Model comparison
GPT-5.5 vs Qwen3.5-122B-A10B
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 #9 (Estimated); Qwen3.5-122B-A10B #47 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and Qwen3.5-122B-A10B share 20 comparable benchmark results. 5 of 8 categories are comparable. 37 results are unique to GPT-5.5; 11 to Qwen3.5-122B-A10B.
Updated July 23, 2026- Shared results
- 20
- GPT-5.5 only
- 37
- Qwen3.5-122B-A10B only
- 11
- Comparable categories
- 5 / 8
Pick GPT-5.5 if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 20 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-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 60.56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 56.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82% to 49.4%. Qwen3.5-122B-A10B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 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 Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 262K for Qwen3.5-122B-A10B.
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-5.5 | Δ | Qwen3.5-122B-A10B |
|---|---|---|---|
| Knowledge | GPT-5.557.8 | Margin→ 25.8 | Qwen3.5-122B-A10B83.6 |
| Agentic | GPT-5.581.6 | Margin← 25.2 | Qwen3.5-122B-A10B56.4 |
| Reasoning | GPT-5.585.0 | Margin← 24.8 | Qwen3.5-122B-A10B60.2 |
| Coding | GPT-5.558.6 | Margin→ 13.4 | Qwen3.5-122B-A10B72.0 |
| Multimodal | GPT-5.570.4 | Margin→ 6.8 | Qwen3.5-122B-A10B77.2 |
| Math | GPT-5.547.6 | MarginNo overlap | Qwen3.5-122B-A10BNot measured |
| Multilingual | GPT-5.5Not measured | MarginNo overlap | Qwen3.5-122B-A10B82.2 |
| Inst. Following | GPT-5.5Not measured | MarginNo overlap | Qwen3.5-122B-A10B93.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 82%B 49.4%Winner: GPT-5.5Δ 32.6Terminal-Bench 2.0: GPT-5.5 scored 82%; Qwen3.5-122B-A10B scored 49.4%. GPT-5.5 wins this benchmark. - Source ↗
OSWorld-Verified
AgenticA 78.7%B 58%Winner: GPT-5.5Δ 20.7OSWorld-Verified: GPT-5.5 scored 78.7%; Qwen3.5-122B-A10B scored 58%. GPT-5.5 wins this benchmark. - Source ↗
BrowseComp
AgenticA 84.4%B 63.8%Winner: GPT-5.5Δ 20.6BrowseComp: GPT-5.5 scored 84.4%; Qwen3.5-122B-A10B scored 63.8%. GPT-5.5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 93.6%B 86.6%Winner: GPT-5.5Δ 7GPQA: GPT-5.5 scored 93.6%; Qwen3.5-122B-A10B scored 86.6%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | Qwen3.5-122B-A10B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | Qwen3.5-122B-A10B$0 input / $0 output | Qwen3.5-122B-A10B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | Qwen3.5-122B-A10BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | Qwen3.5-122B-A10BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | Qwen3.5-122B-A10B262K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 wins24 benchmarks
| Benchmark | GPT-5.5 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 49.4% | GPT-5.5 leads |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | 63.8% | GPT-5.5 leads |
| OSWorld-VerifiedSource | 78.7% | 58% | GPT-5.5 leads |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | 93.6% | GPT-5.5 leads |
| AA Agentic IndexSource | 44.9% | 20.7% | GPT-5.5 leads |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.5% | 23.9% | GPT-5.5 leads |
| GDPval-AASource | 1490 | 978 | GPT-5.5 leads |
| Gert LabsSource | 72.93% | — | Not comparable |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA BriefcaseSource | 1154 | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA Harvey LABSource | 86.3% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| terminalBenchHardSource | 60.6% | — | Not comparable |
| aaTerminalBench21Source | 84.3% | — | Not comparable |
CodingQwen3.5-122B-A10B wins10 benchmarks
| Benchmark | GPT-5.5 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 82.0% | — | Not comparable |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | 45.7% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 42.0% | GPT-5.5 leads |
| FrontierCode 1.1 MainSource | 43.0% | — | Not comparable |
| SWE-bench VerifiedSource | — | 72% | Not comparable |
ReasoningGPT-5.5 wins6 benchmarks
KnowledgeQwen3.5-122B-A10B wins12 benchmarks
| Benchmark | GPT-5.5 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| GPQASource | 93.6% | 86.6% | GPT-5.5 leads |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 32.3% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 85.7% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 23.4% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | -39.6% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 24.7% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 85.5% | Tie |
| MMLU-ProSource | — | 86.7% | Not comparable |
| SuperGPQASource | — | 67.1% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | GPT-5.5 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
MultimodalQwen3.5-122B-A10B wins10 benchmarks
| Benchmark | GPT-5.5 | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 83.2% | — | Not comparable |
| OfficeQA ProSource | 54.1% | — | Not comparable |
| AA-MMMU-ProSource | 79.9% | 75.0% | GPT-5.5 leads |
| Design Arena WebsiteSource | 1282 | — | Not comparable |
| MMMUSource | — | 83.9% | Not comparable |
| MMVUSource | — | 74.7% | Not comparable |
| MathVisionSource | — | 86.2% | Not comparable |
| CharXivSource | — | 77.2% | Not comparable |
| V*Source | — | 93.2% | Not comparable |
Frequently Asked Questions (6)
Which is better, GPT-5.5 or Qwen3.5-122B-A10B?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 60.56. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82% and 49.4%.
Which is better for knowledge tasks, GPT-5.5 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 83.6 versus 57.8. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-5.5 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 72 versus 58.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for reasoning, GPT-5.5 or Qwen3.5-122B-A10B?
GPT-5.5 has the edge for reasoning in this comparison, averaging 85 versus 60.2. Inside this category, CritPt is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.5 or Qwen3.5-122B-A10B?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 56.4. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.5 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the edge for multimodal and grounded tasks in this comparison, averaging 77.2 versus 70.4. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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