Model comparison
Llama 4 Maverick vs Llama 4 Scout
Head-to-head evidence from 18 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Llama 4 Maverick #191 (Supported); Llama 4 Scout #174 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Llama 4 Maverick and Llama 4 Scout share 18 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to Llama 4 Maverick; 0 to Llama 4 Scout.
Updated July 20, 2026- Shared results
- 18
- Llama 4 Maverick only
- 0
- Llama 4 Scout only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Llama 4 Maverick and Llama 4 Scout is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 7 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Llama 4 Scout has the larger context window at 10M, compared with 1M for Llama 4 Maverick.
Operator verdict
What I would choose
Llama 4 Scout is the better starting point when context length and serving speed matter; it lists a 10M-token window and slightly higher measured throughput. Llama 4 Maverick is the safer candidate only when its stronger result on a benchmark relevant to your workload survives a local replay.
Operator receipt. I checked 18 shared sourced rows and the operational metadata on July 15, 2026. Llama 4 Scout lists 10M of context versus 1M, with 128 tok/s versus 121 tok/s.
Honest limit. These are open-weight models, so the $0 catalog entries mean self-hosted cost varies; they do not mean inference is free. Their BenchAlign intervals are broad and overlap, so the point-score gap should not be read as a decisive capability gap.
Reviewed by Glevd on July 15, 2026.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Llama 4 Maverick | Δ | Llama 4 Scout |
|---|---|---|---|
| Math | Llama 4 Maverick0.7 | MarginNo overlap | Llama 4 ScoutNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 0.690%B 0.000%Winner: Llama 4 MaverickΔ 0.7FrontierMath v2 (Tiers 1-3): Llama 4 Maverick scored 0.690%; Llama 4 Scout scored 0.000%. Llama 4 Maverick wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 4 Maverick | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 4 Maverick$0 input / $0 output | Llama 4 Scout$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Llama 4 Maverick121 tok/s | Llama 4 Scout128 tok/s | Llama 4 Scout has the higher measured throughput. |
| First-answer latencyseconds to first token | Llama 4 Maverick0.95 s | Llama 4 Scout0.70 s | Llama 4 Scout reaches the first token sooner. |
| Context windowmaximum listed tokens | Llama 4 Maverick1M | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Llama 4 Maverick | Llama 4 Scout | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.3% | 10.0% | Llama 4 Maverick leads |
| AA-GPQA DiamondSource | 67.1% | 58.7% | Llama 4 Maverick leads |
| AA-HLESource | 4.8% | 4.3% | Llama 4 Maverick leads |
| AA-Omniscience IndexSource | -41.8% | -52.4% | Llama 4 Maverick leads |
| AA-Omniscience AccuracySource | 24.3% | 14.6% | Llama 4 Maverick leads |
| AA-Omniscience Hallucination RateSource | 87.3% | 78.3% | Llama 4 Scout leads |
Math1 benchmarks
| Benchmark | Llama 4 Maverick | Llama 4 Scout | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 0.690% | 0.000% | Llama 4 Maverick leads |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Llama 4 Maverick | Llama 4 Scout | Result |
|---|---|---|---|
| AA-IFBenchSource | 43.0% | 39.5% | Llama 4 Maverick leads |
Frequently Asked Questions (3)
Can I compare Llama 4 Maverick and Llama 4 Scout on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Llama 4 Maverick and Llama 4 Scout today?
Llama 4 Maverick: $0.00 input / $0.00 output per 1M tokens Llama 4 Scout: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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