Mid-October packed five major model launches into one window: Meta Muse Spark Open (weights landed October 15), Reflection AI Beam (October 16), DeepSeek V4.1 Flash 1017 (October 17), NVIDIA Nemotron 3.5 Bolt (October 15), and Mistral Large 4 (API preview October 6). Every launch deck said "open." But set the license terms side by side and the substance splits into three tiers: Beam ships Apache 2.0 with zero addenda and no download gate; DeepSeek 1017 ships MIT with zero addenda; Nemotron Bolt is the only one of the five to release weights, data, and recipe together. Muse Spark Open has public weights, but its attribution clause covers distilled descendants, on top of a $20 million revenue threshold and gated downloads — legally, not open source. Mistral Large 4 is blunter: no weights as of this writing, no named license, and third-party leaderboards still file it as proprietary.
Scope first: Muse Spark Open and Beam already have their own deep dives here; this piece compares license terms and accessibility only. Figures come from official model cards, official blogs, and the Artificial Analysis Intelligence Index v4.3.2 ("AA"), as of 2026-10-23.
1. The five at a glance
| Model | Release and weights | Parameters | License | Hardware bar |
|---|---|---|---|---|
| Muse Spark Open | Oct 15, weights same day | 816B total, ~42B active, 1M context | Muse Model License (Meta custom) | BF16 ~1.5TB; NVFP4 needs 700GB+ VRAM, or two 512GB M5 Ultra machines |
| Beam | Announced Oct 5, weights Oct 16 | 501.3B total, 23.4B active | Apache 2.0, zero addenda | NVFP4 checkpoint ~294GB; vendor suggests 4x B200 |
| DeepSeek V4.1 Flash 1017 | Oct 17, web, API, weights same day | 763B total (third-party config analysis) | MIT, zero addenda | Repo ~510GB (unconfirmed); official FP8 and INT4 quants |
| Nemotron 3.5 Bolt 74B-A9B | Oct 15, weights same day | 74B total, 9B active, 256K context | OpenMDW-1.1 (NVIDIA's own, not OSI) | Native 2-bit QAT ~24GB, one 24GB card |
| Mistral Large 4 | Oct 6 API preview, no weights | 1.05T total, 52B active (with embedding and output, per A52B repo name; docs cite 49B) | Unnamed, docs say "Open" | No weights, no bill |
Calibration notes: DeepSeek's parameter count and repo size are third-party analyses; Mistral's active parameters have two variants — 52B counts embedding and output, 49B does not — this piece uses 52B and flags 49B.
Repositories: Muse Spark Open (plain URL https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B , with an NVFP4 branch), Beam (plus FP8, NVFP4, and a DFlash2 drafter), DeepSeek V4.1 Flash 1017 (on ModelScope too), Nemotron 3.5 family; Mistral Large 4 has no checkpoint under the mistralai org as of this writing.
2. License terms, dimension by dimension
| Term | Muse Spark Open | Beam | DeepSeek 1017 | Nemotron Bolt | Mistral Large 4 |
|---|---|---|---|---|---|
| License name | Muse Model License (custom) | Apache 2.0 | MIT | OpenMDW-1.1 | Unnamed |
| OSI-certified open source | No | Yes | Yes | No (permissive, not OSI) | Unknown |
| Attribution | Prominent "Built with Muse Spark Open" label; distilled models count | Standard Apache NOTICE only | None | None | Unpublished |
| Revenue threshold | $20M over any 12 months (MaaS resale only) | None | None | None | Unpublished |
| Download | HF gated form | Ungated | Ungated | Ungated | Weights unreleased |
| Derivatives and distillation | Allowed, but derivatives carry the same obligations | Fully free | Fully free | Fully free | Unpublished |
| Use restrictions | Bans military, nuclear, critical infrastructure, under-18 use | None | None | None | Unpublished |
| Data and recipe | Training data closed | Data closed; SFT and RL recipe public | Data and recipe closed | Weights, data, and recipe all public | Unpublished |
The gaps are wider than the names suggest.
Attribution is Muse's heaviest addendum. Any product using the model or its derivatives must prominently display "Built with Muse Spark Open" — and "derivative model" is defined expansively: models trained or distilled on its outputs count, so the obligation travels with distillates. Third-party analysts read this as aimed at clouds that would resell the model wholesale. The $20M threshold covers MaaS resale only; internal use, research, and your own commercial products are exempt. The structure resembles Kimi K3's threshold (press-reported).
Beam and DeepSeek form the clean control group: zero addenda, ungated, fully free derivatives and distillation. A clean license also buys ecosystem speed: Beam launched with 40-plus partners and day-zero support across Transformers, vLLM, SGLang, Unsloth, Axolotl, and Dynamo. Muse's hosting coverage is fine (Baseten, Fireworks, Groq, Together), so the burden lands on self-hosters and distillers.
Nemotron Bolt's OpenMDW-1.1 is NVIDIA's own license: permissive, no revenue threshold, no OEM restriction, no MaaS clause — but not submitted for OSI certification. What makes it special is scope: training data and recipe are fully public, the only one of the five. Third-party analysts call this infrastructure strategy rather than generosity: NVIDIA sells hardware, not tokens — the wider the models spread, the more GPUs sell.
Mistral Large 4's license text arrives with the weights. Large 3 was Apache 2.0; Large 4 is not, the company has made clear. Some coverage cites a $20M monthly threshold in Mistral's older licenses, but that describes prior terms and is unconfirmed for Large 4.
3. The hardware bill
Licenses decide whether you may; hardware, whether you can.
Beam: the NVFP4 minimum checkpoint is roughly 294GB (another source says ~280GB, unconfirmed). With KV cache and long-context headroom, ~400GB of memory is a comfortable start. All 188 safetensors shards are ungated; the vendor suggests 4x B200.
Muse Spark Open: BF16 weights run about 1.5TB; NVFP4 needs 700GB-plus of VRAM, effectively a B200 node. The official demo chains two 512GB M5 Ultra Mac Studio machines via MLX into one system — Meta calls it the first of its class to run on a desktop. Checkpoint formats are thorough: official BF16 and NVFP4, AMD and Intel quant flavors, plus Unsloth's GGUF.
DeepSeek V4.1 Flash 1017: the repo is ~510GB (still unconfirmed), with official FP8 and INT4 quants; MLA compression reportedly keeps 128K-context KV cache under 2.8GB. A circulating claim that INT4 runs on a 24GB consumer GPU conflicts with the 763B total parameters and reads as exaggerated.
Nemotron Bolt: the only true small-VRAM option. Native 2-bit QAT (effectively 2.0625 bit) packs to about 24GB, verified on an RTX 5090, and fits DGX Spark and RTX Spark laptops ($2,599 and up in the US from October 16). vLLM supports A100, H100, and Blackwell; Unsloth GGUF ~22GB, Ollama ~25GB. The cost: no official Apple Silicon support (Ollama confirms Metal will not work), and no independent AA score yet.
Mistral Large 4: no weights, no bill. A 1.05T BF16 deployment would start around 2TB by class analogy.
4. Capability, measured with one ruler
Self-reported numbers are all over the map. On AA's v4.3.2 third-party ruler: GLM-5.3 scores 45, Kimi K3 44, DeepSeek 39.5 and Muse Spark Open 39 effectively tied, Mistral Large 4 38, Beam 30 — clearly low for its class. Two footnotes: Beam has no public API, so its AA score comes from self-hosted inference; Mistral Large 4 was still classified as proprietary by AA when it debuted.
Self-reports are not useless — just not comparable. Beam's SWE-bench Verified appears as 80.9% in the official blog and 81.6% on the model card; both coexist, likely different harnesses or precisions. On DeepSWE, Beam self-reports 44.4, DeepSeek 77.0, Muse 60 — each under its own harness; Terminal-Bench spans 2.1 and 4.0. Muse's card shows MCP-Atlas 89.2 and standout safety numbers, but vendor-run. DeepSeek's self-reported DeepSWE 77.0 contrasts with AA's own tests (HLE 39.2%, Terminal-Bench 26.8%). Mistral self-reports Cybench 93% and DeepSWE 62% — with GLM-5.3 at just 61 in its table while public boards put GLM-5.3 near 69. AA also flags Mistral's verbosity: 200M output tokens to finish the index (Muse used 110M), costing $1,607 at list prices versus Muse's $1,183. Bolt leads its class on self-reported PinchBench 87.82, but AA has not scored it separately. Whenever you see a "number one," ask which harness.
5. Tiering, and a cheat sheet for dilution
A third-party framework maps well onto this batch: restricted open weights, genuine open source, and hardware-vendor open source. Adding a slot for unfulfilled promises:
Tier A, fully open. Beam: Apache 2.0, zero addenda, ungated, with the SFT and RL recipe and Mirror CLI open, plus a transparent training account (23.8T pretraining tokens; RL on 10,500 GB300 GPUs for four weeks); the weakness is a low AA score for its class. DeepSeek 1017: MIT, zero addenda; the weakness is closed data and recipe. Nemotron Bolt: OpenMDW-1.1 with no addenda, and the only one to open weights, data, and recipe together; the weakness is the missing OSI certification and deep binding to NVIDIA's stack.
Tier B, open weights with strings. Muse Spark Open: weights are out, but attribution covers distillates, the $20M MaaS threshold and gated download apply, and the usage policy bans military, nuclear, critical-infrastructure, and under-18 use. For most individual and internal commercial developers, practical freedom is close to MIT — legally it is not open source, and the official safety report had not shipped.
Tier C, promise unfulfilled. Mistral Large 4: seventeen days after launch (as of October 23), no weights and no license name. Even the context window has three versions: 1M in official docs, 524,288 recorded by AA, 512K on OpenRouter. Its headline CyberGym 81.7% rests on rival models refusing 98% of tasks, which caps how much it proves.
Dilution dimensions, heaviest first: attribution obligations (Muse), revenue threshold (Muse, $20M per year), gated downloads (Muse), late weights (Mistral), license opacity (Mistral), closed data and recipe (Muse, Beam, and DeepSeek all close training data; NVIDIA is the sole exception).
6. Picking a license by use case
| Your use case | Prefer | Why |
|---|---|---|
| Personal projects, internal commercial use | Any of them, Muse included | Muse's threshold targets MaaS resale; internal use is exempt — mind the attribution |
| Running MaaS or reselling API | Beam, DeepSeek, Nemotron | No revenue thresholds; Muse requires a separate agreement past $20M |
| Fine-tuning or distilling for redistribution | Beam, DeepSeek, Nemotron | Apache and MIT carry no derivative entanglement; Muse's attribution spreads through distillates |
| Reproducing training, studying recipes | Nemotron Bolt | The only one with data and recipe fully open |
| One 24GB card, local deployment | Nemotron Bolt | Native 2-bit QAT, ~24GB packed |
| Waiting on Mistral Large 4 | Keep waiting | Reassess when weights and license text land; only the API exists today |
The short version: only the Apache 2.0 and MIT pair lets both legal and procurement sleep easy; Nemotron Bolt trades ecosystem lock-in for openness; Muse reserves its freedoms for those below the threshold; Mistral tells an open-source story with a license not yet written down. Read the license first, then do the hardware math.
FAQ
Q1: Muse Spark Open published its weights — why is it not open source? A: Open source is judged by the license, not by whether weights are downloadable. The Muse Model License requires a prominent "Built with Muse Spark Open" label, defines derivatives broadly enough to cover distillates, and adds a $20M MaaS threshold plus gated downloads. The OSI definition requires no discrimination and no field-of-use restrictions; these addenda fail that bar.
Q2: Do Apache 2.0, MIT, and OpenMDW-1.1 differ in practice? A: For most developers, barely: all permit commercial use, modification, redistribution, and distillation, with no revenue thresholds. The differences are compliance ones. Apache 2.0 and MIT are mature, OSI-certified licenses with explicit patent grants; OpenMDW-1.1 is NVIDIA's own, permissive but uncertified, which may add a legal review cycle at larger companies.
Q3: Can I self-host Mistral Large 4 today? A: No. As of 2026-10-23 there is no Large 4 checkpoint under the mistralai org on Hugging Face, the license is unnamed, and AA still files it as proprietary. The company promises weights by end of October, citing red-team work with cybersecurity and government partners. Until weights and license text land together, the API is all there is.
Q4: Which of the five can run on 24GB of VRAM? A: Only Nemotron 3.5 Bolt, trained with native 2-bit QAT, packing to about 24GB, verified on an RTX 5090. The circulating claim that DeepSeek 1017's INT4 fits a 24GB card conflicts with its 763B total parameters and reads as hype. Beam and Muse's smallest checkpoints are roughly 294GB and 700GB-plus — a different universe.
Q5: Why can't the five models' self-reported scores be compared directly? A: Different harnesses and benchmark versions. Beam's SWE-bench Verified exists as 80.9% (blog) and 81.6% (model card); DeepSWE self-reports come from different harnesses, so Beam 44.4, DeepSeek 77.0, and Muse 60 in one table means nothing; Terminal-Bench spans 2.1 and 4.0. The only reliable cross-vendor ruler today is the AA Intelligence Index v4.3.2 — noting Beam's score came from self-hosted inference.
Related reading
- Reflection AI Beam 501B: does 1TB of weights fit four B200s?: the hardware math behind Beam, deeper than this article's license view
- Nemotron 3.5 Bolt local deploy: 74B into 24GB VRAM: three deployment routes for the tier-A pocket rocket
- Small-VRAM strategies compared: how far each route gets you: general playbooks for big models on small cards
- Mistral Large 4: weights and license still in transit: the tier-C launch cadence and license black box, tracked separately