Which is cheaper, GPT-5.1 Codex or Llama-3.3 Nemotron Super 49B v1?
As of August 30, 2026, Llama-3.3 Nemotron Super 49B v1 is 96% cheaper on blended LLM API pricing. Llama-3.3 Nemotron Super 49B v1 costs $0.10 per million input tokens and $0.40 per million output tokens ($0.50 blended 1M-in + 1M-out). GPT-5.1 Codex costs $1.25 / $10.00 per million tokens ($11.25 blended). Sources: https://developers.openai.com/api/docs/pricing and https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GPT-5.1 Codex vs Llama-3.3 Nemotron Super 49B v1 cost per million tokens?
GPT-5.1 Codex OpenAI API pricing is $1.25 input and $10.00 output per million tokens via OpenAI. Llama-3.3 Nemotron Super 49B v1 NVIDIA API pricing is $0.10 input and $0.40 output per million tokens via NVIDIA. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GPT-5.1 Codex or Llama-3.3 Nemotron Super 49B v1?
GPT-5.1 Codex has a published SWE-bench Verified score of 73.7%. Llama-3.3 Nemotron Super 49B v1 does not have that eval on this page yet.
What is the context window for GPT-5.1 Codex vs Llama-3.3 Nemotron Super 49B v1?
GPT-5.1 Codex supports a 400K token context window, while Llama-3.3 Nemotron Super 49B v1 supports 131K tokens. GPT-5.1 Codex 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, GPT-5.1 Codex or Llama-3.3 Nemotron Super 49B v1?
Shared benchmark scores for GPT-5.1 Codex and Llama-3.3 Nemotron Super 49B v1 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 GPT-5.1 Codex or Llama-3.3 Nemotron Super 49B v1 better for production use?
Both GPT-5.1 Codex and Llama-3.3 Nemotron Super 49B v1 are production API models. For cost-sensitive production traffic, Llama-3.3 Nemotron Super 49B v1 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-5.1 Codex and Llama-3.3 Nemotron Super 49B v1 in my app?
Yes. Call GPT-5.1 Codex through OpenAI and Llama-3.3 Nemotron Super 49B v1 through NVIDIA 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-5.1 Codex vs Llama-3.3 Nemotron Super 49B v1 pricing data?
List rates are the latest published OpenAI API pricing and NVIDIA API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1. 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-5.1 Codex and Llama-3.3 Nemotron Super 49B v1?
GPT-5.1 Codex supports up to 128K output tokens per request, while Llama-3.3 Nemotron Super 49B v1 supports up to an unspecified number of 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-5.1 Codex or Llama-3.3 Nemotron Super 49B v1?
GPT-5.1 Codex has a measured throughput of approximately 50 tokens/second. Throughput data for Llama-3.3 Nemotron Super 49B v1 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 GPT-5.1 Codex vs Llama-3.3 Nemotron Super 49B v1 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-5.1 Codex or Llama-3.3 Nemotron Super 49B v1 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
