Model comparison
Llama 4 Scout vs o1-pro
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Llama 4 Scout #183 (Supported); o1-pro #148 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Llama 4 Scout and o1-pro share 1 comparable benchmark result. 0 of 8 categories are comparable. 17 results are unique to Llama 4 Scout; 1 to o1-pro.
Updated July 24, 2026- Shared results
- 1
- Llama 4 Scout only
- 17
- o1-pro only
- 1
- Comparable categories
- 0 / 8
Benchmark data for Llama 4 Scout and o1-pro is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 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.
o1-pro is priced at $150.00 input / $600.00 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 200K for o1-pro.
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 Scout | Δ | o1-pro |
|---|---|---|---|
| Knowledge | Llama 4 ScoutNot measured | MarginNo overlap | o1-pro79.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 4 Scout | o1-pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 4 Scout$0 input / $0 output | o1-pro$150 input / $600 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | Llama 4 Scout128 tok/s | o1-proNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Llama 4 Scout0.70 s | o1-proNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Llama 4 Scout10M | o1-pro200K | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | Llama 4 Scout | o1-pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 10.0% | 18.9% | o1-pro leads |
| AA-GPQA DiamondSource | 58.7% | — | Not comparable |
| AA-HLESource | 4.3% | — | Not comparable |
| AA-Omniscience IndexSource | -52.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 14.6% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 78.3% | — | Not comparable |
| GPQASource | — | 79% | Not comparable |
Math1 benchmarks
| Benchmark | Llama 4 Scout | o1-pro | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 0.000% | — | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Llama 4 Scout | o1-pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 39.5% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Llama 4 Scout and o1-pro 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 Scout and o1-pro today?
Llama 4 Scout: $0.00 input / $0.00 output per 1M tokens o1-pro: $150.00 input / $600.00 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.