Which is cheaper, GPT-4 Turbo or Llama 3.1 70B Instruct?
As of August 25, 2026, Llama 3.1 70B Instruct is 99% cheaper on blended LLM API pricing. Llama 3.1 70B Instruct costs $0.20 per million input tokens and $0.20 per million output tokens ($0.40 blended 1M-in + 1M-out). GPT-4 Turbo costs $10.00 / $30.00 per million tokens ($40.00 blended). Sources: https://developers.openai.com/api/docs/pricing and https://llama.meta.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GPT-4 Turbo vs Llama 3.1 70B Instruct cost per million tokens?
GPT-4 Turbo OpenAI API pricing is $10.00 input and $30.00 output per million tokens via OpenAI. Llama 3.1 70B Instruct Meta API pricing is $0.20 input and $0.20 output per million tokens via Meta. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GPT-4 Turbo or Llama 3.1 70B Instruct?
GPT-4 Turbo leads on HumanEval: GPT-4 Turbo at 87.1% vs Llama 3.1 70B Instruct at 80.5%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for GPT-4 Turbo vs Llama 3.1 70B Instruct?
GPT-4 Turbo supports a 128K token context window, while Llama 3.1 70B Instruct supports 128K tokens. Both models have the same context window size. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, GPT-4 Turbo or Llama 3.1 70B Instruct?
GPT-4 Turbo leads on 7 of 7 shared benchmarks versus Llama 3.1 70B Instruct's 0 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is GPT-4 Turbo or Llama 3.1 70B Instruct better for production use?
Both GPT-4 Turbo and Llama 3.1 70B Instruct are production API models. For cost-sensitive production traffic, Llama 3.1 70B Instruct has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between GPT-4 Turbo and Llama 3.1 70B Instruct in my app?
Yes. Call GPT-4 Turbo through OpenAI and Llama 3.1 70B Instruct through Meta with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this GPT-4 Turbo vs Llama 3.1 70B Instruct pricing data?
List rates are the latest published OpenAI API pricing and Meta API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://llama.meta.com/. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for GPT-4 Turbo and Llama 3.1 70B Instruct?
GPT-4 Turbo supports up to 4K output tokens per request, while Llama 3.1 70B Instruct supports up to 128K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, GPT-4 Turbo or Llama 3.1 70B Instruct?
GPT-4 Turbo runs at approximately 100 tokens/second while Llama 3.1 70B Instruct runs at 42 tokens/second. GPT-4 Turbo is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this GPT-4 Turbo vs Llama 3.1 70B Instruct comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-4 Turbo or Llama 3.1 70B Instruct from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
