Which is cheaper, Gemini 1.5 Pro or Grok-4 Fast Reasoning?
As of August 20, 2026, Grok-4 Fast Reasoning is 94% cheaper on blended LLM API pricing. Grok-4 Fast Reasoning costs $0.20 per million input tokens and $0.50 per million output tokens ($0.70 blended 1M-in + 1M-out). Gemini 1.5 Pro costs $2.50 / $10.00 per million tokens ($12.50 blended). Sources: https://ai.google.dev/gemini-api/docs/pricing and https://docs.x.ai/docs/models. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Gemini 1.5 Pro vs Grok-4 Fast Reasoning cost per million tokens?
Gemini 1.5 Pro Gemini API pricing is $2.50 input and $10.00 output per million tokens via Google. Grok-4 Fast Reasoning xAI API pricing is $0.20 input and $0.50 output per million tokens via xAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Gemini 1.5 Pro or Grok-4 Fast Reasoning?
Grok-4 Fast Reasoning has a published SWE-bench Verified score of 45.4%. Gemini 1.5 Pro does not have that eval on this page yet.
What is the context window for Gemini 1.5 Pro vs Grok-4 Fast Reasoning?
Gemini 1.5 Pro supports a 2M token context window, while Grok-4 Fast Reasoning supports 2M tokens. Gemini 1.5 Pro 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, Gemini 1.5 Pro or Grok-4 Fast Reasoning?
Grok-4 Fast Reasoning leads on 4 of 4 shared benchmarks versus Gemini 1.5 Pro'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 Gemini 1.5 Pro or Grok-4 Fast Reasoning better for production use?
Both Gemini 1.5 Pro and Grok-4 Fast Reasoning are production API models. For cost-sensitive production traffic, Grok-4 Fast Reasoning 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 Gemini 1.5 Pro and Grok-4 Fast Reasoning in my app?
Yes. Call Gemini 1.5 Pro through Google and Grok-4 Fast Reasoning through xAI 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 Gemini 1.5 Pro vs Grok-4 Fast Reasoning pricing data?
List rates are the latest published Gemini API pricing and xAI API pricing, quoted per million tokens. Official rate sheets: https://ai.google.dev/gemini-api/docs/pricing and https://docs.x.ai/docs/models. 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 Gemini 1.5 Pro and Grok-4 Fast Reasoning?
Gemini 1.5 Pro supports up to 8K output tokens per request, while Grok-4 Fast Reasoning supports up to 30K 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, Gemini 1.5 Pro or Grok-4 Fast Reasoning?
Gemini 1.5 Pro runs at approximately 85 tokens/second while Grok-4 Fast Reasoning runs at 90 tokens/second. Grok-4 Fast Reasoning 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 Gemini 1.5 Pro vs Grok-4 Fast Reasoning comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Gemini 1.5 Pro or Grok-4 Fast Reasoning from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
