Llama vs Mistral
Meta’s open-weight titan versus Europe’s Apache 2.0 champion. Two open-source philosophies. Two different visions for the future of AI. One definitive comparison.
TL;DR — The Quick Verdict
- Meta Llama is the ecosystem leader — the largest community, the widest deployment base, and the most recognizable name in open-weight AI. Llama 4 introduced natively multimodal MoE models with record-setting 10M-token context.
- Mistral AI is Europe’s open-source champion — delivering remarkable efficiency from a Paris-based startup. Mistral Small 4 unifies reasoning, vision, and coding in a single Apache 2.0 model with only 6B active parameters.
- On benchmarks, Llama 4 Maverick edges ahead on general knowledge (MMLU 83.2%) and multimodal tasks, while Mistral models excel at code generation (HumanEval 92%) and instruction following with shorter, more disciplined outputs.
- The critical licensing divide: Llama uses a custom community license with commercial restrictions (700M+ MAU threshold), while Mistral releases under Apache 2.0 — genuinely unrestricted for commercial use.
- For most developers in 2026, the choice depends on use case: Llama for multimodal applications and ecosystem support, Mistral for efficient self-hosting and truly open commercial deployment.
Two Visions of Open AI
The open-source AI landscape in 2026 is defined by two dominant forces — and they approach the problem from radically different positions. Meta, the trillion-dollar social media giant, releases Llama as a strategic play to democratize AI while keeping its ecosystem gravitational pull. Mistral AI, a three-year-old French startup valued at $14 billion, builds models designed to prove that European engineering can compete at the frontier while staying true to genuine open-source principles.
Meta Llama represents the corporate open-weight strategy. Backed by billions in compute and an army of researchers at Meta AI (formerly FAIR), Llama models are trained on massive infrastructure and released under a custom license that Meta calls “open source” but that the Open Source Initiative says is not. The goal is clear: flood the ecosystem with Meta’s weights, make Llama the default foundation, and let competitors build on Meta’s infrastructure instead of competing with it.
Mistral AI represents the startup challenger strategy. Founded by three researchers who left DeepMind and Meta to build something different in Paris, Mistral releases its models under the Apache 2.0 license — one of the most permissive and well-understood licenses in software. No usage thresholds, no acceptable use policies, no geographic restrictions. If you can run it, you can ship it.
— Arthur Mensch, CEO of Mistral AI
This philosophical divide shapes everything: how you can deploy these models, what commercial restrictions apply, and ultimately, which family belongs in your production stack.
How We Got Here
Meta Llama — The Corporate Open-Weight Play
Meta’s journey into open-weight AI started with the original LLaMA in February 2023 — a research release intended for academic use that quickly leaked to the public. Rather than fighting the leak, Meta leaned in. Llama 2 (July 2023) came with a commercial-use license, and the strategy was born: release powerful models to undermine the closed-source moats of OpenAI and Google, while ensuring Meta’s own AI infrastructure became the industry standard.
The pace accelerated. Llama 3 arrived in April 2024 with 8B and 70B models. Llama 3.1 (July 2024) pushed to 405B parameters with 128K context. Llama 3.2 added multimodal vision and lightweight models (1B to 90B). Llama 3.3 (December 2024) delivered a single 70B model trained to match 405B performance. Then came Llama 4 in April 2025 — a paradigm shift to Mixture-of-Experts architecture with natively multimodal models supporting up to 10 million tokens of context.
Meta invested over $30 billion in AI infrastructure in 2024 alone. By early 2025, Llama had become the most downloaded open-weight model family on Hugging Face, with Llama 3 variants alone crossing 350+ million downloads.
Mistral AI — The European Challenger
Mistral AI was founded in April 2023 by three researchers with impeccable pedigrees: Arthur Mensch, a former Google DeepMind researcher who spent nearly three years at Google’s AI laboratory; Guillaume Lample, one of the original creators of Meta’s LLaMA model; and Timothée Lacroix, also from Meta. The trio met during their studies at École Polytechnique, France’s most elite engineering school.
The founding story is remarkable. Within four weeks of incorporation, Mistral raised €105 million in seed funding — the largest seed round in European history at the time — on nothing but a pitch deck and the founders’ reputations. Their first model, Mistral 7B (September 2023), immediately proved the thesis: a 7.3-billion-parameter model that outperformed Llama 2 13B on every benchmark, released under Apache 2.0.
Growth was relentless. Mixtral 8x7B (December 2023) introduced the Mixture-of-Experts architecture to open-source AI. Mistral Large, Medium, and Small variants followed throughout 2024–2025. In December 2025, Mistral 3 launched an entire family under Apache 2.0, including the 675B-parameter Mistral Large 3. Then came Mistral Small 4 in March 2026 — a 119B MoE model unifying reasoning, vision, and coding with only 6B active parameters per token.
By early 2026, all three co-founders had become billionaires, with net worths of approximately $1.1 billion each. Mistral had grown from zero to one of the most consequential AI companies in the world in under three years — a trajectory rivaled only by OpenAI and Anthropic.
Complete Model
Comparison
Both families have expanded dramatically. Here is the full model lineup as of April 2026:
| Category | Meta Llama | Mistral AI |
|---|---|---|
| Flagship (Large) | Llama 4 Maverick (400B total, 17B active, 128 experts) | Mistral Large 3 (675B total, 41B active, MoE) |
| Efficient (Medium) | Llama 4 Scout (109B total, 17B active, 16 experts) | Mistral Small 4 (119B total, 6B active, 128 experts) |
| Previous Flagship | Llama 3.1 405B (dense, 128K context) | Mistral Large 2 (123B, dense) |
| Workhorse | Llama 3.3 70B (matches 405B quality) | Mistral Medium 3 (May 2025) |
| Small / Edge | Llama 3.2 1B, 3B | Ministral 3: 3B, 8B, 14B (dense) |
| Multimodal Vision | Llama 4 (native), Llama 3.2 11B/90B Vision | Pixtral Large (124B), Pixtral 12B |
| Code Specialist | Code Llama (7B–70B, legacy) | Codestral 25.01 (256K context), Devstral 2, Devstral Small 2 (24B) |
| Reasoning | Llama 4 Behemoth (2T, training) | Magistral Medium 1.2, Magistral Small 1.2 |
| Audio / Speech | — | Voxtral (speech understanding), Voxtral TTS (text-to-speech) |
| Max Context | 10M tokens (Scout) | 256K tokens (Small 4, Codestral) |
| Architecture | MoE (Llama 4), Dense (Llama 3.x) | MoE (flagship/efficient), Dense (edge) |
| License | Llama Community License | Apache 2.0 (open models) |
Meta Llama:
The Ecosystem Giant
Llama’s power lies in its ecosystem gravity. When Meta releases a model, the entire AI industry reorganizes around it. Hugging Face builds optimized inference, cloud providers race to offer it, and thousands of community fine-tunes appear within days. This network effect is Llama’s greatest asset — and it is something no other open-weight provider can match.
Llama 4: The MoE Revolution
Llama 4 marked Meta’s biggest architectural shift. Both Scout and Maverick use the Mixture-of-Experts (MoE) architecture, activating only 17B parameters during inference regardless of total model size. This means Llama 4 Scout (109B total) fits on a single NVIDIA H100 GPU, while delivering performance that surpasses all previous Llama generations.
Maverick takes this further with 128 expert pathways, enabling highly specialized internal routing depending on the prompt — whether it involves coding, image-to-text understanding, or long-context dialogue. Its 400B total parameters make it one of the largest openly available MoE models, and Meta claims it beats GPT-4o and Gemini 2.0 Flash across a broad range of benchmarks.
Then there is Behemoth: a 288 billion active parameter model with 16 experts and 2 trillion total parameters. Meta previewed Behemoth alongside the Llama 4 launch but noted it was still in training. When (or if) it ships, it could redefine the frontier of open-weight AI. On early benchmarks, Behemoth scores 82.2 on MMLU Pro — surpassing Gemini Pro’s 79.1.
— Meta AI blog, Llama 4 launch announcement (April 2025)
Mistral AI:
The Efficiency Pioneer
Mistral’s superpower is doing more with less. Where Meta throws compute at problems, Mistral engineers solutions. The result: models that achieve frontier-competitive performance with a fraction of the active parameters, making them cheaper to run, easier to self-host, and more practical for production deployment.
Mistral Small 4: Three Models in One
Released March 16, 2026, Mistral Small 4 is perhaps the most elegant model in the open-source landscape. It unifies three previously separate product lines into a single 119B-parameter MoE model: Magistral (reasoning), Pixtral (multimodal vision), and Devstral (agentic coding). Despite 128 experts and 119B total parameters, it activates only 6B parameters per token (8B including embedding and output layers).
The efficiency numbers are striking. Compared to Mistral Small 3, the new model delivers a 40% reduction in end-to-end completion time in latency-optimized setups, and handles 3x more requests per second in throughput-optimized configurations. On LiveCodeBench, it outperforms GPT-OSS 120B while producing 20% less output. On the Artificial Analysis LCR benchmark, Mistral Small 4 scores 0.72 with just 1.6K characters, while Qwen models need 3.5–4x more output for comparable performance.
Mistral Large 3: The Open-Weight Heavyweight
Released December 2, 2025, Mistral Large 3 is a 675B-parameter sparse MoE model with approximately 41B active parameters during inference. It is the largest open-weight MoE model released by a major lab under Apache 2.0, scoring 73.11% on MMLU-Pro and 93.60% on MATH-500 in independent evaluations.
The Specialist Arsenal
What truly distinguishes Mistral is its specialized model lineup. Codestral 25.01 offers a 256K context window for code generation with roughly twice the speed of the original. Devstral 2 and Devstral Small 2 (24B) target agentic coding, claiming better performance than Qwen 3 Coder Flash. Voxtral handles speech understanding while Voxtral TTS delivers text-to-speech with zero-shot voice cloning. Magistral models provide dedicated reasoning capabilities.
— Arthur Mensch, CEO of Mistral AI, McKinsey interview
The Numbers
Head to Head
Benchmark comparisons between Llama and Mistral are complicated by the wide range of model sizes. Here we compare the most directly competitive models in each tier.
Flagship Tier: Llama 4 Maverick vs Mistral Large 3
Efficient Tier: Llama 4 Scout vs Mistral Small 4
The efficiency comparison is revealing. Mistral Small 4 activates only 6 billion parameters per token — less than half of Llama 4 Scout’s 17B — yet achieves competitive results on coding and instruction-following benchmarks. This means Mistral Small 4 can run on significantly less hardware while delivering comparable quality for many tasks.
Code Generation
83.2%
92.1%
10M tokens
Native
92.0%
93.6%
6B active
3.5x shorter
The benchmark picture is nuanced. Llama leads on general knowledge and multimodal understanding, with Maverick’s 83.2% MMLU score surpassing comparable models. Mistral leads on code generation (92% HumanEval), mathematical reasoning (93.6% MATH-500), and output efficiency — producing comparable quality with significantly shorter, more focused responses. For instruction-following precision, where you need the model to do exactly what you say without extra commentary, Mistral models tend to be more disciplined than Llama.
The License Divide
That Matters
This is perhaps the most consequential difference between Llama and Mistral — and the one that matters most for production deployment. The choice of license affects what you can build, who you can sell to, and how you distribute your AI-powered products.
| Licensing Aspect | Meta Llama | Mistral AI (Open Models) |
|---|---|---|
| License Type | Llama Community License (custom) | Apache 2.0 (standard OSS) |
| Commercial Use | Allowed with restrictions | Unrestricted |
| MAU Threshold | 700M+ MAU requires special permission | No threshold |
| Acceptable Use Policy | Yes — restricts certain use cases | No — use for anything |
| Output Training Restriction | Cannot use outputs to train competing models | No restrictions on outputs |
| Geographic Restrictions | EU exclusions reported in recent versions | None |
| Redistribution | Allowed with license preservation | Allowed, no copyleft |
| Fine-Tuning | Allowed | Allowed |
| OSI-Approved | No — OSI explicitly says it is not open source | Yes — Apache 2.0 is OSI-approved |
| Training Data Transparency | Limited disclosure | Limited disclosure |
— Open Source Initiative, official blog post (2025)
This licensing difference has real-world implications. If you are building a commercial product with over 700 million monthly active users — think large social media platforms, global messaging apps, or major consumer services — you cannot use Llama without negotiating a separate agreement with Meta. Mistral’s Apache 2.0 models have no such ceiling.
For startups and mid-market companies, Llama’s license is practically fine — the 700M MAU threshold is unlikely to matter. But for enterprises with GDPR concerns, legal teams that prefer well-understood standard licenses, or companies philosophically committed to genuine open source, Mistral’s Apache 2.0 stance is a significant advantage.
When to Choose
Which Model
The practical advice comes down to three questions. First, what is your primary task? If it is multimodal (text + images) or requires extremely long context, Llama 4 is the clear winner. If it is code generation, mathematical reasoning, or speech processing, Mistral’s specialist models have the edge. Second, what is your hardware budget? Mistral Small 4’s 6B active parameters make it dramatically cheaper to self-host than models with higher activation counts. Third, do your legal or compliance teams care about license type? If you need genuine OSI-approved open source or operate in the EU with strict data sovereignty requirements, Mistral is the safer bet.
For fine-tuning specifically, both families are strong choices. Llama benefits from the largest community of LoRA adapters, quantized variants, and training recipes. Mistral benefits from its parameter efficiency — fine-tuning a 6B-active model is significantly cheaper than fine-tuning a 17B-active one, and the Apache 2.0 license means no restrictions on how you distribute your fine-tuned derivative.
The Network
Effect Battle
In open-source AI, the model is only part of the story. The ecosystem around it — tools, tutorials, fine-tunes, hosting providers, and community support — determines how useful the model is in practice.
Llama’s ecosystem is unmatched. With over 350 million downloads on Hugging Face, thousands of community fine-tunes, and first-class support from every major cloud provider (AWS Bedrock, Azure, GCP Vertex AI, Oracle, IBM), Llama is the default choice when organizations want an open-weight model with battle-tested tooling. Ollama, vLLM, llama.cpp, and text-generation-inference all prioritize Llama compatibility. If you need a specific fine-tune — medical, legal, financial, multilingual — someone in the Llama community has probably already built it.
Mistral’s ecosystem is smaller but growing fast. Mistral models are well-supported on Hugging Face, Ollama, and all major cloud platforms. The company also operates La Plateforme (its API service) and Le Chat (its consumer chatbot). Mistral’s partnership with Microsoft (Azure AI) and its presence on NVIDIA NIM and Baseten ensure broad deployment options. The community of Mistral fine-tunes is growing, but it remains a fraction of Llama’s volume.
Llama’s ecosystem advantage is real but narrowing. As Mistral raises more capital and expands partnerships — the recent $830M datacenter investment signals serious infrastructure ambitions — the gap is likely to continue shrinking. For now, if ecosystem maturity is your primary concern, Llama remains the safer choice.
Trust Issues &
Open Questions
Llama’s “Open Source” Debate
The most persistent controversy around Llama is Meta’s use of the term “open source.” The Open Source Initiative has explicitly and repeatedly stated that Llama’s community license is not open source by any accepted definition. The license restricts commercial use above 700M MAU, prohibits using model outputs to train competing AI systems, imposes an Acceptable Use Policy, and in recent versions has included geographic exclusions for EU users.
Critics call this “open washing” — using the positive connotations of open source for marketing while imposing proprietary-style restrictions. Meta’s defenders argue that the license is more permissive than most commercial AI models and that the 700M MAU threshold affects virtually no one outside the biggest tech companies. The debate continues, with implications for how the industry defines and regulates “open” AI.
Llama’s Strategic Shift: Muse Spark
In April 2026, Meta’s newly formed Superintelligence Labs released Muse Spark, a proprietary model that achieves comparable reasoning capabilities to Llama 4 Maverick with over an order of magnitude less compute. Muse Spark notably breaks with the Llama tradition by launching as a closed model, raising questions about Meta’s long-term commitment to the open-weight strategy. Some observers see this as Meta hedging its bets; others view it as a sign that the Llama era may be coming to an end.
Mistral’s Dual-Track Model
Mistral faces its own transparency challenge. While the company champions Apache 2.0 for its open-weight releases, not all Mistral models are open. The Mistral Large API, Le Chat premium features, and certain enterprise offerings are proprietary. Critics point out that Mistral markets itself on open-source credibility while increasingly building a commercial moat around its best models. The company’s growing focus on API revenue and enterprise contracts mirrors a path that could eventually deprioritize open releases.
Benchmark Reliability
Both families face questions about benchmark integrity. MMLU and HumanEval are increasingly considered saturated, with concerns about data contamination (models trained on test set data). Newer benchmarks like LiveCodeBench, SWE-bench Pro, and Artificial Analysis LCR attempt to address this, but the open-source community still lacks a universally trusted evaluation framework. Take all reported numbers with appropriate skepticism.
The Bigger
Landscape
Llama and Mistral are the two most prominent open-weight model families, but 2026 has seen the open-source AI landscape explode with formidable alternatives. Understanding the full picture helps contextualize what each family truly offers.
| Model Family | Origin | Key Strength |
|---|---|---|
| Qwen 3.5 (Alibaba) | China | 122B MoE, 10B active, multilingual champion, runs on 64GB MacBook |
| DeepSeek V3.2 | China | 685B total / 37B active, beats GPT-5 on reasoning, best open-source for agentic workloads |
| Gemma 4 (Google) | USA | 26B params, 14GB model size, 85 tok/sec on consumer hardware, beats Llama-405B on LMArena |
| Phi-4 (Microsoft) | USA | 14B “small language model” that beats larger models on reasoning |
| Llama (Meta) | USA | Largest ecosystem, multimodal MoE, 10M context, community license |
| Mistral (Mistral AI) | France | Efficiency leader, Apache 2.0, specialist models, European data sovereignty |
The 2026 open-source landscape has a clear macro trend: the MoE architecture has become dominant. DeepSeek, Qwen, Llama 4, and Mistral’s flagship models all use sparse expert routing to achieve high effective parameter counts while keeping inference costs low. The capability gap between open-weight and proprietary models has largely closed — and in specific domains (coding, reasoning), open-weight models now lead.
What remains different is the deployment trade-offs. Self-hosting requires infrastructure expertise, quantization knowledge, and ongoing maintenance. For organizations that want open-weight performance without the operational burden, API services from Mistral (La Plateforme), Meta (via cloud providers), and third parties like Together AI, Fireworks, and Groq offer turnkey inference at competitive per-token pricing.
— Open-source LLM survey, Q1 2026
Both Llama and Mistral face intensifying competition from Chinese open-source models. Qwen 3.5 and DeepSeek V3.2 offer comparable or superior performance under MIT/Apache licenses, with no geographic or usage restrictions. For developers primarily concerned with capability rather than brand loyalty, the Chinese models are increasingly compelling alternatives — though geopolitical considerations and supply chain risks add a layer of complexity for enterprise adoption.
The Bottom Line
You want the biggest ecosystem and broadest capabilities
Llama is the right choice when you need the largest community support, the widest range of pre-existing fine-tunes, and the most battle-tested deployment tooling. Llama 4’s native multimodal capabilities and record-setting 10M-token context window make it unmatched for applications that combine text and image understanding or process enormous documents. If your organization is not affected by the 700M MAU threshold and can live with Meta’s custom license, Llama offers the most well-rounded open-weight experience available. The risk: Meta’s pivot toward proprietary Muse Spark raises questions about Llama’s long-term trajectory.
You want genuine open source, efficiency, and specialization
Mistral is the right choice when licensing matters, hardware budgets are constrained, or you need specialized capabilities for code, reasoning, or speech. Mistral Small 4’s 6B active parameters deliver frontier-competitive performance at a fraction of the compute cost, and the Apache 2.0 license means zero legal ambiguity about commercial use. For European organizations with GDPR requirements, Mistral’s French headquarters and data sovereignty commitments add an additional layer of confidence. The complete specialist model lineup — Codestral, Devstral, Magistral, Voxtral — means you can build an entire AI product stack on one vendor’s models.
Evaluate Both for Your Specific Use Case
The open-source AI landscape in 2026 is too rich for one-size-fits-all answers. The smartest teams are benchmarking Llama, Mistral, Qwen, DeepSeek, and Gemma against their own data and use cases — not relying on public benchmarks alone. Tools like Promptfoo, LM Evaluation Harness, and custom evaluations on representative data will tell you which model family works best for your specific task, latency requirements, and hardware constraints. The good news: every option is strong, and switching costs between open-weight models are low.
Frequently Asked
Questions
No, by the accepted definition. The Open Source Initiative has explicitly stated that Llama’s Community License is not open source. It restricts commercial use above 700 million monthly active users, imposes an Acceptable Use Policy, and prohibits using outputs to train competing AI models. Meta uses the term “open source” in its marketing, but the license is more accurately described as “open weight” or “source available.” For most developers and companies under the MAU threshold, the practical difference is minimal — but for legal teams and organizations committed to genuine open source, this distinction matters.
Yes, for Mistral’s open-weight releases. Models like Mistral Small 4, Mistral Large 3, Codestral, and the Ministral family are released under Apache 2.0, which permits unrestricted commercial use, modification, and redistribution with no licensing fees. However, not all Mistral products are open — the Mistral API, Le Chat premium features, and certain enterprise services are proprietary. Always check the specific model’s license on Hugging Face or Mistral’s documentation.
Mistral has the edge for code-specific tasks. Mistral Large 2 scored 92% on HumanEval, and the dedicated Codestral and Devstral model families offer 256K context windows optimized for code. Mistral Small 4 outperforms GPT-OSS 120B on LiveCodeBench while producing shorter output. Llama models are competitive but lack a current dedicated code model — Code Llama has fallen behind. For general coding in a broader context, Llama 4 Maverick performs well but Mistral’s specialist approach gives it an advantage in pure code generation.
Mistral Small 4 is the efficiency champion, activating only 6B parameters per token despite 119B total parameters. It generates 80–100 tokens per second on suitable hardware and can run on consumer-grade GPUs with quantization. Llama 4 Scout, while impressive at fitting on a single H100 with 17B active parameters, still requires roughly 3x the compute per token. For resource-constrained deployments, Mistral’s efficiency advantage is substantial.
In April 2026, Meta’s Superintelligence Labs released Muse Spark, a proprietary model that achieves reasoning capabilities comparable to Llama 4 Maverick using over an order of magnitude less compute. Muse Spark breaks from the Llama tradition by being closed-source. While Meta has not officially discontinued Llama, this shift raises questions about the company’s long-term commitment to open-weight releases. Llama 4 models remain available and widely used, and Llama 4 Behemoth is still reportedly in training.
Both families support multiple languages, but with different strengths. Llama 3.1+ officially supports 8 languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai), while Llama 3.3 showed a 4.2-point improvement on the MGSM multilingual benchmark. Mistral models support multiple languages as well, with particular strength in French and European languages given the company’s French origins. For Asian language support, third-party models like Qwen (Alibaba) generally outperform both.
Llama 4 is the clear winner for multimodal applications. Scout and Maverick are natively multimodal, meaning text and image understanding is built into the base architecture rather than bolted on as a separate component. Mistral offers multimodal capabilities through Pixtral (a separate vision encoder added to its language models), but the approach is less integrated. For applications that heavily combine text and image processing, Llama 4 provides a more seamless experience.
Depending on your use case, yes. DeepSeek V3.2 (685B/37B active) beats GPT-5 on reasoning benchmarks and is excellent for agentic workloads. Qwen 3.5 (122B/10B active) is the strongest multilingual MoE model and runs on a MacBook. Google’s Gemma 4 (26B) beats Llama-405B on LMArena at 14GB model size. Microsoft’s Phi-4 (14B) excels at reasoning for its size. The “best” model depends entirely on your specific task, hardware, and licensing requirements. The beauty of the open-source landscape in 2026 is that you have genuine choices.
Both families support fine-tuning, and both have robust tooling for LoRA, QLoRA, and full-parameter training. Llama has the larger community of existing fine-tunes and training recipes, which can save significant time. Mistral’s advantage is cost: fine-tuning a 6B-active-parameter model is dramatically cheaper than a 17B-active model, and the Apache 2.0 license means no restrictions on distributing your derivative. For domain-specific applications (medical, legal, financial), both families serve as strong foundations.
Llama 4 Scout’s 10M-token context window is by far the largest, but achieving full performance at extreme context lengths requires substantial memory. For most practical applications, Llama 4 Maverick’s 1M-token context or Mistral Small 4’s 256K context is more realistic. Both are sufficient for processing very long documents, entire codebases, or multi-turn conversations. If your application specifically requires processing millions of tokens in a single pass, Llama 4 Scout is the only open-weight option.
Both Meta Llama and Mistral AI represent the best of what open-weight AI has to offer in 2026. Llama brings scale, ecosystem gravity, and native multimodal capabilities backed by one of the world’s largest technology companies. Mistral brings efficiency, genuine open-source licensing, and specialized models built by some of the researchers who helped create the very models they now compete against. The choice between them is not about which is better — it is about which is better for you.