Capability
39.7/100
field median 59.1
#206 of 230 ranked models
Llama 4 Scout
EstablishedReleased Feb 28, 2026Open WeightNon-Reasoning10M context
Released Feb 28, 2026 — see all recent releases
Decision reading
Llama 4 Scout scores 39.7 out of 100 and ranks #206 of 230. This profile shows 1 source-displayable benchmark rows. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Data as of September 2, 2026 · How the score is built
Strongest published evidence
Published rows are visible, but no category has enough eligible evidence for a comparative rank.
Validate before choosing
1 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
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.
Price
Self-hosted; infrastructure cost varies
input median $1
No comparable first-party hosted token rate
Speed
118tok/s
field median 90 tok/s
First token 0.81 s
Context
10Mtokens
field median 256,000
Reported for this model; direct source link not stored
How 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.
- AgenticNot measured
- CodingNot measured
- ReasoningNot measured
- KnowledgeNot measured
- Math1/1 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
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
- 10M
- 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 Scout needs ~48GB VRAM (2× NVIDIA RTX 4090 (24GB)).
- Rate limits
- Not tracked yet
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.
Llama 4 Scout
API / mo$0
Self-host / mo$2,278
Break-even—
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 |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | Not ranked | Not available | 22% | 0 benchmarks | Not measured |
| CodingWeight 20%0 benchmarksNot measured | Not measured | Not ranked | Not available | 20% | 0 benchmarks | Not measured |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| MathRank Not rankedWeight 5%1 benchmarkVerified | 25.4 | Not ranked | Not available | 5% | 1 benchmark | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Math 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.
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score0.000% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap89 behind | WeightWeighted 30% | Benchmark exact |
Lineage
The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Feb 28, 2026 · you are here
Llama 4 ScoutScore 39.7 · Price not listed
Base entry
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 Scout ranks #206 of 230 on the public leaderboard with a score of 39.7/100. Its source-verified position is #86 of 105.
Llama 4 Scout is a open weight model with a 10M context window. No explicit reasoning mode is documented in this profile.
1 of 416 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Frequently asked questions
How does Llama 4 Scout perform overall in AI benchmarks?
Llama 4 Scout ranks #206 out of 230 models on the public BenchAlign leaderboard, with a score of 39.7/100. 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 Scout good for mathematics?
Llama 4 Scout has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Llama 4 Scout open source?
Llama 4 Scout 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 Scout have full benchmark coverage on BenchLM?
No. Llama 4 Scout currently has 18 source-displayable rows across 416 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 Scout?
Llama 4 Scout has a reported context window of 10M 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.
Compare Llama 4 Scout with every tracked model409 comparisons
Last updated September 2, 2026. Runtime fields remain blank until a sourced snapshot exists.
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