Llama 4 Maverick 17B 128E Instruct FP8
meta/llama-4-maverick-17b-128e-instruct出品方： Meta · 系列: llama · 发布 2025-04-05 · 知识截止: 2024-08
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding52
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents50
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability10/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency66
- Headline price (log-scaled)66/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Vision87
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability10/10
Production-readiness94
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.99 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $1.99 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.57 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $1.71 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $2.03 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 abacus · meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
最便宜的渠道： llama · Unknown 输入 + Unknown 输出
在 12 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| Azure azure | llama-4-maverick-17b-128e-instruct-fp8 | $0.250 | $1.00 | 128K | 2025-04-05 |
| Groq groq | meta-llama/llama-4-maverick-17b-128e-instruct | $0.200 | $0.600 | 131K | 2025-04-05 |
| Deep Infra deepinfra | meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | $0.150 | $0.600 | 1M | 2025-04-05 |
| Abacus abacus | meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | $0.140 | $0.590 | 1M | 2025-04-05 |
| Llama llama | cerebras-llama-4-maverick-17b-128e-instruct | Unknown | Unknown | 128K | 2025-04-05 |
| Llama llama | llama-4-maverick-17b-128e-instruct-fp8 | Unknown | Unknown | 128K | 2025-04-05 |
| IO.NET io-net | meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | $0.150 | $0.600 | 430K | 2025-01-15 |
| NovitaAI novita-ai | meta-llama/llama-4-maverick-17b-128e-instruct-fp8 | $0.270 | $0.850 | 1.05M | 2025-04-06 |
| Azure Cognitive Services azure-cognitive-services | llama-4-maverick-17b-128e-instruct-fp8 | $0.250 | $1.00 | 128K | 2025-04-05 |
| Synthetic synthetic | hf:meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | $0.220 | $0.880 | 524K | 2025-04-05 |
| Nvidia nvidia | meta/llama-4-maverick-17b-128e-instruct | Unknown | Unknown | 128K | 2025-04-01 |
| GitHub Models github-models | meta/llama-4-maverick-17b-128e-instruct-fp8 | Unknown | Unknown | 128K | 2025-01-31 |
各渠道数据存在不一致
- context_window varies: 1000000, 1048576, 128000, 131072, 430000, 524000
- release_date varies (span 81d): 2025-01-15, 2025-01-31, 2025-04-01, 2025-04-05, 2025-04-06
- modalities varies across offerings
各服务商对此模型的报告值存在差异。上方「核心数据」使用代表性服务商的值；逐项请以下表为准。
Frequently asked questions
How much does Llama 4 Maverick 17B 128E Instruct FP8 cost?
Llama 4 Maverick 17B 128E Instruct FP8 costs $0.140 per 1M input tokens and $0.590 per 1M output tokens, sourced from abacus. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Llama 4 Maverick 17B 128E Instruct FP8?
Llama 4 Maverick 17B 128E Instruct FP8 has a context window of 128K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 4 Maverick 17B 128E Instruct FP8 support tool calling?
Yes. Llama 4 Maverick 17B 128E Instruct FP8 supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Llama 4 Maverick 17B 128E Instruct FP8 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama 4 Maverick 17B 128E Instruct FP8 in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama 4 Maverick 17B 128E Instruct FP8 accept image input?
Yes. Llama 4 Maverick 17B 128E Instruct FP8 accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Meta's docs for the exact rule.
Is Llama 4 Maverick 17B 128E Instruct FP8 open-weight?
Yes. Llama 4 Maverick 17B 128E Instruct FP8's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Llama 4 Maverick 17B 128E Instruct FP8?
If Llama 4 Maverick 17B 128E Instruct FP8 doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct, Llama 4 Scout 17B 16E Instruct. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.