Which is cheaper, Llama 3.1 70B Instruct or Solar Pro 4?
As of August 20, 2026, Llama 3.1 70B Instruct is 73% 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). Solar Pro 4 costs $0.30 / $1.20 per million tokens ($1.50 blended). Sources: https://llama.meta.com/ and https://www.upstage.ai. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Llama 3.1 70B Instruct vs Solar Pro 4 cost per million tokens?
Llama 3.1 70B Instruct Meta API pricing is $0.20 input and $0.20 output per million tokens via Meta. Solar Pro 4 Upstage API pricing is $0.30 input and $1.20 output per million tokens via Upstage. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Llama 3.1 70B Instruct or Solar Pro 4?
Solar Pro 4 has a published SWE-bench Verified score of 70.6%. Llama 3.1 70B Instruct does not have that eval on this page yet.
What is the context window for Llama 3.1 70B Instruct vs Solar Pro 4?
Llama 3.1 70B Instruct supports a 128K token context window, while Solar Pro 4 supports 524K tokens. Solar Pro 4 offers a larger context window. 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, Llama 3.1 70B Instruct or Solar Pro 4?
Solar Pro 4 leads on 2 of 2 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 Llama 3.1 70B Instruct or Solar Pro 4 better for production use?
Both Llama 3.1 70B Instruct and Solar Pro 4 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 Llama 3.1 70B Instruct and Solar Pro 4 in my app?
Yes. Call Llama 3.1 70B Instruct through Meta and Solar Pro 4 through Upstage 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 Llama 3.1 70B Instruct vs Solar Pro 4 pricing data?
List rates are the latest published Meta API pricing and Upstage API pricing, quoted per million tokens. Official rate sheets: https://llama.meta.com/ and https://www.upstage.ai. 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 Llama 3.1 70B Instruct and Solar Pro 4?
Llama 3.1 70B Instruct supports up to 128K output tokens per request, while Solar Pro 4 supports up to 131K 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, Llama 3.1 70B Instruct or Solar Pro 4?
Llama 3.1 70B Instruct has a measured throughput of approximately 42 tokens/second. Throughput data for Solar Pro 4 is not yet available. 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 Llama 3.1 70B Instruct vs Solar Pro 4 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Llama 3.1 70B Instruct or Solar Pro 4 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.