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Llama 3.1 is the better default choice when comparing equivalent sizes: Llama 3.1 8B against Llama 3 8B, or 70B against 70B. It expands the context limit from 8,192 to up to 128,000 tokens, adds a 405B tier, broadens documented language support, and places more emphasis on tool use, coding, reasoning, and instruction following. Meta released Llama 3.1 on July 23, 2024, after Llama 3 launched on April 18, 2024. (Meta announcement; Llama 3.1 model card)
That does not make every response better or make Llama 3 obsolete. A stable, short-context English application may not justify migration, while long documents, multilingual workflows, agents, and new projects usually benefit from Llama 3.1. In 2026, also check whether your chosen host still supports the older release: AWS, for example, lists Llama 3.1 405B as legacy with a July 7, 2026 end-of-life date. (AWS model card)
Quick comparison
| Feature | Llama 3 | Llama 3.1 |
|---|---|---|
| Release | April 18, 2024 | July 23, 2024 |
| Sizes | 8B and 70B | 8B, 70B and 405B |
| Maximum context in the model documentation | 8,192 tokens | Up to 128,000 tokens |
| Language positioning | English-focused intended use | Eight explicitly supported languages, with unequal performance requiring testing |
| Model types | Pretrained and instruct | Pretrained and instruct |
| Tool use | Possible through integrations | More prominently documented and supported |
| License | Llama 3 Community License | Llama 3.1 Community License |
Source details are in Meta’s Llama 3 model card and Llama 3.1 model card.
Compare like with like
The fair upgrade tests are Llama 3 8B Instruct versus Llama 3.1 8B Instruct, and Llama 3 70B Instruct versus Llama 3.1 70B Instruct. A base model is designed for continuation or fine-tuning; an instruct model is tuned for following requests. Comparing Llama 3 Base with Llama 3.1 Instruct confuses model-generation changes with tuning differences.
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Llama 3.1 405B has no direct Llama 3 counterpart. It is a separate scale class with radically different hardware, latency, and budget requirements, not a drop-in replacement for 70B.
What changed in Llama 3.1?
A 16-times larger nominal context
Llama 3 documents an approximately 8K-token context; Llama 3.1 supports up to 128K. That makes the newer family much more suitable for manuals, contracts, repositories, long conversations, and retrieval batches. The maximum is not a promise of perfect retrieval: relevant information can be missed in a long prompt, and input plus output limits depend on the serving system. Long prompts also increase compute, memory, and often price. A provider may expose a smaller cap; Groq, for example, currently documents 131,072 tokens for its Llama 3.1 8B endpoint. (Groq model documentation)
Broader language and capability goals
Meta explicitly names English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai for Llama 3.1. That is documented support, not equal quality in every language. Test grammar, translation direction, cultural references, terminology, token usage, and safety behavior for the languages you actually serve. Meta also reports improvements across general knowledge, steerability, mathematics, coding, multilingual translation, reasoning, instruction following, and tool use. These are vendor-reported evaluations across more than 150 datasets, so treat them as an aggregate signal rather than a guarantee for your prompts. (Meta’s evaluation overview; evaluation details)
A new capability tier
The 405B model raises the quality ceiling within this comparison, but openly downloadable weights are not the same as affordable inference. It is generally a data-center or specialist-hosting choice, not a normal laptop model.
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Llama 3.1 8B versus Llama 3 8B
At the same nominal parameter count, base weight memory can be similar at the same precision. Llama 3.1 8B nevertheless offers the 128K specification, broader language positioning, and newer tool-use behavior. Those advantages matter for document processing, coding agents, multilingual assistants, and structured workflows.
The older 8B can still be sensible when an existing quantized deployment is fast, tested, and short-context English-only. Remember that actually using a very long context adds KV-cache and runtime memory; a 128K-capable checkpoint does not make 128K cheap on a laptop. Quantization method, GPU offload, RAM bandwidth, backend, batch size, and requested tokens per second determine the experience.
Llama 3.1 70B versus Llama 3 70B
The 70B-to-70B comparison favors Llama 3.1 for overall capability, long-context work, multilingual tasks, coding, and tool-oriented applications. Serving costs and operational complexity remain substantial, so measure the quality gain against latency and volume economics. Re-test prompts, adapters, chat templates, structured output, and safety filters before switching a production endpoint.
Coding, reasoning, and tool calling
Coding
Llama 3.1 is the stronger candidate for code explanation, generation, and repository-level assistance because its documented context and reported coding improvements allow more surrounding code to be supplied. Results still depend on quantization, prompt format, and whether the provider implements structured output correctly. Test completion, debugging, explanation, and whole-repository retrieval separately.
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Mathematics and reasoning
Meta reports stronger mathematics and reasoning results, particularly for larger variants, but benchmark gains do not guarantee reliable arithmetic or formal proofs. Verify important numbers with a calculator or program, and evaluate the exact checkpoint, decoding settings, and quantization you will deploy. (Meta evaluation details)
Tool use
Llama 3.1 can be a better foundation for search agents, database assistants, API orchestration, extraction pipelines, and coding agents. The model does not execute tools itself. Your application must define schemas, parse and validate calls, execute functions, return results, and ask the model to continue. Provider-specific chat templates can silently alter this path, so test the complete application rather than a raw prompt. (Hugging Face model page)
Hardware and local deployment
- 8B: the practical local option for many users, especially with a suitable quantization.
- 70B: usually needs high-memory consumer hardware, multiple GPUs, or hosted inference.
- 405B: normally requires specialized or data-center infrastructure.
There is no honest single minimum-memory number. Precision, quantization, context length, batch size, KV-cache settings, runtime, and speed target all change requirements. Quantized files also differ in quality and performance; a fixed percentage loss cannot be assumed. A smaller Llama 3.1 model may beat an older larger model on one task, but not necessarily on difficult reasoning or coding.
Licensing and production safety
Meta describes Llama as an open model, but Llama 3.1 is openly downloadable under a custom Llama 3.1 Community License rather than a conventional permissive license such as Apache 2.0. Review the license, Acceptable Use Policy, attribution and notice duties, distribution rules, and any restrictions relevant to your service before commercial deployment. (Llama 3.1 license)
Meta’s ecosystem includes Llama Guard 3, Prompt Guard, and CyberSecEval 3. These tools do not make an unmodified model automatically safe. Production systems still need prompt-injection defenses, least-privilege tools, output validation, PII controls, jailbreak testing, logging, and human escalation. (Meta responsible-AI announcement)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which model should you choose?
- New project: start with Llama 3.1 at the smallest size that passes your evaluation, unless your provider has discontinued it or offers materially better support for another model.
- Existing Llama 3 deployment: migrate when long context, multilingual input, tool use, or instruction quality matters; retain Llama 3 when compatibility and proven economics matter more.
- Long documents, codebases, retrieval, or agents: prefer Llama 3.1, subject to the host’s actual context limit.
- Simple short English chat: Llama 3 may remain adequate.
- Maximum capability in this pair: consider 405B only when evaluation justifies its infrastructure and a supported host is available.
- Local budget: compare quantized 8B builds using your real prompts; do not buy hardware from parameter count alone.
- Production in 2026: check lifecycle, region, pricing, rate limits, context caps, retention terms, and successor models before committing.
Hosted versus local options
Hosted inference is the fastest way to test quality without purchasing hardware. Groq currently lists Llama 3.1 8B at $0.05 per million input tokens and $0.08 per million output tokens on its pricing materials, while OpenRouter displays provider-dependent prices around $0.02 input and $0.04 output per million tokens for its Llama 3.1 8B listing. Prices, routing, caching, and availability can change. (Groq pricing; OpenRouter listing)
Hugging Face weights suit self-hosting and fine-tuning, while Ollama offers a simple local entry point. AWS Bedrock is useful for AWS governance and regional integration, but its model cards direct users to current Bedrock pricing and lifecycle information. Review data-retention, region, and enterprise terms before sending sensitive material. (Hugging Face models; Ollama library; Bedrock pricing)
Migration checklist
- Confirm you are comparing the same size and Base or Instruct variant.
- Verify the tokenizer, chat template, context limit, and maximum output exposed by your runtime.
- Re-test JSON, structured output, tool calls, and argument validation.
- Run representative multilingual, coding, retrieval, and safety evaluations.
- Measure latency, memory, throughput, and total cost with the intended quantization and hardware.
- Review the Llama 3.1 license, provider terms, model lifecycle, and rollback plan.
Do not ignore newer alternatives
Llama 3.1 is the winner of this direct generation comparison, not automatically the best model available in 2026. Compare current Llama generations and other open-weight models on your own workload, especially if a provider marks Llama 3.1 as legacy or limits its context and tool APIs.
The Bottom Line
Bottom line: choose Llama 3.1 for an equivalent-size upgrade, especially for 128K-class context, multilingual applications, tool use, and stronger reported reasoning. Keep Llama 3 when its short-context English workload, compatibility, price, or availability makes migration unjustified. The winning deployment is determined by size, quantization, infrastructure, license, and current provider support—not the family name alone.
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