Meta flagship open-weights MoE model designed for high-reasoning multimodal dialogue Need a token count first? Use the Llama token counter.
meta-llama/llama-4-maverick-instruct| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00069 | N/A |
| Document Summarization | 10,000 | 2,000 | $0.00440 | N/A |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.0440 | N/A |
| Batch Corpus Processing | 1,000,000 | 100,000 | $0.3550 | N/A |
Tier 1 (free tier) reference limits — your account's actual quota may be higher.
Ideal for production workloads demanding flagship capabilities, deep context depth (1M tokens), and reliability from Meta / Together. Excellent when predictable tokenomics and prompt caching support are paramount.
If your use-case requires sub-second streaming latency or ultra-high frequency classification at micro-cent pricing, consider lighter budget options such as Gemini Flash-Lite or Claude Haiku. For deep formal logic, consider dedicated reasoning models like o3.
For Llama 4 Maverick (400B) — Together AI, 1 million input tokens costs $0.27, while 1 million output tokens costs $0.85. If using prompt caching, repetitive input prefixes are discounted to $0.068 per million.
Llama 4 Maverick (400B) — Together AI features a maximum context window of 1,000,000 tokens (~750,000 words), with a maximum output limit of 16,384 tokens per completion.
Llama 4 Maverick (400B) — Together AI utilizes the Meta Llama 3/4 Tiktoken (128k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
No, Llama 4 Maverick (400B) — Together AI does not currently advertise prompt caching discounts on its standard API tier.