Model comparison
GPT-5.6 Sol vs Llama 4 Scout
Head-to-head evidence from 17 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (Supported); Llama 4 Scout #174 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and Llama 4 Scout share 17 comparable benchmark results. 0 of 8 categories are comparable. 29 results are unique to GPT-5.6 Sol; 1 to Llama 4 Scout.
Updated July 23, 2026- Shared results
- 17
- GPT-5.6 Sol only
- 29
- Llama 4 Scout only
- 1
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.6 Sol and Llama 4 Scout is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 17 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-5.6 Sol is priced at $5.00 input / $30.00 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 GPT-5.6 Sol.
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-5.6 Sol | Δ | Llama 4 Scout |
|---|---|---|---|
| Agentic | GPT-5.6 Sol92.0 | MarginNo overlap | Llama 4 ScoutNot measured |
| Coding | GPT-5.6 Sol64.6 | MarginNo overlap | Llama 4 ScoutNot measured |
| Knowledge | GPT-5.6 Sol94.6 | MarginNo overlap | Llama 4 ScoutNot measured |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | Llama 4 ScoutNot measured |
| Multimodal | GPT-5.6 Sol83.0 | 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 89.000%B 0.000%Winner: GPT-5.6 SolΔ 89FrontierMath v2 (Tiers 1-3): GPT-5.6 Sol scored 89.000%; Llama 4 Scout scored 0.000%. GPT-5.6 Sol wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | Llama 4 Scout$0 input / $0 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | Llama 4 Scout128 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | Llama 4 Scout0.70 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | GPT-5.6 Sol | Llama 4 Scout | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| BrowseCompSource | 92.2% | — | Not comparable |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | — | Not comparable |
| AA Agentic IndexSource | 54.0% | 1.1% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 15.5% | GPT-5.6 Sol leads |
| GDPval-AASource | 61.8% | 0.0% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 90 | GPT-5.6 Sol leads |
| AA BriefcaseSource | 1501 | — | Not comparable |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | — | Not comparable |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | — | Not comparable |
| terminalBenchHardSource | 65.9% | — | Not comparable |
| aaTerminalBench21Source | 88% | — | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-5.6 Sol | Llama 4 Scout | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | 8.2% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 17.0% | GPT-5.6 Sol leads |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.6 Sol | Llama 4 Scout | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 10.0% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 58.7% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 4.3% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -52.4% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 14.6% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 78.3% | Llama 4 Scout leads |
Math3 benchmarks
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.6 Sol | Llama 4 Scout | Result |
|---|---|---|---|
| AA-IFBenchSource | 72.7% | 39.5% | GPT-5.6 Sol leads |
Frequently Asked Questions (3)
Can I compare GPT-5.6 Sol 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-5.6 Sol and Llama 4 Scout today?
GPT-5.6 Sol: $5.00 input / $30.00 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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