As of August 20, 2026, Ling-3.0-flash is 96% cheaper per million tokens than Muse Spark 1.2. Ling-3.0-flash is $0.06 / $0.18 per million input/output tokens versus Muse Spark 1.2 at $1.25 / $4.25. Sources: https://developer.ant-ling.com/en/docs/models/ling/ and https://llama.meta.com/
Sources: inclusionAI pricing (https://developer.ant-ling.com/en/docs/models/ling/); Meta pricing (https://llama.meta.com/)
Ling-3.0-flash
inclusionAI
Input
$0.06
Output
$0.18
Muse Spark 1.2
Meta
Input
$1.25
Output
$4.25
Token pricing, specs, and benchmarks side by side
Ling-3.0-flashMuse Spark 1.2Verdict
Both models are closely matched on benchmarks. Ling-3.0-flash has the pricing advantage, making it the better value unless Muse Spark 1.2's specific capabilities are critical to your workflow.
As of August 20, 2026, Ling-3.0-flash is 96% cheaper on blended LLM API pricing. Ling-3.0-flash costs $0.06 per million input tokens and $0.18 per million output tokens ($0.24 blended 1M-in + 1M-out). Muse Spark 1.2 costs $1.25 / $4.25 per million tokens ($5.50 blended). Sources: https://developer.ant-ling.com/en/docs/models/ling/ and https://llama.meta.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
Ling-3.0-flash inclusionAI API pricing is $0.06 input and $0.18 output per million tokens via inclusionAI. 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%. Ling-3.0-flash does not have that eval on this page yet.
Ling-3.0-flash supports a 262K 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.
Shared benchmark scores for Ling-3.0-flash and Muse Spark 1.2 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.
Both Ling-3.0-flash and Muse Spark 1.2 are production API models. For cost-sensitive production traffic, Ling-3.0-flash 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 Ling-3.0-flash through inclusionAI 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 inclusionAI API pricing and Meta API pricing, quoted per million tokens. Official rate sheets: https://developer.ant-ling.com/en/docs/models/ling/ 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.
Ling-3.0-flash supports up to 33K 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.
Ling-3.0-flash runs at approximately 1000 tokens/second while Muse Spark 1.2 runs at 22.636101613788817 tokens/second. Ling-3.0-flash 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 Ling-3.0-flash 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. Ling-3.0-flash costs $0.06/M input and $0.18/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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