Four open-weight models landed between October 5 and October 17, 2026, and they all chase the same buyer: someone who wants frontier-level reasoning on hardware they control. Reflection AI published the weights for Beam 1.0 on October 16 under Apache 2.0. Meta shipped Muse Spark Open on October 15. DeepSeek pushed a V4.1 Flash update on October 17, and Mistral opened a public preview of Large 4 on October 6. If you searched for the best local LLM this week, these four are the names crowding the results.
The honest answer needs a caveat before any ranking. Each model is a huge mixture-of-experts system, with 501 billion to roughly one trillion total parameters, so “local” here means a workstation cluster or a rack, not a gaming laptop. Meta’s own launch, which we covered when Muse Spark Open debuted on seven clouds, leaned on hosted providers as much as on downloads. This comparison sorts the four by what you can actually run today, what it costs, what the license allows, and which one deserves your first test.
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What “Local” Means for Models This Size
A year ago, a best local LLM list meant models that fit in 8GB to 32GB of memory. The top search results for that phrase still read that way, with guides sorted by RAM tier. The four models in this article sit in a different class. Muse Spark Open has 816 billion total parameters with 42 billion active per token. Beam 1.0 has 501 billion total with 23 billion active. Mistral describes Large 4 as roughly one trillion total parameters. Only the active slice does work on each token, but every expert still has to live in memory or on fast storage.
A rule of thumb helps here, and it is an estimate rather than a vendor spec. At FP8 precision a parameter takes about one byte, so Beam’s 501 billion parameters need roughly 500 GB for weights alone, before any context cache. Reflection released BF16, FP8 and NVFP4 versions, which gives buyers three points on the size-versus-accuracy curve. One report on DeepSeek’s release put the Hugging Face files at roughly 510 GB, which fits the same math.
Reports on Muse Spark Open say it can run locally on multiple high-memory Mac Studios. One account says two 512-GB systems or four 256-GB systems may be required. Our earlier piece on Muse Spark Open on two Mac Studios walks through that pairing. That is a five-figure hardware bill for most buyers, which is why the cloud-hosted route matters even for teams that care about control.
The three ways to run these models
You can download weights and serve them yourself, rent the same weights from a third-party host, or call the vendor’s own API. Self-hosting gives you data control and no per-token bill, but it needs the memory budget above. Renting open weights from providers keeps you portable, since you can move the same model between hosts. The vendor API is the fastest to start and the weakest on control.
Treat that choice as the real “local” decision. A team that wants to leave a hosted frontier model for data-residency reasons might be fine renting Muse Spark Open from a European provider today and buying hardware later. A research lab with a Mac Studio budget may skip the middle step.
If your machine has 8GB to 32GB of memory, none of the four fits, and you should look at the smaller open models in our wider best open source LLM ranking instead. The rest of this guide assumes you have a workstation cluster, a server budget, or a plan to rent.
Specs Table: Muse Spark Open vs DeepSeek V4.1 Flash vs Beam 1.0 vs Mistral Large 4
The table below lists only figures that appear on official pages or in at least two independent sources as of October 23, 2026. Where a number rests on a single report, the cell says so and the text below covers it with a hedge. Several vendors have not published every spec, and we left gaps instead of guessing.
| Spec | Muse Spark Open | DeepSeek V4.1 Flash | Beam 1.0 | Mistral Large 4 |
|---|---|---|---|---|
| Developer | Meta | DeepSeek | Reflection AI | Mistral AI |
| Release date | Oct 15, 2026 | V4.1 Flash Sept 10, 1017 update Oct 17 | Announced Oct 5, weights Oct 16 | Public preview Oct 6 |
| Total parameters | 816 billion | See text | 501 billion | About 1 trillion |
| Active parameters | 42 billion | See text | 23 billion | See text (figures conflict) |
| Context window | 1 million tokens | See text | 1 million tokens | See text |
| Input modalities | Multimodal | See text | Text only | Natively multimodal |
| Architecture | Mixture of experts | See text | Sparse mixture of experts | Hybrid instruction and reasoning MoE |
| License | Muse Model License (custom) | See text | Apache 2.0 | Not confirmed as of Oct 16 |
| Weights downloadable today | Yes, Hugging Face | Yes, Hugging Face | Yes, Hugging Face | No, planned for end of October |
| Weights location and formats | Hugging Face (see repo) | deepseek-ai/DeepSeek-V4.1-Flash on Hugging Face | Hugging Face, BF16, FP8, NVFP4 | Not yet released |
| API model ID | Varies by host | deepseek-flash | Beam-1.0-501B-A23B | Mistral Large 4 in Mistral Studio |
| Positioned for | Agentic coding, tool use | Fast, shorter outputs | Coding, reasoning, agentic work | Hybrid reasoning, engineering workflows |
Two details deserve a closer look. First, Beam is text only, while Muse Spark Open and Large 4 accept images. If your workflow reads screenshots, charts or scanned documents, Beam drops out of contention no matter how well it scores elsewhere. Second, Large 4 is the only model on this list you cannot download yet, so every “local” claim about it is a forward-looking statement.
DeepSeek’s cells say “See text” because the figures sit on a single source. The model card, as one report describes it, lists a multimodal mixture-of-experts model with up to a one-million-token context window and an MIT license. NVIDIA’s documentation describes V4.1 Flash as accepting text and images with controllable reasoning effort. We treat those as reported, not confirmed, until DeepSeek’s own pages say the same.
Mistral’s active-parameter number moved. Its October 6 announcement gave 52 billion active parameters, and a later October 21 article gave about 49 billion. Mistral did not explain the gap, so use neither number for capacity planning until the weights ship.
Why active parameters matter for speed
The active-parameter figure drives decoding speed, while the total figure drives memory. A mixture-of-experts model routes each token through a small set of experts, so Beam touches 23 billion parameters per token and Muse Spark Open touches 42 billion. In practice, that means Beam should generate tokens faster than Muse Spark Open on identical hardware once both are loaded, because it moves less data per step. This is reasoning from architecture, not a measured result, and real throughput depends on the serving stack, batch size and quantization.
Memory still sets the entry price. A cluster that can hold 816 billion parameters costs far more than one that holds 501 billion, even though both models run only a slice of their weights on any given token. Buyers often miss that gap. They see “42 billion active” and picture a mid-size model, then discover that the whole expert set must sit in fast memory. Plan around the total, then judge speed by the active count.
Long context adds a second bill. Muse Spark Open and Beam both advertise one-million-token windows, and the key-value cache for a long prompt grows with context length. A deployment that fits the weights comfortably can still run out of memory when users paste an entire repository. Test your real prompt lengths early, not after the hardware arrives.
Meta Muse Spark Open: 816B Parameters, 42B Active
Muse Spark Open, at 816 billion total parameters, is the only model in this group from a US big-tech lab. Meta released it in October 2026 as an open-weight model, with weights on Hugging Face under its custom Muse Model License. It takes multimodal input, supports up to one million tokens of context, and targets agentic coding, which Meta describes as reading codebases, writing and debugging code, and using tools.
The launch came with unusual reach. Meta listed API availability through Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI on day one. Reports also say the weights landed in cloud model repositories including Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models, though that comes from a single source. Intel announced day-zero hardware support, which suggests the model was tested on non-NVIDIA silicon before release.
Where Muse Spark Open leads
On Plotly’s DataAnalyticsBench, Muse Spark Open scored 38 out of 43, the highest result among the open-weight models Plotly covered at the time. That is a narrow, vendor-neutral test of data analysis work, so read it as one data point. A third-party comparison from October 19 placed it beside DeepSeek V4.1 Flash, Beam, Kimi K3 and GLM-5.3, and called it the most capable US open-weight model in that group. The same outlet’s headline said it trails Kimi K3 by five points, so the title of “best” depends on whether you count Chinese labs.
Where it falls short
The license is the catch. The Muse Model License is custom, not Apache or MIT, so legal teams must read it before shipping a product on top. Hosting is also paid, since Meta does not run a price list for the open-weight model. Third-party providers charge $1.25 per million input tokens and $4.25 per million output tokens, according to public listings, which makes it the priciest of the three hosted options here on output.
For local use, the Mac Studio route gives it the clearest story. No other model in this group has a documented desktop-cluster path yet. If you can afford two 512-GB machines, Muse Spark Open is the strongest single download for general capability today.
DeepSeek V4.1 Flash: The 1017 Update and the Price Floor
DeepSeek announced V4.1 Flash on September 10, 2026 as a smarter, faster and more efficient model, with new API pricing effective at 04:00 UTC the same day. On October 17 it shipped DeepSeek-V4.1-Flash-1017, a point update that the company describes as stronger on benchmarks, faster at finishing tasks and shorter in its outputs. The update is live on DeepSeek’s website, mobile app and developer API. The API name did not change, so existing code that calls deepseek-flash picks up the new version without edits.
The open-weight side lives on Hugging Face as deepseek-ai/DeepSeek-V4.1-Flash. One report says the repository uses an MIT license and holds roughly 510 GB of files. If that holds, DeepSeek offers the most permissive license in this group alongside Beam’s Apache 2.0, with the caveat that you should confirm the license text yourself. DeepSeek’s own V4 Pro license and specifications are separate and should not be assumed to match.
Pricing is the headline
A report dated October 19 listed DeepSeek at $0.30 per million input tokens and $1.20 per million output tokens at peak rates. DeepSeek’s API documentation states that “Off-peak rates are 50% of peak rates,” so scheduled batch work can run at half those figures. Reports of the exact peak windows differ, with one giving 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays and another giving Japan-time windows. Check the current schedule before you rely on it.
Teams that read our DeepSeek V4.1 Flash versus V4 Pro comparison will remember the routing saga. DeepSeek first said that all deepseek-v4-pro requests would route to V4.1 Flash from September 14, then its changelog reversed that plan and kept V4 Pro as a separate service with unchanged billing. For anyone building on a hosted API, that reversal is a lesson in vendor volatility, and one more reason to prefer weights you can download.
Who should pick it
Pick V4.1 Flash when cost per token matters most and you can accept a vendor in China, which some procurement teams cannot. The step-by-step in our DeepSeek V4.1 Flash API setup covers the hosted path. For fully local use, plan around that 510 GB figure and test the 1017 weights on your own tasks before you trust any benchmark.
Reflection Beam 1.0: Apache 2.0 and the Text-Only Trade
Reflection AI introduced Beam on October 5, 2026 as its first open-weight model, aimed at coding, reasoning and agentic workloads. The weights followed on October 16 on Hugging Face. Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active, a one-million-token context window, and an Apache 2.0 license. Reflection also released the tokenizer, a reference implementation, and BF16, FP8 and NVFP4 weight formats.
Apache 2.0 is the cleanest license in this comparison. It permits commercial use, modification and redistribution with a patent grant, and procurement teams already know it. For a company that wants to fine-tune a model, ship it inside a product and avoid a custom-license review, Beam removes the biggest friction in the group. Our earlier look at Reflection’s Beam launch covers the company background.
Efficiency details from the release
Reflection’s technical write-up reports that dynamic packing kept training batches 99.99% full on average, and that expert load stayed near-uniform, with the busiest expert reaching only 1.04 times the mean across MoE layers at the end of pretraining. Those are engineering metrics, not quality scores. They suggest a well-balanced expert layout, which tends to help serving efficiency, but they say little about how Beam handles your prompts.
Access and price
Reflection’s own API is a free public beta, and the company has not published a paid price. A third-party gateway began selling hosted Beam access on October 17. Third-party trackers recorded rapid price swings between October 16 and October 22, so any per-token figure you see for Beam may already be stale. Microsoft announced Beam’s availability in Foundry on October 19, with the post updated October 22.
The trade-off
Beam is text only. It cannot read images, and its price and provider ecosystem are younger than Meta’s or DeepSeek’s. The upside is the smallest active parameter count in the group at 23 billion, which usually means faster decoding for the same memory footprint. For a coding agent that lives in a terminal and never needs vision, that trade looks good.
Mistral Large 4: Open-Weight on Paper, Preview in Practice
Mistral launched Large 4 as a public preview on October 6, 2026 through its API in Mistral Studio. It describes the model as natively multimodal and a hybrid of instruction following and reasoning in one mixture-of-experts system with roughly one trillion total parameters. Mistral’s own nickname for it is “Le Chonk,” though the official product name is Mistral Large 4. On October 21, Mistral said it trained Large 4 on 3,800 NVIDIA Grace Blackwell GPUs in Mistral-operated European data centers.
The key limitation is availability. As of October 23, the weights are not downloadable. Mistral says it will test the model with developers, cybersecurity experts and authorities before release, including a version with fewer safety restrictions, and plans to publish weights at the end of October. One report names October 27 as the date, but that rests on a single source. Mistral’s governance page for Large 4 is the place to watch for the license, which was still unknown as of October 16. Do not assume Apache 2.0 just because Large 3 used it.
Pricing and positioning
The preview carries an official price of $1.36 per million input tokens and $4.18 per million output tokens. That puts it close to Muse Spark Open on cost and well above DeepSeek. Mistral’s launch post also claims strong visual grounding, with Large 4 beating GPT-6-Astra on the Dense 200 test, 42% to 41%. That is a vendor-run comparison against a closed model, so it is a hint, not a verdict.
For European buyers, the story is sovereignty. Mistral announced a partnership with NVIDIA on October 21 to apply Large 4 to engineering work such as coding, agents, simulation and design. Our coverage of the Mistral Large 4 launch and the Mistral Large 4 API setup gives the hosted details. For local use, though, the right plan is to wait. Evaluating Large 4 for self-hosting before the weights and license exist means evaluating a promise.
Benchmarks: What the Numbers Say Across Sources
Independent head-to-head data for these four models is thin, because three of the four shipped within the last 18 days. Nobody has yet run one benchmark suite across all of them under the same settings. What exists is a patchwork. The table collects every concrete result we could verify, with the source and its limits.
| Source | What it measures | Result | Limit |
|---|---|---|---|
| Plotly DataAnalyticsBench | Data analytics tasks | Muse Spark Open scored 38 of 43, top among open-weight models covered | Plotly’s own benchmark, covers a subset of models |
| Mistral Large 4 launch post | Visual grounding (Dense 200) | Large 4 at 42% vs GPT-6-Astra at 41% | Vendor-run, against a closed model |
| Comparison published Oct 19 | Open-weight ranking incl. Kimi K3, GLM-5.3 | Headline says Muse Spark Open trails Kimi K3 by 5 points | Metric not stated in the headline |
| Reflection technical report | Training efficiency | 99.99% batch fill, 1.04x peak expert load | Engineering metrics, not model quality |
| DeepSeek release notes | Self-reported | 1017 update claims stronger benchmarks, faster tasks, shorter outputs | No head-to-head numbers cited by DeepSeek in the notes we verified |
Plotly also published a Beam 1.0 results page for data analysis, which makes DataAnalyticsBench the one suite that covers more than one model here. Pull the per-model scores from that page before you decide, because we only verified the Muse Spark Open figure.
How to read vendor claims
Reflection claims Beam matches GLM 5.2 on reasoning at a third to a quarter of the compute, and reports say DeepSeek beats Beam on Reflection’s own coding table. Both statements come from first-party material or from research summaries, and neither has independent confirmation yet. Treat them as hypotheses to test.
A sensible approach is a private eval. Pick 50 real tasks from your own workload, such as bug fixes from your repo, tickets from your queue or documents from your archive. Run all four models with identical prompts and score the results blind. A two-day private test will tell you more than any public leaderboard released in the first month of a model’s life.
What to score in your private eval
A private eval works only if the scoring is consistent. Use the same system prompt, temperature and maximum output length across all four models, and hide the model names from reviewers. Score each answer on correctness, instruction following, and whether it needs edits before use. Track latency and cost per accepted answer, not per token, since DeepSeek’s shorter outputs and Beam’s lower active parameter count change the math.
Include at least five tasks that need tool use, because the vendors position these models for agentic work. Include a long-context task near the top of the advertised window and a task with an image if you plan to use a multimodal model. Finally, repeat the run a week later. Fresh releases get patched fast, and DeepSeek’s 1017 update arrived just five weeks after the original V4.1 Flash launch.
Pricing and Licenses: Hosted API Rates and Self-Hosting Costs
The pricing picture splits into two tables. The first lists hosted API prices where a figure is verified. The second applies those rates to a sample workload, so you can see the gap in dollars. Beam has no official paid price, and we did not put third-party quotes in the table because they moved several times in a week.
| Model | Input per 1M tokens | Cached input per 1M | Output per 1M | Notes |
|---|---|---|---|---|
| Muse Spark Open | $1.25 | $0.15 | $4.25 | Third-party providers, same headline rates across hosts |
| DeepSeek V4.1 Flash (peak) | $0.30 | Not verified | $1.20 | Off-peak is 50% of peak per DeepSeek |
| Beam 1.0 | Free public beta | Free public beta | Free public beta | No official paid price published |
| Mistral Large 4 (preview) | $1.36 | Not verified | $4.18 | Official Mistral price, preview stage |
Now the sample workload: 100 million input tokens and 20 million output tokens in a month, with no caching. Muse Spark Open is listed at $1.25 per million input tokens and $4.25 per million output tokens across multiple third-party providers. Mistral Large 4 is listed at $1.36 per million input tokens and $4.18 per million output tokens for the preview. DeepSeek is reported at $0.30 input and $1.20 output per million tokens at peak, and off-peak rates are half of peak. Beam costs nothing on Reflection’s beta API, though a free beta is not a service-level commitment.
Licenses decide more than price
Self-hosting changes the math entirely. You pay for hardware, power and staff, and the license sets what you can build. Beam’s Apache 2.0 is the simplest. DeepSeek’s MIT license, as one report describes it, is equally permissive. Muse Spark Open uses Meta’s custom Muse Model License, which a lawyer should read before launch. Large 4’s license is unconfirmed.
A rough break-even helps. If your hosted bill on DeepSeek would run $54 a month, no hardware purchase pays back. If you spend tens of thousands a month on a frontier closed model and want to cut that, the economics of a Mac Studio pair or a GPU server start to look different. Run your own usage numbers before buying anything.
Compliance and Procurement: Origin, License and Data Control
Open weights solve one compliance problem and create two others. They solve data residency, since the model runs where you put it and your prompts never leave your network. They create a license-review burden and, for some buyers, an origin question.
Start with the license. A legal team can approve Apache 2.0 from a template, and the same is likely true for MIT if DeepSeek’s repository confirms it. Meta’s custom Muse Model License needs a line-by-line read, covering any limits on commercial scale, redistribution of fine-tunes and acceptable-use terms. Large 4 has no confirmed license at all, so no approval is possible until Mistral publishes one.
Origin is the second issue. Some public-sector and regulated buyers apply policies that treat models from certain countries differently, and coverage of the October releases frames the US-versus-China open-weight race as a procurement question. Beam and Muse Spark Open come from US labs, Large 4 from a French lab, and DeepSeek from a Chinese one. Check your own policy before you shortlist, because a model that wins your benchmark but fails procurement wastes weeks.
Data handling differs by deployment route as well. Running weights on your own hardware keeps data inside your boundary. Renting the same weights from a provider such as Together AI or Nebius sends prompts to that provider, under its terms, not the model maker’s. Calling DeepSeek’s own API sends data to DeepSeek. Those three routes carry different risk profiles even though the model name is identical.
Finally, think about update control. A hosted alias can change under you, as DeepSeek’s September routing plan showed before it was reversed. Pinning a weights file by hash gives you the same model next month, which matters for audits and reproducibility. That stability is a quiet argument for the local route.
Five Real-World Signals: Who Is Shipping These Models
Because the releases are days old, there are no long-running production case studies yet. What exists is a set of concrete moves by hardware vendors, clouds and gateways, and each one tells you how the ecosystem treats the model.
- Intel supported Muse Spark Open from day zero. Intel’s newsroom announced day-zero support for the 816-billion-parameter model on October 15 and 16. Day-zero support from a silicon vendor means you can plan a non-NVIDIA deployment.
- Mac Studio clusters became the desktop route. Reports say Muse Spark Open runs on multiple high-memory Mac Studios, one account citing two 512-GB or four 256-GB machines. That is the first credible desk-side path for a model of 816 billion parameters.
- Microsoft put Beam in Foundry. The Foundry announcement on October 19, updated October 22, lists Beam alongside other new open models. Enterprises already on Azure can test Beam without new procurement.
- Mistral and NVIDIA targeted engineering work. The October 21 partnership applies Large 4 to coding, agents, simulation and industrial design, and Mistral says Large 4 trained on 3,800 Grace Blackwell GPUs.
- A third-party gateway began selling Beam on October 17. One day after the weights appeared, hosted access existed outside Reflection’s own beta. That speed shows the weights are usable by serving stacks right away.
- DeepSeek’s routing reversal. The September 14 plan to send V4 Pro traffic to Flash was reversed in the changelog. Anyone who built on a vendor-controlled alias learned that a hosted name can change.
The pattern across these signals is that hosted availability races ahead of local tooling. If you want to try a model this week, rent it. If you want to own it, wait for the quantized builds and the community serving recipes that normally follow a release by a few weeks.
Best Local LLM by Use Case
No single model wins every scenario. The recommendations below map the four models to specific jobs, with the reasoning behind each pick.
Best local LLM for a license-clean company deployment: Beam 1.0. Apache 2.0, downloadable weights, three precision formats and the smallest active parameter count make it the lowest-risk choice for a product team.
Best local LLM for maximum capability on a desktop cluster: Muse Spark Open. It has the top reported score on Plotly’s data benchmark among open-weight models covered, multimodal input and a reported Mac Studio route. Budget for a custom license review.
Best for cost-sensitive agents: DeepSeek V4.1 Flash. At $0.30 per million input tokens at peak, and half that off-peak, it undercuts the other hosted options by a wide margin. Shorter outputs in the 1017 update also trim output spend.
Best for image-heavy workflows: Muse Spark Open or Mistral Large 4. Both accept images. Beam does not. Large 4 only counts once its weights ship.
Best for European data-sovereignty plans: Mistral Large 4, after release. The training location and the NVIDIA partnership point to a European-hosted stack. Wait for the weights and the license.
Best for teams without a hardware budget: rented Muse Spark Open or DeepSeek. Hosted open weights keep you portable without a purchase. If you need a smaller model for a 16GB laptop, none of these four applies, and our Llama 4 versus Qwen versus Mistral comparison is a better starting point.
Migration Guide: Moving a Workload to a Local Open-Weight Model
Most teams do not rip out a hosted model in a day. A safer path runs the open model in shadow mode, compares outputs, and shifts traffic once quality holds. The steps below assume you already call a hosted model through an OpenAI-style client.
- Freeze a test set. Collect 50 to 200 real prompts with the outputs you consider good.
- Abstract the endpoint. Put the base URL and model name in environment variables so a swap needs no code change.
- Start on a hosted open-weight endpoint. Rent the model before you buy hardware, since hosted rates are cheap for a trial.
- Run shadow traffic. Send each request to both models and log both answers for blind review.
- Check your hardware budget. Use the rule of thumb of one byte per parameter at FP8 for weights, then add room for context cache.
- Move traffic in stages. Shift 5%, then 25%, then the rest, with a rollback switch.
Reflection’s developer docs show an OpenAI-compatible path for Beam. This pattern keeps your client code identical across providers:
export LLM_BASE_URL="https://api.reflection.ai/openai/v1"
export LLM_MODEL="Beam-1.0-501B-A23B"
export LLM_API_KEY="your-key-here"curl “$LLM_BASE_URL/models/$LLM_MODEL” \
-H “Authorization: Bearer $LLM_API_KEY”
“`
To test a different model, change the three variables. For DeepSeek, the model name is deepseek-flash and you should take the base URL from DeepSeek’s API docs. For Muse Spark Open, take both values from the host you pick, since model IDs vary across Baseten, Fireworks, Groq, Modal, Nebius, OpenRouter and Together AI.
When you move to self-hosting, keep the same client and replace the base URL with your own inference server. Our AI and machine learning coverage tracks the serving stacks as they add support for these models.
Pros and Cons of Each Model
The summary below condenses the sections above. It lists only points backed by the verified material in this guide.
| Model | Pros | Cons |
|---|---|---|
| Muse Spark Open | 816B total and 42B active, multimodal, 1M context, top DataAnalyticsBench result among open models covered, day-zero Intel support, wide host list | Custom Muse Model License, large memory need, highest verified hosted output price at $4.25 per million, just above Large 4’s $4.18 |
| DeepSeek V4.1 Flash | Lowest verified hosted price, off-peak discount of 50%, unchanged API name after 1017 update, weights on Hugging Face | Spec details rest on single sources, vendor-routing history, peak-window reports disagree |
| Beam 1.0 | Apache 2.0, 501B total and 23B active, 1M context, BF16 and FP8 and NVFP4 weights, free beta API | Text only, young provider ecosystem, third-party prices moved repeatedly |
| Mistral Large 4 | Natively multimodal, hybrid reasoning, official price, NVIDIA-backed engineering push | Weights not yet released, license unconfirmed, active-parameter figures conflict |
The pattern is clear. Muse Spark Open buys you capability and ecosystem at the cost of a custom license. Beam buys license freedom at the cost of vision. DeepSeek buys the lowest price at the cost of geopolitical and routing risk. Large 4 buys a European option, once it ships.
Verdict: Which Is the Best Local LLM Today?
For the strict question of the best local LLM you can download and run right now, Beam 1.0 is the safest first test. It has open weights, a permissive Apache 2.0 license, three precision formats and the smallest active parameter count of the downloadable options. If you need images, move up to Muse Spark Open, and have your lawyers read the Muse Model License first. If your goal is the lowest bill and you do not need self-hosting, rent DeepSeek V4.1 Flash and use the off-peak window for batch jobs.
Large 4 is the wildcard. If Mistral ships weights at the end of October under a permissive license, the ranking could change within a week. Until then, keep it on a watchlist and out of your architecture.
The numbers behind this verdict are modest, and we say so plainly. Muse Spark Open scored 38 of 43 on one benchmark. Large 4 beat a closed rival by one percentage point on a vendor test. Nobody has yet run all four under one suite. A two-day private eval on your own tasks will settle the question better than any chart in this article, and it costs a few dollars on hosted endpoints.
Frequently Asked Questions
What is the best local LLM in October 2026?
Among the four newest open-weight models, Beam 1.0 is the safest first download because of its Apache 2.0 license and 23 billion active parameters. Muse Spark Open scores higher on the one public benchmark we verified, but it uses a custom license.
Can I run Muse Spark Open on one computer?
Reports say it runs on multiple high-memory Mac Studios, with one account citing two 512-GB or four 256-GB systems. A single consumer PC cannot hold an 816-billion-parameter model.
Is DeepSeek V4.1 Flash open-weight?
Yes. The weights are on Hugging Face as deepseek-ai/DeepSeek-V4.1-Flash, and one report describes an MIT license. Confirm the license text in the repository before commercial use.
Are Mistral Large 4 weights available yet?
No. As of October 23, 2026, Large 4 is a paid public preview in Mistral Studio, and Mistral plans to release weights at the end of October. The license was still unknown as of October 16.
Which model is cheapest to use through an API?
DeepSeek V4.1 Flash, at $0.30 per million input tokens and $1.20 per million output tokens at peak, with off-peak rates at half. Beam’s Reflection API is free during its public beta.
Does Beam 1.0 support images?
No. Beam is text only. Muse Spark Open and Mistral Large 4 accept images.
What hardware do I need for models of this size?
As a rule of thumb, about one byte per parameter at FP8, so Beam needs roughly 500 GB for weights before context cache. That means a multi-GPU server or a cluster of high-memory Macs, not a laptop.
