As of August 20, 2026, Llama 4 Maverick is 81% cheaper per million tokens than Muse Spark 1.2. Llama 4 Maverick is $0.17 / $0.85 per million input/output tokens versus Muse Spark 1.2 at $1.25 / $4.25. Source: https://llama.meta.com/
Source: Meta pricing (https://llama.meta.com/)
Llama 4 Maverick
Meta
Input
$0.17
Output
$0.85
Muse Spark 1.2
Meta
Input
$1.25
Output
$4.25
Token pricing, specs, and benchmarks side by side
Llama 4 MaverickMuse Spark 1.2Verdict
Llama 4 Maverick offers significantly lower pricing, while Muse Spark 1.2 leads on benchmark performance. Your choice depends on whether cost efficiency or raw capability matters more for your use case.
As of August 20, 2026, Llama 4 Maverick is 81% cheaper on blended LLM API pricing. Llama 4 Maverick costs $0.17 per million input tokens and $0.85 per million output tokens ($1.02 blended 1M-in + 1M-out). Muse Spark 1.2 costs $1.25 / $4.25 per million tokens ($5.50 blended). Sources: https://llama.meta.com/ and https://llama.meta.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
Llama 4 Maverick Meta API pricing is $0.17 input and $0.85 output per million tokens via Meta. 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.
Muse Spark 1.2 has a published SWE-bench Verified score of 86.6%. Llama 4 Maverick does not have that eval on this page yet.
Llama 4 Maverick supports a 1M token context window, while Muse Spark 1.2 supports 1M tokens. Muse Spark 1.2 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.
Muse Spark 1.2 leads on 2 of 2 shared benchmarks versus Llama 4 Maverick'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.
Both Llama 4 Maverick and Muse Spark 1.2 are production API models. For cost-sensitive production traffic, Llama 4 Maverick 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.
Yes. Call Llama 4 Maverick through Meta 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.
List rates are the latest published Meta API pricing and Meta API pricing, quoted per million tokens. Official rate sheets: https://llama.meta.com/ 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.
Llama 4 Maverick supports up to 1M 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.
Llama 4 Maverick runs at approximately 69.42 tokens/second while Muse Spark 1.2 runs at 22.636101613788817 tokens/second. Llama 4 Maverick 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.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Llama 4 Maverick 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.
This comparison covers API pricing, context window, throughput, and benchmark scores. Llama 4 Maverick costs $0.17/M input and $0.85/M output. Muse Spark 1.2 costs $1.25/M input and $4.25/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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