Identity
- Status
- active
- Developer ID
meta-llama/Llama-4-Maverick-17B-128E-Instruct- Released
- Apr 5, 2025
- Version
- Llama-4-Maverick-17B-128E-Instruct
- License
- other
Llama-4-Maverick-17B-128E-Instruct is an open-weight vision-language model published by Meta.
Benchmark Market Position
The orange outline marks Llama 4 Maverick 17B 128E Instruct. Each available panel preserves its real rank and value; missing benchmark or pricing inputs remain explicitly unranked.
| Provider | Input / 1M | Output / 1M | Cache read / 1M | Cache write / 1M | Observed | Evidence |
|---|---|---|---|---|---|---|
OpenrouterAvailable | $0.1875 | $0.6525 | $0.050 | — | Oct 19, 2026 | Provider-reported |
| Aider Polyglotpolyglot-225-5dc9490bb35f | 15.56 | — | 225 | whole; aider 0.81.2.devunknown | Recomputedwinner eligible | Third-party benchmark |
| LMArena Text Arenatext-2026-10-15-a64ca3b12b6e | 1,287.19 | — | 39,450 | llama-4-maverick-17b-128e-instruct; 95% CI [1282.93484207, 1291.44351277]; votes 39450; rank 261unknown | Original sourcewinner eligible | Third-party benchmark |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e | 1,141.63 | — | 6,922 | llama-4-maverick-17b-128e-instruct; 95% CI [1132.58229133, 1150.67926500]; votes 6922; rank 120unknown | Original sourcewinner eligible | Third-party benchmark |
| ToneBench2026-10-17-10-task-e9c76358afb2 | 69.93 | 2,232 | 50 | Llama 4 Maverick (no thinking)average per case | Original sourcenot winner eligible | Third-party benchmark |
At a Glance
meta-llama/Llama-4-Maverick-17B-128E-InstructThis page represents one developer model product. Dated API snapshots, serving endpoints, pricing, regions, quantizations, and service tiers attach as versioned aliases or provider details and never create duplicate public model records.
Primary Evidence
Comparable Peers
| A | Pair | B | Context |
|---|---|---|---|
| vs | Hy4 previewTencent | cross-developer peerstext | |
| vs | DeepSeek V4 Flash BaseDeepSeek | cross-developer peerstext | |
| vs | DeepSeek V4 Flash Vision ExpDeepSeek | cross-developer peersimage, text | |
| vs | family variantstext | ||
| vs | family variantsimage, text | ||
| vs | family variantsimage, text | ||
| vs | Mistral Large 3Mistral AI | cross-developer peersimage, text | |
| vs | Mistral Small 4 119B 2603Mistral AI | cross-developer peersimage, text | |
| vs | cross-developer peerstext | ||
| vs | GPT-4.1OpenAI | cross-developer peersimage, text | |
| vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text | |
| vs | Inkling SmallThinking Machines Lab | cross-developer peersimage, text |
Questions
Llama-4-Maverick-17B-128E-Instruct is an open-weight vision-language model published by Meta. It is developed by Meta and its current sourced lifecycle status is active.
Llama 4 Maverick 17B 128E Instruct's current record lists Text and Image as input and Text as output.
The current record lists a 1,000K-token context window and does not report a maximum output length.
Llama 4 Maverick 17B 128E Instruct is marked as API-available. The directly sourced provider records currently include Openrouter.
The current record lists 401.6B total parameters and 17B active parameters. Its architecture is Llama4ForConditionalGeneration.
Llama 4 Maverick 17B 128E Instruct's recorded capabilities are chat, generation, and tools. Its supported tasks are image and text.
The lowest directly sourced prices currently attached to Llama 4 Maverick 17B 128E Instruct are $0.1875 per million input tokens through Openrouter and $0.6525 per million output tokens through Openrouter. Prices are provider-specific and should be checked against each cited observation date.
Llama 4 Maverick 17B 128E Instruct is an open-weight model. Self-hosting is supported, and the recorded license is other.
No predecessor is recorded. No successor is recorded. Recorded aliases are meta-llama/Llama-4-Maverick-17B-128E-Instruct.
The current compatible peer set includes Hy4 preview, DeepSeek V4 Flash Base, DeepSeek V4 Flash Vision Exp, Llama 3.1 8B Instruct, and Llama 4 Scout 17B 16E. Compatibility requires shared sourced modalities and tasks; it does not imply that one model is better. Browse model comparisons →