Model comparison
Claude Opus 4.6 vs Llama 4 Scout
Head-to-head evidence from 14 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.6 #16 (Supported); Llama 4 Scout #174 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.6 and Llama 4 Scout share 14 comparable benchmark results. 0 of 8 categories are comparable. 32 results are unique to Claude Opus 4.6; 4 to Llama 4 Scout.
Updated July 20, 2026- Shared results
- 14
- Claude Opus 4.6 only
- 32
- Llama 4 Scout only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.6 and Llama 4 Scout is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 14 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.
Claude Opus 4.6 is priced at $5.00 input / $25.00 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 1M for Claude Opus 4.6.
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 | Claude Opus 4.6 | Δ | Llama 4 Scout |
|---|---|---|---|
| Agentic | Claude Opus 4.673.0 | MarginNo overlap | Llama 4 ScoutNot measured |
| Coding | Claude Opus 4.668.1 | MarginNo overlap | Llama 4 ScoutNot measured |
| Knowledge | Claude Opus 4.669.1 | MarginNo overlap | Llama 4 ScoutNot measured |
| Math | Claude Opus 4.636.3 | MarginNo overlap | Llama 4 ScoutNot measured |
| Multimodal | Claude Opus 4.677.3 | 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 40.700%B 0.000%Winner: Claude Opus 4.6Δ 40.7FrontierMath v2 (Tiers 1-3): Claude Opus 4.6 scored 40.700%; Llama 4 Scout scored 0.000%. Claude Opus 4.6 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.6 | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.6$5 input / $25 output | Llama 4 Scout$0 input / $0 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.640 tok/s | Llama 4 Scout128 tok/s | Llama 4 Scout has the higher measured throughput. |
| First-answer latencyseconds to first token | Claude Opus 4.61.78 s | Llama 4 Scout0.70 s | Llama 4 Scout reaches the first token sooner. |
| Context windowmaximum listed tokens | Claude Opus 4.61M | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | Claude Opus 4.6 | Llama 4 Scout | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 65.4% | — | Not comparable |
| BrowseCompSource | 83.7% | — | Not comparable |
| OSWorld-VerifiedSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | 15.5% | Claude Opus 4.6 leads |
| Claw-EvalSource | 70.4% | — | Not comparable |
| DeepSearchQASource | 73.7% | — | Not comparable |
| CyberGymSource | 66.6% | — | Not comparable |
| Gert LabsSource | 61.85% | — | Not comparable |
| ResearchClawBenchSource | 19.9% | — | Not comparable |
| JobBenchSource | 36.7% | — | Not comparable |
| AA Agentic IndexSource | — | 1.1% | Not comparable |
| GDPval-AASource | — | 0.0% | Not comparable |
| GDPval-AASource | — | 90 | Not comparable |
Coding10 benchmarks
| Benchmark | Claude Opus 4.6 | Llama 4 Scout | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.8% | — | Not comparable |
| SWE-bench Verified*Source | 75.6% | — | Not comparable |
| LiveCodeBench ProSource | 70.7% | — | Not comparable |
| SWE-bench ProSource | 53.4% | — | Not comparable |
| SWE-RebenchSource | 65.3% | — | Not comparable |
| React Native EvalsSource | 84.1% | — | Not comparable |
| Vibe Code BenchSource | 57.57% | — | Not comparable |
| AA-SciCodeSource | 45.7% | 17.0% | Claude Opus 4.6 leads |
| FrontierCode 1.1 MainSource | 26.9% | — | Not comparable |
| AA Coding IndexSource | — | 8.2% | Not comparable |
Reasoning2 benchmarks
Knowledge15 benchmarks
| Benchmark | Claude Opus 4.6 | Llama 4 Scout | Result |
|---|---|---|---|
| GPQASource | 91.3% | — | Not comparable |
| GPQA-DSource | 89.2% | — | Not comparable |
| SuperGPQASource | 95% | — | Not comparable |
| MMLU-ProSource | 82% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 89.1% | — | Not comparable |
| HLESource | 53% | — | Not comparable |
| HLE w/o toolsSource | 40% | — | Not comparable |
| HealthBench HardSource | 14.8% | — | Not comparable |
| MedXpertQA (Text)Source | 52.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.8% | 10.0% | Claude Opus 4.6 leads |
| AA-GPQA DiamondSource | 84.0% | 58.7% | Claude Opus 4.6 leads |
| AA-HLESource | 18.6% | 4.3% | Claude Opus 4.6 leads |
| AA-Omniscience IndexSource | 3.5% | -52.4% | Claude Opus 4.6 leads |
| AA-Omniscience AccuracySource | 45.2% | 14.6% | Claude Opus 4.6 leads |
| AA-Omniscience Hallucination RateSource | 76.0% | 78.3% | Claude Opus 4.6 leads |
Math3 benchmarks
Multimodal6 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.6 | Llama 4 Scout | Result |
|---|---|---|---|
| AA-IFBenchSource | 44.6% | 39.5% | Claude Opus 4.6 leads |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.6 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 Claude Opus 4.6 and Llama 4 Scout today?
Claude Opus 4.6: $5.00 input / $25.00 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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