Model comparison
GPT-5.6 Sol vs Qwen3.5-35B-A3B
Head-to-head evidence from 15 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); Qwen3.5-35B-A3B #72 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and Qwen3.5-35B-A3B share 15 comparable benchmark results. 3 of 8 categories are comparable. 31 results are unique to GPT-5.6 Sol; 13 to Qwen3.5-35B-A3B.
Updated July 23, 2026- Shared results
- 15
- GPT-5.6 Sol only
- 31
- Qwen3.5-35B-A3B only
- 13
- Comparable categories
- 3 / 8
Pick GPT-5.6 Sol if you want the stronger benchmark profile. Qwen3.5-35B-A3B only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 evidence categories; 3 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 56.97. 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 agentic, where it averages 92 against 51. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 91.9% to 40.5%.
GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. GPT-5.6 Sol gives you the larger context window at 1M, compared with 262K for Qwen3.5-35B-A3B.
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 | Qwen3.5-35B-A3B |
|---|---|---|---|
| Agentic | GPT-5.6 Sol92.0 | Margin← 41.0 | Qwen3.5-35B-A3B51.0 |
| Knowledge | GPT-5.6 Sol94.6 | Margin← 13.0 | Qwen3.5-35B-A3B81.6 |
| Coding | GPT-5.6 Sol64.6 | Margin← 4.0 | Qwen3.5-35B-A3B60.6 |
| Reasoning | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5-35B-A3B59.0 |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | Qwen3.5-35B-A3BNot measured |
| Multilingual | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5-35B-A3B81.0 |
| Multimodal | GPT-5.6 Sol83.0 | MarginNo overlap | Qwen3.5-35B-A3BNot measured |
| Inst. Following | GPT-5.6 SolNot measured | MarginNo overlap | Qwen3.5-35B-A3B91.9 |
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 40.5%Winner: GPT-5.6 SolΔ 51.4Terminal-Bench 2.0: GPT-5.6 Sol scored 91.9%; Qwen3.5-35B-A3B scored 40.5%. GPT-5.6 Sol wins this benchmark. - Source ↗
BrowseComp
AgenticA 92.2%B 61%Winner: GPT-5.6 SolΔ 31.2BrowseComp: GPT-5.6 Sol scored 92.2%; Qwen3.5-35B-A3B scored 61%. GPT-5.6 Sol wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.6%B 84.2%Winner: GPT-5.6 SolΔ 10.4GPQA: GPT-5.6 Sol scored 94.6%; Qwen3.5-35B-A3B scored 84.2%. 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 | Qwen3.5-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | Qwen3.5-35B-A3B$0 input / $0 output | Qwen3.5-35B-A3B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | Qwen3.5-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | Qwen3.5-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | Qwen3.5-35B-A3B262K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.6 Sol wins19 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5-35B-A3B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | 40.5% | GPT-5.6 Sol leads |
| BrowseCompSource | 92.2% | 61% | GPT-5.6 Sol leads |
| 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 |
| τ²-bench resultsSource | 85.1% | 89.2% | Qwen3.5-35B-A3B leads |
| 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 |
| OSWorld-VerifiedSource | — | 54.5% | Not comparable |
| Gert LabsSource | — | 28.96% | Not comparable |
CodingGPT-5.6 Sol wins10 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5-35B-A3B | 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% | — | Not comparable |
| AA-SciCodeSource | 56.1% | 37.7% | GPT-5.6 Sol leads |
| SWE-bench VerifiedSource | — | 69.2% | Not comparable |
| SWE-RebenchSource | — | 53.7% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.6 Sol wins12 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5-35B-A3B | Result |
|---|---|---|---|
| GPQASource | 94.6% | 84.2% | 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% | 29.3% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 84.5% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 19.7% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -46.4% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 20.5% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 84.0% | Qwen3.5-35B-A3B leads |
| MMLU-ProSource | — | 85.3% | Not comparable |
| SuperGPQASource | — | 63.4% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | GPT-5.6 Sol | Qwen3.5-35B-A3B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 81% | Not comparable |
Multimodal7 benchmarks
Frequently Asked Questions (4)
Which is better, GPT-5.6 Sol or Qwen3.5-35B-A3B?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 56.97. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 91.9% and 40.5%.
Which is better for knowledge tasks, GPT-5.6 Sol or Qwen3.5-35B-A3B?
GPT-5.6 Sol has the edge for knowledge tasks in this comparison, averaging 94.6 versus 81.6. 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 Qwen3.5-35B-A3B?
GPT-5.6 Sol has the edge for coding in this comparison, averaging 64.6 versus 60.6. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.5-35B-A3B?
GPT-5.6 Sol has the edge for agentic tasks in this comparison, averaging 92 versus 51. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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