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GPT-5 vs Llama 3.1 405B Instruct

GPT-5 (2025) and Llama 3.1 405B Instruct (2024) are compact production models from OpenAI and AI at Meta. GPT-5 ships a 128K-token context window, while Llama 3.1 405B Instruct ships a 128K-token context window. On pricing, GPT-5 costs $1.25/1M input tokens versus $2.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

GPT-5 is ~92% cheaper at $1.25/1M; pay for Llama 3.1 405B Instruct only for provider fit.

Specs

Released2025-01-212024-07-23
Context window128K128K
Parameters405B
Architecturedecoder onlydecoder only
LicenseProprietaryOpen Source
Knowledge cutoff--

Pricing and availability

GPT-5Llama 3.1 405B Instruct
Input price$1.25/1M tokens$2.4/1M tokens
Output price$10/1M tokens$2.4/1M tokens
Providers

Capabilities

GPT-5Llama 3.1 405B Instruct
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5, multimodal input: GPT-5, function calling: GPT-5, tool use: GPT-5, and code execution: GPT-5. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, GPT-5 lists $1.25/1M input and $10/1M output tokens, while Llama 3.1 405B Instruct lists $2.4/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 405B Instruct lower by about $1.48 per million blended tokens. Availability is 2 providers versus 11, so concentration risk also matters.

Choose GPT-5 when coding workflow support and lower input-token cost are central to the workload. Choose Llama 3.1 405B Instruct when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, GPT-5 or Llama 3.1 405B Instruct?

GPT-5 supports 128K tokens, while Llama 3.1 405B Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, GPT-5 or Llama 3.1 405B Instruct?

GPT-5 is cheaper on tracked token pricing. GPT-5 costs $1.25/1M input and $10/1M output tokens. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5 or Llama 3.1 405B Instruct open source?

GPT-5 is listed under Proprietary. Llama 3.1 405B Instruct is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, GPT-5 or Llama 3.1 405B Instruct?

GPT-5 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, GPT-5 or Llama 3.1 405B Instruct?

GPT-5 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5 and Llama 3.1 405B Instruct?

GPT-5 is available on Replicate API and OpenRouter. Llama 3.1 405B Instruct is available on OctoAI API, Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.