Llama 3.1 405B Instruct: Complete Specifications, Pricing, API Access & Use Cases (2026)
What Is Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct is Meta’s open-weight, instruction-tuned multilingual large language model, released on July 23, 2024, featuring a 128K-token context window and text/code generation capability, with no single official Meta per-token API price confirmed as of June 2026.
Meta introduced Llama 3.1 405B as the largest model in the Llama 3.1 family, alongside upgraded 8B and 70B variants. The official model card describes the Llama 3.1 collection as pretrained and instruction-tuned generative models in 8B, 70B, and 405B sizes, with the instruction-tuned text-only models optimized for multilingual dialogue use cases.
Users typically search for Llama 3.1 405B Instruct to understand whether its open-weight distribution, long context window, and large parameter count make it suitable for enterprise experimentation, model distillation, synthetic data generation, coding assistance, multilingual assistants, or long-document workflows. Teams evaluating smaller deployment footprints may also compare it with Llama 3.1 70B and Llama 3.1 8B for cost and infrastructure trade-offs.
What Are Llama 3.1 405B Instruct’s Key Specifications and Pricing?
| Field | Verified Value |
|---|---|
| Provider | Meta (as of June 2026) |
| Model Family | Llama 3.1 (as of June 2026) |
| Model Type | Open-weight, instruction-tuned, autoregressive text large language model (as of June 2026) |
| Release Date | July 23, 2024 (as of June 2026) |
| Context Window | 128K tokens (as of June 2026) |
| Input Pricing | Not specified in official Meta documentation as a single per-token API price as of June 2026 |
| Cached Input Pricing | Not specified in official Meta documentation as of June 2026 |
| Output Pricing | Not specified in official Meta documentation as a single per-token API price as of June 2026 |
| Pricing Unit | Not confirmed from official Meta sources as of June 2026 |
| Modality Support | Text input; multilingual text and code output (as of June 2026) |
| Supported Input Types | Multilingual text (as of June 2026) |
| Supported Output Types | Multilingual text and code (as of June 2026) |
| API Access | Model weights/download via Meta and Hugging Face; hosted API availability depends on partner platforms (as of June 2026) |
| Model ID | meta-llama/Llama-3.1-405B-Instruct on Hugging Face (as of June 2026) |
| Availability | Downloadable through Meta’s Llama ecosystem and Hugging Face, subject to license terms (as of June 2026) |
| Knowledge Cutoff | December 2023 (as of June 2026) |
| Rate Limits | Not specified in official Meta model documentation as of June 2026 |
| Fine-tuning Support | Open-weight adaptation is described for pretrained models and additional-language use, subject to the Llama 3.1 Community License and Acceptable Use Policy (as of June 2026) |
| Streaming Support | Not specified as a Meta-hosted API feature in official model documentation as of June 2026 |
| Batch API Support | Not specified as a Meta-hosted API feature in official model documentation as of June 2026 |
| Tool / Function Calling | Meta describes tool-use capability, but exact API function-calling schema depends on the serving platform (as of June 2026) |
| Structured Output / JSON Mode | Not specified in official Meta model documentation as of June 2026 |
| License / Usage Restrictions | Llama 3.1 Community License and Acceptable Use Policy apply (as of June 2026) |
What Can Llama 3.1 405B Instruct Do That Makes It Useful in Production?
Llama 3.1 405B Instruct is useful when teams need an open-weight model with strong text, code, long-context, and multilingual capabilities, while accepting the infrastructure burden of serving a 405B-parameter model.
- It can support long-document summarization and retrieval-augmented workflows because Meta verifies a 128K context window for the Llama 3.1 family. The limitation is that long context does not guarantee perfect recall or reasoning across every token.
- It can support multilingual assistants in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, which are the supported languages listed in the official model card. Use outside those languages should be tested carefully because Meta discourages unsupported-language deployment without additional controls.
- It can support coding assistance and code-oriented text generation because the model card lists multilingual text and code as output modalities. Developers should still run generated code through review, testing, and security checks.
- It can support synthetic data generation and model distillation workflows, a use case Meta explicitly discusses for the 405B release. Teams comparing open-weight and hosted proprietary options often evaluate GPT-4o and Claude 3.5 Sonnet alongside Llama 3.1 405B for governance, cost, and deployment differences.
What Are Llama 3.1 405B Instruct’s Supported Modalities?
| Modality | Supported? | Notes |
|---|---|---|
| Text input | Yes | Official model card lists multilingual text input. |
| Image input | No | Llama 3.1 405B Instruct is described as text-only; image input is not listed for this model. |
| Audio input | No | Not listed in the official model card. |
| Video input | No | Not listed in the official model card. |
| Text output | Yes | Official model card lists multilingual text output. |
| Code output | Yes | Official model card lists code output. |
| Image/audio/video output | No | Not listed for Llama 3.1 405B Instruct. |
Where Does Llama 3.1 405B Instruct Fall Short?
Llama 3.1 405B Instruct has several limitations that matter in production.
- The first limitation is cost and infrastructure. Meta does not publish one universal per-token API price for the model, because the model is distributed as open weights and may be served through different infrastructure or partner platforms. Running a 405B-parameter model can require substantial GPU capacity, quantization, or managed hosting.
- The second limitation is modality. The official Llama 3.1 405B Instruct model is text-only, so teams that need native image, audio, or video input should compare it with multimodal models such as GPT-4o, whose OpenAI documentation lists text input/output and image input.
- The third limitation is knowledge freshness. The official model card lists a December 2023 pretraining data cutoff, so the model may need retrieval, tools, or updated context for current events and fast-changing domains.
- The fourth limitation is general AI reliability. This is a general AI limitation and is not unique to Llama 3.1 405B Instruct: outputs can be inaccurate, biased, incomplete, or unsafe if prompts, retrieval data, system design, and human review are weak. Meta’s model card also says developers should perform safety testing and tuning tailored to their applications before deployment.
What Is Llama 3.1 405B Instruct Best Used For?
| Use Case | Why Llama 3.1 405B Instruct May Fit | Important Limitation |
|---|---|---|
| Long-document summarization | 128K context window supports larger prompts and documents. | Long context still requires evaluation for recall and factuality. |
| Multilingual assistants | Supports eight listed languages including English, French, German, Hindi, Spanish, Portuguese, Italian, and Thai. | Unsupported languages need additional testing and safeguards. |
| Code assistance | Official output types include text and code. | Generated code needs review, testing, and security validation. |
| Synthetic data and distillation | Meta describes using Llama model outputs to improve other models. | License and quality-control requirements still apply. |
| Open-weight experimentation | Teams can inspect, host, adapt, and evaluate the model under license terms. | Serving 405B parameters can be expensive and operationally complex. |
How Does Llama 3.1 405B Instruct Compare to GPT-4o and Claude 3.5 Sonnet?
| Comparison Area | Llama 3.1 405B Instruct | GPT-4o | Claude 3.5 Sonnet | Scenario Fit |
|---|---|---|---|---|
| Provider | Meta | OpenAI | Anthropic | Useful when provider governance matters. |
| Distribution | Open-weight model release | Hosted OpenAI API model | Hosted Anthropic API model | Llama may fit teams needing open-weight control; hosted models may fit managed API workflows. |
| Context window | 128K tokens | 128K context is referenced in OpenAI’s GPT-4.1 comparison to GPT-4o; current pricing docs list GPT-4o pricing and modalities. | 200K tokens in Anthropic’s Claude 3.5 Sonnet announcement. | Claude 3.5 Sonnet may fit longer hosted-context workflows; Llama 3.1 405B may fit open-weight long-context workflows. |
| Modalities | Text input; text/code output | Text input/output and image input listed in OpenAI API docs. | Primarily text and vision-capable Claude workflows, depending on API version and product surface. | GPT-4o may fit image-input workflows; Llama 3.1 405B fits text-only deployments. |
| Pricing | No single official Meta per-token API price confirmed | OpenAI docs list GPT-4o at \$2.50 input and \$10.00 output per 1M tokens. | Anthropic’s announcement listed \$3 input and \$15 output per 1M tokens. | Hosted pricing is easier to forecast; self-hosted Llama cost depends on infrastructure. |
| Control | Higher deployment control under license | Managed API | Managed API | Llama may fit teams that need model-weight access; hosted models may reduce operations work. |
How Do I Access Llama 3.1 405B Instruct?
Developers can access Llama 3.1 405B Instruct through Meta’s Llama distribution channels and the Hugging Face model repository, subject to Meta’s license and acceptable use terms. Meta’s release announcement says the Llama 3.1 models are available for download on Llama’s site and Hugging Face, and the Hugging Face repository identifies the Instruct model as meta-llama/Llama-3.1-405B-Instruct.
This page does not include executable provider API code because Meta’s official Llama 3.1 model card does not specify a single hosted API endpoint, authentication method, request schema, and response schema for Llama 3.1 405B Instruct as of June 2026. Hosted API syntax and token pricing vary by serving provider, cloud platform, and deployment method.
FAQs
What is the context window of Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct has a verified 128K-token context window as of June 2026, according to Meta’s official Llama 3.1 model card.
How much does Llama 3.1 405B Instruct cost?
Meta does not publish one universal official per-token API price for Llama 3.1 405B Instruct as of June 2026. Costs depend on whether teams self-host it or use a managed provider.
Does Llama 3.1 405B Instruct have an API?
Meta provides model access through downloads and ecosystem platforms, but no single official Meta-hosted API endpoint and request schema were confirmed as of June 2026.
What is Llama 3.1 405B Instruct useful for?
It is relevant for long-context text tasks, multilingual assistants, code generation, synthetic data generation, model distillation, and open-weight experimentation, provided teams can manage infrastructure and safety evaluation.
