Which is cheaper, Gemini 3.1 Pro or Muse Spark 1.2?
As of August 20, 2026, Muse Spark 1.2 is 69% cheaper on blended LLM API pricing. Muse Spark 1.2 costs $1.25 per million input tokens and $4.25 per million output tokens ($5.50 blended 1M-in + 1M-out). Gemini 3.1 Pro costs $2.50 / $15.00 per million tokens ($17.50 blended). Sources: https://ai.google.dev/gemini-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 Gemini 3.1 Pro vs Muse Spark 1.2 cost per million tokens?
Gemini 3.1 Pro Gemini API pricing is $2.50 input and $15.00 output per million tokens via Google. Muse Spark 1.2 Meta API pricing is $1.25 input and $4.25 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, Gemini 3.1 Pro or Muse Spark 1.2?
Muse Spark 1.2 leads on SWE-bench Verified: Gemini 3.1 Pro at 80.6% vs Muse Spark 1.2 at 86.6%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for Gemini 3.1 Pro vs Muse Spark 1.2?
Gemini 3.1 Pro supports a 1M token context window, while Muse Spark 1.2 supports 1M 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, Gemini 3.1 Pro or Muse Spark 1.2?
Muse Spark 1.2 leads on 14 of 18 shared benchmarks versus Gemini 3.1 Pro's 4 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 3.1 Pro or Muse Spark 1.2 better for production use?
Both Gemini 3.1 Pro and Muse Spark 1.2 are production API models. For cost-sensitive production traffic, Muse Spark 1.2 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 3.1 Pro and Muse Spark 1.2 in my app?
Yes. Call Gemini 3.1 Pro through Google and Muse Spark 1.2 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 Gemini 3.1 Pro vs Muse Spark 1.2 pricing data?
List rates are the latest published Gemini API pricing and Meta API pricing, quoted per million tokens. Official rate sheets: https://ai.google.dev/gemini-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 Gemini 3.1 Pro and Muse Spark 1.2?
Gemini 3.1 Pro supports up to 66K output tokens per request, while Muse Spark 1.2 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, Gemini 3.1 Pro or Muse Spark 1.2?
Gemini 3.1 Pro runs at approximately 90 tokens/second while Muse Spark 1.2 runs at 22.636101613788817 tokens/second. Gemini 3.1 Pro 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 3.1 Pro vs Muse Spark 1.2 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Gemini 3.1 Pro or Muse Spark 1.2 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.