Model comparison
Claude Opus 4.6 (Adaptive) vs GPT-5.6 Sol
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.6 (Adaptive) #35 (Estimated); GPT-5.6 Sol #3 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.6 (Adaptive) and GPT-5.6 Sol share 12 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to Claude Opus 4.6 (Adaptive); 34 to GPT-5.6 Sol.
Updated July 23, 2026- Shared results
- 12
- Claude Opus 4.6 (Adaptive) only
- 4
- GPT-5.6 Sol only
- 34
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.6 (Adaptive) and GPT-5.6 Sol is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 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.
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 (Adaptive) | Δ | GPT-5.6 Sol |
|---|---|---|---|
| Agentic | Claude Opus 4.6 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Sol92.0 |
| Coding | Claude Opus 4.6 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Sol64.6 |
| Knowledge | Claude Opus 4.6 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Sol94.6 |
| Math | Claude Opus 4.6 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Sol87.5 |
| Multimodal | Claude Opus 4.6 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Sol83.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.6 (Adaptive)Not available | GPT-5.6 Sol$5 input / $30 output | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Opus 4.6 (Adaptive)Not available | GPT-5.6 SolNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.6 (Adaptive)Not available | GPT-5.6 SolNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.6 (Adaptive)1M | GPT-5.6 Sol1M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Result |
|---|---|---|---|
| APEX-Agents-AASource | 33.0% | — | Not comparable |
| τ²-bench resultsSource | 92.1% | 85.1% | Claude Opus 4.6 (Adaptive) leads |
| 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% | Not comparable |
| GDPval-AASource | — | 61.8% | Not comparable |
| GDPval-AASource | — | 1736 | Not comparable |
| 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 |
Coding9 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Result |
|---|---|---|---|
| Vibe Code BenchSource | 53.50% | — | Not comparable |
| AA-SciCodeSource | 51.9% | 56.1% | GPT-5.6 Sol leads |
| 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% | Not comparable |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 43.7% | 58.9% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 89.6% | 94.1% | GPT-5.6 Sol leads |
| AA-HLESource | 36.7% | 47.2% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 13.5% | 21.7% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 46.4% | 58.5% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 61.3% | 88.8% | Claude Opus 4.6 (Adaptive) leads |
| GPQASource | — | 94.6% | Not comparable |
| GPQA-DSource | — | 94.6% | Not comparable |
| HealthBench ProfessionalSource | — | 60.5% | Not comparable |
| HealthBench HardSource | — | 33.1% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 92.2% | — | Not comparable |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA-IFBenchSource | 53.1% | 72.7% | GPT-5.6 Sol leads |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.6 (Adaptive) and GPT-5.6 Sol 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 (Adaptive) and GPT-5.6 Sol today?
GPT-5.6 Sol: $5.00 input / $30.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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