Jetson tutorial ships. Smaller still are the community GGUF quants from the apolo13x repo, ungated and built for llama.cpp. The stock weights need license acceptance. The quants do not.

Now the edge split, from the same tutorial. This is the table that answers the People Also Ask question:

The bottom row carries two surprises. First, the 8B is off the table on the smallest board, by memory. Second, the stock 2B does not fit there either. Embedl, an edge AI company, benchmarked the board and says it plainly: the baseline 2B runs out of memory on Jetson Orin Nano. Their 4-bit mixed-precision builds fit, and their newer build recovers most of what the first one lost, on their tests. On that board you are not choosing between the two sizes. You are choosing a quantized 2B, or a bigger board.

One window warning from the tutorial. At 768 tokens, a single image eats 500 to 600 of them. Default output limits fail with a 400 error. Cut the output budget to about 150 tokens, or the smallest board will refuse work it could have done.

Speed: what is published, and what is not

NVIDIA publishes no speed table for either size. Every dash above is an absence at the source, checked 27 October 2026. What exists is guidance. The Jetson tutorial says that on AGX Orin, switching from the 8B to the 2B improves latency. Embedl claims their 4-bit 2B build roughly halves end-to-end latency on AGX Orin. Neither comes with a number you can put in a budget. If you need tokens per second, measure on your own board. That is the whole published record.

Who you can call, size by size

The size pick is also an access pick. Every row below was read on 27 October 2026.

Start with NVIDIA's own tier. Its public list carries 85 model ids today. Exactly one is a Cosmos Reason 2 id, and it is the 8B. There is no charge for it inside request limits. NVIDIA's Build copy names limits of up to 40 requests per minute and 10,000 requests per day, with a note that they may vary by model. Note what happened to the pages. The 8B's own Build page now forwards to a next-generation model, and the 2B's Build page renders a generic shell. The 8B still answers through the shared list; the 2B has no page and no id there.

AWS rents the 8B by the host hour, as a SageMaker listing: 1.00 US dollars per hour on the small g6e instance sizes, 4.00 on the mid sizes, and 8.00 on the largest, read from the listing on 27 October 2026. That covers the software. The AWS instance bills on top.

No third party hosts the 2B on any surface this page checked. Not the model hub's provider lists, not the multi-provider router, not the marketplace. The 32B, out of scope here, does have third-party rows. The Cosmos Reason 2 pricing guide carries that census in full.

This platform is the 2B's listed per-token route. The cosmos-reason2-2b model id has been on the chat route since 6 October 2026, with a served window of 4,096 tokens per request. The models index is the live catalog. There is no 8B row in the platform's model data as of 27 October 2026. So if your plan says 8B, your hosting plan says NVIDIA's free tier, AWS, or your own hardware.

The cost shape, compared

Three shapes of cost sit on this family, and they do not compare head to head. Free with limits, at NVIDIA, for the 8B. Per token, here, for the 2B. Per host hour, at AWS, for the 8B.

The free tier first, because "free" needs its limits read closely. A perception loop that polls once a second fires 86,400 requests a day. The named daily cap is 10,000. You would hit the wall in under three hours. And there is no per-token number to budget against once you outgrow the demo. Free is for evaluation. It is not a product budget.

The per-token route is what a robot loop can budget. The rate card for this platform's 2B row:

The worked math, on named inputs:

cost per query  =  ( I x Pin + O x Pout ) / 1,000,000

I:     input tokens per query, image tokens plus text
O:     output tokens per query, reasoning included
Pin:   input rate from the fenced row
Pout:  output rate from the fenced row

A labeled example: one camera frame plus a task sentence, about 1,000 input tokens, and a reasoned answer of about 500 output tokens. At the fenced rates that query costs 0.0003. Ten thousand of them cost about 3 US dollars in total, at the example shape. A repeated input prefix bills at the cached rate in the fence. Spending stops when the prepaid wallet is empty, and a refused request bills nothing. The spend-stop argument is the bill shock guide.

The host-hour shape is the third one. One small-size host hour at AWS, at the 1.00 rate read above, equals about 3,300 of the example queries at the fenced per-token rate. Rent when you need the whole instance and its memory. Meter when you need a budget that survives an unattended month. The deeper token math and the per-robot-hour view live in the pricing units guide. Current listed rates always render on the pricing page.

Choose in 60 seconds

Work down the list. Stop at the first line that fits.

1. Smallest Jetson board, or any device with shared memory under 16 GB: the quantized 2B. Nothing official fits there.
2. One 24 GB card, full-precision path: the 2B.
3. 32 GB or more, and scores on physical tasks matter: the 8B.
4. Demos, evaluation, a class project: the 8B at NVIDIA's free tier, inside its limits.
5. An unattended robot loop on a hosted route: the 2B, per token, with a wallet cap.
6. Renting a whole GPU anyway: the 8B at AWS rides along at its host-hour rate.
7. Shipping a product: either size. The license allows commercial use on both. Read the file once and move on.

Common questions

Is the 8B smarter than the 2B? On every published category total, yes: 73.73 against 62.21 on general, 56.90 against 45.52 on robotics, and so on, from the publisher's April 28, 2026 table. On single rows the answer is not clean. The 2B scored higher on a driving collision judgment task, 74.33 to 74.00. And on two tasks per size, the plain base model beat the post-trained build. Test on your own scenes.

Can the 8B run on Jetson Orin Nano? No. The official Jetson tutorial supports the 8B on AGX Thor and AGX Orin only, citing memory. On the smallest board only a 4-bit 2B runs, in a 768-token window. The stock 2B runs out of memory on that board too.

What GPU do I need for each size? The official floors are 24 GB for the 2B and 32 GB for the 8B, on the tested full-precision paths. The FP8 files are about 5 GB and 17 GB. Community 4-bit files are 1.28 GB and 5.03 GB. Add the vision file on top for image work.

Can I call these models as an API? Yes, but not both sizes in one place. NVIDIA's hosted tier carries the 8B id, at no charge inside request limits, read 27 October 2026. This platform carries the 2B, per token, under the cosmos-reason2-2b model id. AWS rents the 8B by the host hour. No third party hosts the 2B on any surface checked.

Is one size newer? No. The 2B and the 8B shipped the same day, December 19, 2025. Its 32B sibling came later, April 29, 2026. The whole family is now in maintenance mode, with Cosmos 3 as the successor line, per the repository banner of June 1, 2026.

Which size should I fine-tune? Both fine-tune, through the repository's post-training examples. The 2B trains and serves on far cheaper hardware, which is why the community quant work and the edge builds all target it. If your task is already close to what the 2B does, a tuned 2B often beats a stock 8B on cost per result. That is a test to run, not a law.

The short version

Same family, same day, same license, same native window. Different weight, different floor, different score. Pick the 2B when the device is small, the loop is fast, or the budget is a metered route. It is also the size this platform lists. Pick the 8B when memory allows and published scores matter, and call it free at NVIDIA's tier or rent it by the hour at AWS. The hardware trade has its own guide, the Jetson versus cloud walkthrough, and the break-even math is the hosted versus local guide. Another family's version chooser, the GR00T N1 chooser, is the sibling page. When you are ready to budget metered calls, the pricing page carries the live rates, and you can sign up and create a key without talking to anyone.

Last verified: 2026-10-27