In the “fully open-source” LLM lane, two projects are almost always mentioned together: OLMo (Allen Institute for AI, USA) and LLM360 (MBZUAI, UAE). Both advocate releasing training data, code, weights, and even intermediate checkpoints — yet they differ in generation, scale, performance, and iteration cadence. This post lays them side by side so newcomers can build a full picture quickly.

TL;DR

One-liner: for benchmark performance pick OLMo; for studying the training process both work, and LLM360’s K2-V2 is a single-point flagship at 70B.

The OLMo Family (AI2)

One-liner positioning

The best-known fully-open LLM family in academia. Every generation releases pretraining data, code, and intermediate checkpoints. It has iterated from OLMo 1 (2024-02) to OLMo 3 (2025-11), and expanded into vision/multimodal (Molmo / OLMoVision).

OLMo 2 (7B / 13B, 2024-11)

Officially positioned as “the best fully-open model to date,” benchmarked against Llama 3.1, Gemma 2, and Qwen 2.5 at the same size. Core evaluations span 11 academic benchmarks: Average / ARC-C / HellaSwag / WinoGrande / MMLU / DROP / NQ / AGIEval / GSM8k / MMLUPro / TriviaQA.

A few key observations:

• vs Llama-3.1-8B: OLMo-2-7B averages 62.9 vs 61.8 — roughly tied or slightly ahead, but at only ~1/4 the training FLOPs. Better compute efficiency.
• vs Qwen-2.5-7B: trails by ~4-5 points (Qwen is clearly stronger on MMLU, ARC-C, GSM8k), but Qwen also uses ~4.5× the FLOPs.
• Generational leap: nearly every benchmark doubled vs the original OLMo-7B (GSM8k 9.2 → 67.5, MMLU 28.3 → 63.7).
• Weak spots: MMLUPro (31-35) and NQ (factual QA) are on the weaker side.

OLMo 3 / 3.1 (2025-11 / 2025-12)

This is OLMo’s biggest generational jump yet. It’s no longer just “a set of weights” — it open-sources the entire model flow: data, code, and checkpoints at every stage, all forkable.

Four product lines:

OLMo 3-Think 32B core benchmarks:

OLMo 3-Think 7B: MATH 94.4 ties Qwen 3 8B; AIME 2024/2025 within a few points. Instruct 7B even beats Qwen 3 8B on Safety (87.3 vs 78.0).

The 2025-12-12 (3.1) update: extended the 32B Think’s RL training by another 21 days, yielding AIME +5, IFEval +4, IFBench +20, plus the first large-size Instruct (32B).

OLMo Hybrid (2026-03, an architecture-research branch)

Note this is not OLMo 4 (OLMo 4 is unannounced). It’s a controlled experiment: take the OLMo 3 7B recipe as the control, change only the architecture (transformer layers → hybrid SSM/attention, Mamba-style), keeping all data/code/hyperparams identical.

It’s the first time hybrid SSM architecture entered the mainstream open-source LLM roadmap, providing controlled evidence that hybrid models scale more efficiently than pure transformers — alongside NVIDIA Nemotron-3 and Jamba, marking “hybrid architectures moving from paper to production.”

LLM360 (MBZUAI + Petuum + Cerebras)

Lead org & positioning

LLM360 is an open research initiative co-founded by three institutions:

The key figure is Eric Xing: MBZUAI President, Petuum founder, former CMU professor. LLM360 and OLMo form an East/West contrast of the two academic open-source camps.

⚠️ Important clarification: the “K2 trillion-parameter, SWE-Bench 65.8%” you often see in search results refers to Moonshot AI’s Kimi K2 (a commercial open-weight MoE, 1T total params) — not an LLM360 product. They just share a name.

Generation 1: Amber & CrystalCoder (both 7B, 2023-12)

Honestly, Gen-1 LLM360 was on the weak side at 7B — its value was “transparency,” not “performance.”

New generation: K2-V2 (early 2026, 70B dense)

LLM360’s most important update since 2024:

Key benchmarks (mid-4 checkpoint):

There’s also K2 Think V2 (an end-to-end reasoning system built on K2-V2, positioned as “the UAE’s first fully sovereign reasoning system,” with partners including G42 and Cerebras).

⚠️ Important: K2-V2’s GPQA-Diamond 55.1 / MATH 94.7 (mid-4 checkpoint) can’t be directly compared with OLMo 3-Think 32B — eval setups, checkpoint stages, and whether reasoning mode is on all differ. Always cite each project’s own technical report.

Side-by-side: how to choose

My take

Both projects are benchmarks of open-science spirit, but differ clearly in engineering investment and iteration cadence:

1. OLMo’s generational progress is visible to the naked eye. From 1 → 2 → 3, each generation raises the ceiling of the fully-open camp by a big margin, and Gen-3 has begun catching Qwen 3. AI2’s pace feels more like a product team.
2. LLM360’s value is “the whole process is researchable.” Gen-1 Amber/CrystalCoder was genuinely weak, but K2-V2 jumping to 70B gives it real competitive teeth. The “360°” slogan isn’t marketing — even training logs are open, making it excellent reproduction/teaching material.
3. Both share a weak spot: general knowledge. Both lose to the strongest open-weights on MMLU / GPQA. That’s the price of full-open (including data): you can’t use high-quality proprietary data.

If you’re doing research reproduction or teaching, OLMo 2/3 + LLM360 K2-V2 are currently the two most complete textbook-grade open LLM stacks.

Further reading

• OLMo 3 official blog — AI2
• “2 OLMo 2 Furious” technical report (arXiv:2501.00656)
• Introducing OLMo Hybrid — AI2
• OLMo-2-1124-7B · Hugging Face
• LLM360 official site
• K2-V2 paper (arXiv:2512.06201)
• LLM360 original paper (arXiv:2312.06550)
• MBZUAI: K2-65B launch