Model comparison
GPT-5.6 Sol vs Qwen3.5 397B
Head-to-head evidence from 22 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (Supported); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and Qwen3.5 397B share 22 comparable benchmark results. 5 of 8 categories are comparable. 24 results are unique to GPT-5.6 Sol; 33 to Qwen3.5 397B.
Updated July 23, 2026- Shared results
- 22
- GPT-5.6 Sol only
- 24
- Qwen3.5 397B only
- 33
- Comparable categories
- 5 / 8
Pick GPT-5.6 Sol if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 22 shared benchmark results across 6 evidence categories; 5 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.6 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 57.01. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.6 Sol's sharpest advantage is in knowledge, where it averages 94.6 against 56.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 91.9% to 52.5%. Qwen3.5 397B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.60 input / $3.60 output per 1M tokens for Qwen3.5 397B. That is roughly 8.3x on output cost alone. GPT-5.6 Sol is the reasoning model in the pair, while Qwen3.5 397B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.6 Sol gives you the larger context window at 1M, compared with 128K for Qwen3.5 397B.
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 | Qwen3.5 397B |
|---|---|---|---|
| Knowledge | GPT-5.6 Sol94.6 | Margin← 38.0 | Qwen3.5 397B56.6 |
| Agentic | GPT-5.6 Sol92.0 | Margin← 35.5 | Qwen3.5 397B56.5 |
| Multimodal | GPT-5.6 Sol83.0 | Margin← 3.4 | Qwen3.5 397B79.6 |
| Math | GPT-5.6 Sol87.5 | Margin→ 3.1 | Qwen3.5 397B90.6 |
| Coding | GPT-5.6 Sol64.6 | Margin→ 1.9 | Qwen3.5 397B66.5 |
| Reasoning | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5 397B63.2 |
| Multilingual | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Inst. Following | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5 397B92.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 91.9%B 52.5%Winner: GPT-5.6 SolΔ 39.4Terminal-Bench 2.0: GPT-5.6 Sol scored 91.9%; Qwen3.5 397B scored 52.5%. GPT-5.6 Sol wins this benchmark. - Source ↗
BrowseComp
AgenticA 92.2%B 62%Winner: GPT-5.6 SolΔ 30.2BrowseComp: GPT-5.6 Sol scored 92.2%; Qwen3.5 397B scored 62%. GPT-5.6 Sol wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.6%B 50.9%Winner: GPT-5.6 SolΔ 13.7SWE-bench Pro: GPT-5.6 Sol scored 64.6%; Qwen3.5 397B scored 50.9%. GPT-5.6 Sol wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.6%B 88.4%Winner: GPT-5.6 SolΔ 6.2GPQA: GPT-5.6 Sol scored 94.6%; Qwen3.5 397B scored 88.4%. GPT-5.6 Sol wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 83%B 79%Winner: GPT-5.6 SolΔ 4MMMU-Pro: GPT-5.6 Sol scored 83%; Qwen3.5 397B scored 79%. 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 | Qwen3.5 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | Qwen3.5 397B$0.6 input / $3.6 output | Qwen3.5 397B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | Qwen3.5 397B96 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | Qwen3.5 397B2.44 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | Qwen3.5 397B128K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.6 Sol wins28 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | 52.5% | GPT-5.6 Sol leads |
| BrowseCompSource | 92.2% | 62% | GPT-5.6 Sol leads |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | 36.3% | GPT-5.6 Sol leads |
| AA Agentic IndexSource | 54.0% | 19.9% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 95.6% | Qwen3.5 397B leads |
| GDPval-AASource | 61.8% | 23.1% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 962 | 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 |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| VITA-BenchSource | — | 43.7% | Not comparable |
| DeepPlanningSource | — | 37.6% | Not comparable |
| MCP AtlasSource | — | 46.1% | Not comparable |
| MCP-TasksSource | — | 74.2% | Not comparable |
| WideResearchSource | — | 74.0% | Not comparable |
| Gert LabsSource | — | 46.76% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
CodingQwen3.5 397B wins10 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5 397B | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | 50.9% | GPT-5.6 Sol leads |
| 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% | 48.2% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 42.0% | GPT-5.6 Sol leads |
| SWE-bench VerifiedSource | — | 76.2% | Not comparable |
| LiveCodeBench v6Source | — | 83.6% | Not comparable |
Reasoning6 benchmarks
KnowledgeGPT-5.6 Sol wins15 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5 397B | Result |
|---|---|---|---|
| GPQASource | 94.6% | 88.4% | GPT-5.6 Sol leads |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 33.7% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 89.3% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 27.3% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -29.8% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 31.4% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 89.1% | GPT-5.6 Sol leads |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ProSource | — | 87.8% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
MathQwen3.5 397B wins8 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5 397B | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | 89% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 89.000% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 83.000% | — | Not comparable |
| AIME26Source | — | 93.3% | Not comparable |
| HMMT Feb 2025Source | — | 94.8% | Not comparable |
| HMMT Nov 2025Source | — | 92.7% | Not comparable |
| HMMT Feb 2026Source | — | 87.9% | Not comparable |
| MMAnswerBenchSource | — | 80.9% | Not comparable |
Multilingual2 benchmarks
MultimodalGPT-5.6 Sol wins8 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5 397B | Result |
|---|---|---|---|
| MMMU-ProSource | 83% | 79% | GPT-5.6 Sol leads |
| MMMU-Pro w/ PythonSource | 84.6% | — | Not comparable |
| AA-MMMU-ProSource | 83.4% | 77.3% | GPT-5.6 Sol leads |
| MathVisionSource | — | 88.6% | Not comparable |
| CharXivSource | — | 80.8% | Not comparable |
| VideoMMMUSource | — | 84.7% | Not comparable |
| ScreenSpot ProSource | — | 65.6% | Not comparable |
| V*Source | — | 95.8% | Not comparable |
Frequently Asked Questions (6)
Which is better, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 57.01. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 91.9% and 52.5%.
Which is better for knowledge tasks, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol has the edge for knowledge tasks in this comparison, averaging 94.6 versus 56.6. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-5.6 Sol or Qwen3.5 397B?
Qwen3.5 397B has the edge for coding in this comparison, averaging 66.5 versus 64.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.6 Sol or Qwen3.5 397B?
Qwen3.5 397B has the edge for math in this comparison, averaging 90.6 versus 87.5. GPT-5.6 Sol stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol has the edge for agentic tasks in this comparison, averaging 92 versus 56.5. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol has the edge for multimodal and grounded tasks in this comparison, averaging 83 versus 79.6. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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