Model comparison
GPT-4.1 nano 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: GPT-4.1 nano #170 (Estimated); Llama 4 Scout #183 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and Llama 4 Scout share 18 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to GPT-4.1 nano; 0 to Llama 4 Scout.
Updated July 24, 2026- Shared results
- 18
- GPT-4.1 nano only
- 3
- Llama 4 Scout only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-4.1 nano 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.
GPT-4.1 nano is priced at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Scout. Llama 4 Scout has the larger context window at 10M, compared with 1M for GPT-4.1 nano.
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 | GPT-4.1 nano | Δ | Llama 4 Scout |
|---|---|---|---|
| Knowledge | GPT-4.1 nano50.3 | MarginNo overlap | Llama 4 ScoutNot measured |
| Math | GPT-4.1 nano1.0 | MarginNo overlap | Llama 4 ScoutNot measured |
| Inst. Following | GPT-4.1 nano83.2 | 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 1.034%B 0.000%Winner: GPT-4.1 nanoΔ 1FrontierMath v2 (Tiers 1-3): GPT-4.1 nano scored 1.034%; Llama 4 Scout scored 0.000%. GPT-4.1 nano wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 nano | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Llama 4 Scout$0 input / $0 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | Llama 4 Scout128 tok/s | GPT-4.1 nano has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | Llama 4 Scout0.70 s | GPT-4.1 nano reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-4.1 nano | Llama 4 Scout | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 9.6% | 10.0% | Llama 4 Scout leads |
| AA-GPQA DiamondSource | 51.2% | 58.7% | Llama 4 Scout leads |
| AA-HLESource | 3.9% | 4.3% | Llama 4 Scout leads |
| AA-Omniscience IndexSource | -56.4% | -52.4% | Llama 4 Scout leads |
| AA-Omniscience AccuracySource | 13.3% | 14.6% | Llama 4 Scout leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 78.3% | Llama 4 Scout leads |
Math1 benchmarks
| Benchmark | GPT-4.1 nano | Llama 4 Scout | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.034% | 0.000% | GPT-4.1 nano leads |
Multimodal2 benchmarks
Frequently Asked Questions (3)
Can I compare GPT-4.1 nano 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 GPT-4.1 nano and Llama 4 Scout today?
GPT-4.1 nano: $0.10 input / $0.40 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.