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Mistral Large 4 was not yet available as a downloadable model on October 7, 2026. Mistral announced it as a public-preview API on October 6 and said it planned to release the weights by the end of October. Until those weights and their deployment terms are published, people choosing a model to run locally should evaluate currently available alternatives instead—without treating any one family as a universal winner.
Which alternatives can you use now?
The most relevant candidates supported by current official sources are Meta’s Llama 4 Scout and Maverick, Alibaba’s Qwen3.8 releases, and several open-weight models in Mistral’s own catalog. They differ in what is established about their size, licensing, and distribution, so compare specific downloadable checkpoints rather than choosing by family name alone.
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| Candidate | What is established | What to verify before choosing |
|---|---|---|
| Llama 4 Scout and Maverick | Meta describes both as natively multimodal mixture-of-experts models and publishes model cards with parameter and context figures. | Check the Llama 4 Community License and applicable use policy, then test the exact checkpoint, quantization, runtime, and hardware you plan to use. |
| Qwen3.8 | Alibaba’s official repository identifies releases including Qwen3.8-27B and says weights are available through Hugging Face Hub or ModelScope. | Use the individual model page and the license file distributed with that checkpoint to confirm its specifications, terms, hardware requirements, and runtime support. |
| Mistral Large 3, Small 4, and Ministral 3 variants | Mistral’s catalog checked October 7, 2026 lists these alongside Large 4. It describes Large 3 as open-weight and general-purpose multimodal, and Small 4 as a hybrid instruction, reasoning, and coding model. | Confirm the individual release page, downloadable artifacts, current license, and compatibility with your serving stack. The catalog lists Apache 2.0 for Large 3 and Small 4. |
These are candidates, not a benchmark ranking. A family-level description cannot establish that a particular checkpoint will meet your requirements for accuracy, speed, context, or commercial use.
What Llama 4’s published specifications do—and do not—tell you
Meta’s Llama 4 model card gives the following figures for Scout and Maverick. They are publisher specifications, not a guarantee of performance on every task or deployment.
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- 4 × M.2 PCIe 4.0 + 4 × DDR5 SODIMM slots:Four DDR5 SODIMM slots support up to 256 GB of memory, while ECC helps maintain data integrity in mission-critical environments. Four PCIe 4.0 M.2 slots support up to 24 TB of storage, supporting RAID 0/1/5/10, combining high-speed performance with data protection. It allows for the creation of independent scratch disks, media libraries, and project drives, providing high-throughput for production workflows.
- PCIe & USB 4.0 v2: Up to three PCIe slots can be equipped, including a dual-slot x16 GPU. The main slot supports PCIe 5.0, meeting the needs of high-bandwidth creative and computing workloads. USB 4.0 v2 (80Gbps) supports high-bandwidth external storage and displays.
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- Server-grade thermal architecture: Utilizing a dedicated CPU/GPU airflow design, equipped with a 6-pipe dual-fan cooler, it maintains stable performance even under sustained loads, delivering up to 140W Turbo power while maintaining a 100W TDP, and operating with noise levels as low as 36 dB. An integrated 350W power supply ensures stable and reliable output for demanding computing tasks and fully loaded extended configurations.
| Model | Activated parameters | Total parameters | Listed context |
|---|---|---|---|
| Llama 4 Scout | 17 billion | 109 billion | 10 million tokens |
| Llama 4 Maverick | 17 billion | 400 billion | 1 million tokens |
Meta says Scout can fit on a single H100 GPU with on-the-fly int4 quantization. That is a vendor claim under a specific quantization condition; it does not establish speed, concurrency, or fit on another GPU or serving stack. In particular, 17 billion activated parameters should not be read as a 17-billion-parameter memory requirement: the same model card lists much larger total parameter counts. Quantization, context length, batch size, runtime, and offloading also affect memory use.
The listed context lengths are advertised limits, not proof that a model will reliably handle useful work across the full window. Test the length and quality you actually need.
Check Llama’s terms before a commercial deployment
Llama 4 is not licensed under Apache or MIT. Meta’s Llama 4 Community License has conditions for use and redistribution, including attribution requirements and a special license condition for products with more than 700 million monthly active users. Read the agreement and applicable use policy for your specific deployment rather than assuming “open weights” means unrestricted commercial use.
What Mistral has announced about Large 4
In its October 6, 2026 announcement, Mistral describes Large 4 as a natively multimodal model with 1 trillion total parameters and 49 billion active parameters. Mistral says it was trained on 3,800 NVIDIA Grace Blackwell GPUs in its European datacenters and on multilingual data spanning more than 160 languages. These are company-published specifications and claims, not independently reproduced findings. Mistral said it would publish further architecture details and methodology alongside the weights.
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The announcement states, “We will release the weights by the end of the month.” That is Mistral’s October 6 plan, not confirmation that the weights are already downloadable. Release timing and the availability of a model card can change; confirm them before making a deployment decision.
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Mistral reported the following scores in 2026. They are not independent head-to-head tests, and the announcement’s results should not be treated as a general ranking of assistant quality or local inference speed.
| Benchmark or test | Mistral-reported result | Interpretation limit |
|---|---|---|
| DeepSWE v1.1 | 61.7% | A reported benchmark result; not a measure of general assistant quality. |
| SWE-Atlas-QnA | 59.4% | A reported benchmark result; it does not establish local speed or reliability. |
| Terminal-Bench 4 | 28.3% | A reported benchmark result; it does not establish general coding-agent performance. |
| Mistral’s combined Coding Agent Index | 49.8% | A Mistral-reported combined score; the announcement’s further methodology details were to accompany the weights. |
| Vulnerability reproduction-and-patching test | 82% | A Mistral-reported result on this test, not a general security guarantee. |
| Cybench | 93% | A Mistral-reported benchmark result, not evidence of security in a deployed system. |
| AutomationBench | 59.9% across 657 business workflows | A Mistral-reported result; it does not predict performance on your organization’s workflows. |
How to choose for local or self-hosted use
Start with your constraints and task, then test exact artifacts. A model that loads successfully can still miss your requirements for context, concurrency, or latency; the comparison method should account for the complete deployment setup.
- Availability: Confirm that the exact checkpoint is downloadable now. Record its repository, revision, file format, and quantization rather than relying on an announcement or family label.
- License and governance: Read the terms attached to the exact weights and any applicable use policy. Open weights do not, by themselves, establish open training data, permissive redistribution, or unrestricted commercial use.
- Workload: Test representative tasks from your own coding, document, vision, reasoning, language, or tool-use work. A score on one published benchmark is a narrow signal, not a universal ranking.
- Hardware and serving: Account for total model size, quantization, context length, batch size, KV cache, runtime support, and offloading. Active-parameter counts alone cannot tell you whether a model will fit or perform well.
- Operational quality: Measure latency and throughput on the target system. Also check structured-output reliability, tool execution, failure recovery, update process, and cost per accepted result.
A practical comparison you can reproduce
- Define the job. Choose a small set of representative prompts and acceptance criteria for the task you care about, such as correct code changes, reliable tool calls, or accurate document answers.
- Fix the deployment conditions. Use the same hardware, runtime where supported, context, concurrency, prompt set, and generation settings for each candidate. Record any differences that cannot be held constant.
- Identify each artifact. Write down the model revision, checkpoint format, quantization, and license. This keeps the result tied to a version rather than an evolving model-family name.
- Measure the outcomes that matter. Track quality against your acceptance criteria alongside latency, throughput, memory use, and failure rates. For agent loops, include whether tool calls succeed and whether errors recover correctly.
- Choose against your constraints. A single-GPU hobbyist, an organization with multiple accelerators, and a team with specific licensing or regional requirements may reasonably pick different models. Prefer the candidate that passes your tests and fits your operating conditions.
Which model should you try first?
If you want a currently documented open-weight candidate, begin by comparing the exact Llama 4 or Qwen3.8 checkpoints that fit your task and terms; also consider Mistral Large 3, Small 4, or Ministral 3 if their model cards and deployment requirements suit your setup. The evidence available here does not establish a universal winner or a hardware-fit ranking for Qwen3.8. Treat Large 4 as an announced future local option until its weights are actually released, then evaluate its exact checkpoint under the same conditions as the alternatives.
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