Best Open-Source LLMs: Llama 4, Qwen3.8, DeepSeek V4.1, Mistral

TL;DR

In May 2026 the open-weight LLM landscape has four serious flagships: Llama 4 (Meta), Qwen 3 (Alibaba), DeepSeek-V3 (DeepSeek), and Mistral Large 3 (Mistral AI). We compared all four on reasoning, coding, context handling, multilingual ability, and the real cost of running them — using each lab's published benchmark results, open leaderboards, and reports from teams deploying them.

Short version: the open-vs-closed gap narrowed to roughly 6-9 months. Open weights now handle most production workloads. Llama 4 wins ecosystem, Qwen 3 wins breadth and multilingual, DeepSeek-V3 wins reasoning per dollar, Mistral Large 3 wins clean enterprise licensing.

Updated 22 October 2026. Qwen 3 is now Qwen3.8 and DeepSeek-V3 is now DeepSeek V4.1,

with licence strings re-read from each model's Hugging Face card. Mistral Large 3 stays:

Large 4 was announced on 6 October but its weights have not been released. Llama 4 is

unchanged.

Why Open-Weight Models Matter in 2026

Three reasons open weights matter more in 2026 than they did in 2024:

1. The quality gap shrank. Open-weight flagships now match the closed frontier from roughly two-thirds of a year prior. For most workloads, that is good enough.
2. Data control. Regulated industries and privacy-sensitive products can run open models entirely on their own infrastructure — no data leaves the building.
3. Cost and lock-in. Open weights remove per-token API dependency on a single vendor and give a path to lower cost at scale.

The trade-off is operational: you (or a provider you choose) are responsible for running the model. That demand for GPUs is large enough that it has spawned decentralized alternatives to the hyperscalers — see our guide to DePIN and decentralized physical infrastructure, where shared GPU networks are emerging as one way to source inference compute.

How We Compared

We rated each model on:

• Reasoning — math, logic, multi-step problem solving
• Coding — standard code-generation and bug-fix benchmarks
• Context — accuracy at long context lengths
• Multilingual — quality across English, Chinese, Spanish, Arabic, Hindi
• Cost — inference cost at production volume (hosted and self-hosted)
• License — what you are actually allowed to do

The evidence base: benchmark results published by each lab, open leaderboards and independent evaluations, hosted-provider pricing, and deployment reports from teams running these models. Published benchmarks are directional, not absolute — always test on your own workload before committing.

The Scoreboard

Licence strings above are the values on each model's Hugging Face card, read 22 October 2026.

1. Llama 4 — Best Ecosystem

Best for: Teams that want the lowest-friction open model

Llama 4 is not always the top scorer, but it has the strongest ecosystem of any open-weight model — the most fine-tuned variants, the broadest quantization support, first-class support in every inference engine, and the deepest pool of tutorials and community knowledge. For most teams, "everything supports Llama" outweighs a few benchmark points.

• Largest ecosystem: Most fine-tunes, quantizations, and tooling
• Universal engine support: Works everywhere — vLLM, llama.cpp, TGI, Ollama
• Deep community: The most documented open model to deploy and debug
• Strong general performance: Very good across reasoning and coding

Limitations: Ships under Meta's community license, which adds usage restrictions above a large user threshold and acceptable-use terms — read it before assuming "free for anything."

2. Qwen3.8 — Best All-Rounder, With a Licence Split

Best for: Multilingual products, coding, broad workloads

Qwen3.8 from Alibaba is the strongest all-round open-weight family. The flagship, Qwen3.8-2.4T-A95B, is a mixture-of-experts model with 2.4 trillion total and 95 billion activated parameters, and its card describes it as the first time Qwen has brought a Qwen-Max-class model to open release. Context is 262,144 natively, extensible to 1,010,000.

• Best multilingual: Leads on non-English, especially Asian languages
• Widest size range: From a 27B dense model to a 2.4T MoE flagship
• Long context: 262K native on both, extensible to roughly 1M
• 27B is multimodal: The smaller model takes image input; the flagship is text-only

Limitations: The licence split is the thing to check before building. Qwen3.8-27B is apache-2.0. The 2.4T flagship is not — it ships under a custom qwen3.8-max licence. The flagship also cannot have thinking disabled, which matters if you need short deterministic outputs.

3. DeepSeek V4.1 — Best Reasoning Per Dollar, and the Cleanest Licence

Best for: Reasoning-heavy workloads on a budget

DeepSeek-V4.1-Flash is a multimodal mixture-of-experts model with a 552B backbone that activates only 8B parameters per token during prefill and 16B during decode. That is the whole argument: near-frontier behaviour at a fraction of the memory and inference cost. Its card puts the global KV cache at roughly 890 bytes per token, about a quarter of DeepSeek-V4-Flash. A larger DeepSeek-V4-Pro is also published.

• Best reasoning value: Very low activated-parameter count for the capability
• Genuinely open licence: mit on the card and in the repository text, the most permissive here
• 1M context: Supported, with a controllable reasoning-effort setting from 1 to 100
• Multimodal: Ships with a vision encoder

Limitations: MoE is harder to self-host efficiently than a dense model, and the cost advantage depends on an inference stack that routes experts well. The headline parameter count also flatters it — 552B of weights still has to sit somewhere, even if only 8 to 16B are active per token.

4. Mistral Large 3 — Best for EU Enterprise

Best for: European enterprises, clean licensing requirements

Mistral Large 3 is the pick when licensing and jurisdiction matter as much as benchmarks. Mistral is EU-based, and its open models ship under Apache-style licensing with minimal restrictions — which legal and procurement teams at regulated European enterprises strongly prefer.

And it is still the current one, which is the surprise. Mistral announced Mistral Large 4 on 6 October 2026, a 1-trillion-parameter model with 52 billion active, priced at $1.36 input and $4.18 output per million tokens. But that announcement describes a public preview API and says the weights will be released by the end of the month. As of 22 October there is no Mistral-Large-4 repository in Mistral's Hugging Face organisation, and the newest Mistral Large weights published there are still Large 3. For an open-weights comparison, Large 4 does not qualify yet.

• Cleanest licensing: Apache-style, minimal restrictions
• EU jurisdiction: Data-residency and regulatory alignment for European firms
• Strong general performance: Very good across reasoning and coding
• Efficient: Competitive performance at moderate model sizes

Limitations: Does not lead any single benchmark category outright. The value is the combination of solid performance plus genuinely unrestricted licensing.

Self-Hosting vs API: The Real Math

A common mistake is assuming "open model" means "self-host." They are separate decisions.

You can use Llama 4, Qwen 3, DeepSeek-V3, or Mistral Large 3 through a hosted API provider — getting open-model economics and no vendor lock-in without running any GPUs yourself. We break down the fastest and cheapest of these in our LLM inference providers comparison, which pits Groq, Cerebras, Together, and Fireworks against each other on speed and price.

Actually self-hosting (your own GPUs) only beats hosted pricing above roughly 2-3 million tokens per day of sustained usage. Below that threshold:

• GPU rental or purchase cost exceeds API spend
• Idle capacity is wasted money
• Engineering time to run inference reliably is a real, ongoing cost

Above that threshold, self-hosting wins — and the bigger your sustained volume, the more it wins.

For most teams: use an open model via an API provider first, and only move to self-hosting when sustained volume clearly justifies it. For running models locally for development or privacy, see our local LLM guide.

Open vs Closed: Where Each Wins

For the closed-frontier side of this comparison, see our frontier model head-to-head.

Read the License

• Genuinely open — DeepSeek V4.1 carries mit, stated both in the card metadata and the repository text. Qwen3.8-27B carries apache-2.0. Mistral Large 3 is Apache-style. These are free for commercial use with minimal restrictions.
• Custom licences — Llama 4 Maverick ships under llama4, the Llama 4 Community License Agreement, which carries a 700 million monthly-active-user threshold and requires Built with Llama attribution. Qwen's flagship uses a custom qwen3.8-max licence while its 27B sibling is Apache 2.0 — same family, different terms.
• Not open at all yet — Mistral Large 4 is an API preview with no published licence and no released weights.

The direction of travel is worth noting. The usual assumption is that open weights get more permissive over time. Across this set it has gone the other way: Qwen's most capable model moved off Apache onto a custom licence, and Mistral's newest flagship has not shipped weights at all. The permissive end of this table is now held by DeepSeek and by the smaller Qwen.

Conclusion

Where each model lands in May 2026:

• Best ecosystem and lowest friction: Llama 4
• Best all-rounder and multilingual: Qwen 3
• Best reasoning per dollar: DeepSeek-V3
• Best for EU enterprise and clean licensing: Mistral Large 3

The bigger story is that open-weight models closed most of the gap. For the majority of production AI workloads in 2026, an open model is not a compromise — it is the sensible default. Reserve the closed frontier for the hardest reasoning and the longest agentic tasks.

Start with Llama 4 because everything supports it, and switch to Qwen 3, DeepSeek-V3, or Mistral Large 3 when a specific constraint — multilingual, reasoning cost, or licensing — makes one of them the better fit. For running these models yourself, our local LLM guide covers the practical setup.

The distribution layer these models rely on changed hands in September 2026: NVIDIA agreed to acquire Hugging Face, the registry where nearly all of them are published.