Data as of October 17, 2026 · How the score is built
Released Apr 5, 20251M context
Llama 4 Maverick
Decision readingLlama 4 Maverick scores 21.6 out of 100 and ranks #211 of 224. This profile shows 1 source-displayable benchmark rows; its strongest eligible category is Agentic at #121. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Apr 5, 2025 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
21.6/100
field median 49.6#211 of 224 ranked models
Public
#211of 224
Verified #85 of 88
Price
Self-hosted; infrastructure cost varies
input median $0.75No comparable first-party hosted token rate
Speed
56tok/s
field median 92 tok/sFirst token 1.11 s
Context
1Mtokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Agentic ranks #121. Particularly useful for coding agents, browser research, and computer-use workflows.
Validate before choosing
1 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 1 displayable benchmark row
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #121 of 128Percentile 6thWeight 22%1 benchmarkVerified | 10.6 | 1 benchmark | Verified | |||
| CodingWeight 20%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
1 of 681 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic1/1 verified
- CodingNot measured
- ReasoningNot measured
- MultimodalNot measured
- KnowledgeNot measured
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Llama 4 Maverick category percentile values
- Agentic6th percentile
- CodingNot eligible
- ReasoningNot eligible
- MultimodalNot eligible
- KnowledgeNot eligible
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#121/128
- CodingNot ranked
- ReasoningNot ranked
- MultimodalNot ranked
- KnowledgeNot ranked
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
Benchmark ledger
Agentic opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Agentic1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | Score0% | Versus best verified row Best verified: GPT-6 Astra · 23% | Gap23 behind | WeightDisplay only | Benchmark exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 1 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 1M
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Llama 4 Maverick needs ~160GB VRAM (2× NVIDIA A100 (80GB)).
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Llama 4 Maverick ranks #211 of 224 on the public leaderboard with a score of 21.61/100. Its source-verified position is #85 of 88.
Llama 4 Maverick is a open weight model with a 1M context window. No explicit reasoning mode is documented in this profile.
1 of 681 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Agentic at #121. particularly useful for coding agents, browser research, and computer-use workflows.
Last updated October 17, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Questions
How does Llama 4 Maverick perform overall in AI benchmarks?
Llama 4 Maverick ranks #211 out of 224 models on the public BenchAlign leaderboard, with a score of 21.61/100. Its evidence status is Supported, and this profile shows 1 source-displayable benchmark row. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Llama 4 Maverick good for agentic tool use and computer tasks?
Llama 4 Maverick ranks #121 out of 128 eligible models for agentic tool use and computer tasks, with a public category score of 10.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Llama 4 Maverick open source?
Llama 4 Maverick is an open-weight model from Meta. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Llama 4 Maverick have full benchmark coverage on BenchLM?
No. Llama 4 Maverick currently has 18 source-displayable rows across 681 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Llama 4 Maverick?
Llama 4 Maverick has a reported context window of 1M in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.