Which is cheaper, Llama 3.1 Nemotron 70B Instruct or LongCat-Flash-Thinking?
As of August 30, 2026, LongCat-Flash-Thinking is 38% cheaper on blended LLM API pricing. LongCat-Flash-Thinking costs $0.30 per million input tokens and $1.20 per million output tokens ($1.50 blended 1M-in + 1M-out). Llama 3.1 Nemotron 70B Instruct costs $1.20 / $1.20 per million tokens ($2.40 blended). Sources: https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct and https://anotherwrapper.com/tools/llm-pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Llama 3.1 Nemotron 70B Instruct vs LongCat-Flash-Thinking cost per million tokens?
Llama 3.1 Nemotron 70B Instruct NVIDIA API pricing is $1.20 input and $1.20 output per million tokens via NVIDIA. LongCat-Flash-Thinking Meituan API pricing is $0.30 input and $1.20 output per million tokens via Meituan. 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 Nemotron 70B Instruct or LongCat-Flash-Thinking?
LongCat-Flash-Thinking has a published SWE-bench Verified score of 59.4%. Llama 3.1 Nemotron 70B Instruct does not have that eval on this page yet.
What is the context window for Llama 3.1 Nemotron 70B Instruct vs LongCat-Flash-Thinking?
Llama 3.1 Nemotron 70B Instruct supports a 131K token context window, while LongCat-Flash-Thinking supports 128K tokens. Llama 3.1 Nemotron 70B Instruct 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 Nemotron 70B Instruct or LongCat-Flash-Thinking?
Shared benchmark scores for Llama 3.1 Nemotron 70B Instruct and LongCat-Flash-Thinking are still limited. Check the comparison table for the evals each model has published. 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 Nemotron 70B Instruct or LongCat-Flash-Thinking better for production use?
Both Llama 3.1 Nemotron 70B Instruct and LongCat-Flash-Thinking are production API models. For cost-sensitive production traffic, LongCat-Flash-Thinking 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 Nemotron 70B Instruct and LongCat-Flash-Thinking in my app?
Yes. Call Llama 3.1 Nemotron 70B Instruct through NVIDIA and LongCat-Flash-Thinking through Meituan 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 Nemotron 70B Instruct vs LongCat-Flash-Thinking pricing data?
List rates are the latest published NVIDIA API pricing and Meituan API pricing, quoted per million tokens. Official rate sheets: https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct and https://anotherwrapper.com/tools/llm-pricing. 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 Nemotron 70B Instruct and LongCat-Flash-Thinking?
Llama 3.1 Nemotron 70B Instruct supports up to an unspecified number of output tokens per request, while LongCat-Flash-Thinking 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, Llama 3.1 Nemotron 70B Instruct or LongCat-Flash-Thinking?
LongCat-Flash-Thinking has a measured throughput of approximately 100 tokens/second. Throughput data for Llama 3.1 Nemotron 70B Instruct 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 Nemotron 70B Instruct vs LongCat-Flash-Thinking 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 Nemotron 70B Instruct or LongCat-Flash-Thinking from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
