Model comparison
Qwen3.5-122B-A10B vs Qwen3.5-27B
Head-to-head evidence from 26 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Qwen3.5-122B-A10B #47 (Supported); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.5-122B-A10B and Qwen3.5-27B share 26 comparable benchmark results. 6 of 8 categories are comparable. 5 results are unique to Qwen3.5-122B-A10B; 2 to Qwen3.5-27B.
Updated July 23, 2026- Shared results
- 26
- Qwen3.5-122B-A10B only
- 5
- Qwen3.5-27B only
- 2
- Comparable categories
- 6 / 8
Pick Qwen3.5-27B if you want the stronger benchmark profile. Qwen3.5-122B-A10B only becomes the better choice if coding is the priority.
Confidence note. This is a partial-evidence comparison with 26 shared benchmark results across 7 evidence categories; 6 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.5-27B has the cleaner BenchAlign overall profile here, landing at 60.7 versus 60.56. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-27B's sharpest advantage is in instruction following, where it averages 95 against 93.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 49.4% to 41.6%. Qwen3.5-122B-A10B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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 | Qwen3.5-122B-A10B | Δ | Qwen3.5-27B |
|---|---|---|---|
| Coding | Qwen3.5-122B-A10B72.0 | Margin← 7.1 | Qwen3.5-27B64.9 |
| Agentic | Qwen3.5-122B-A10B56.4 | Margin← 4.4 | Qwen3.5-27B52.0 |
| Inst. Following | Qwen3.5-122B-A10B93.4 | Margin→ 1.6 | Qwen3.5-27B95.0 |
| Knowledge | Qwen3.5-122B-A10B83.6 | Margin← 0.9 | Qwen3.5-27B82.7 |
| Reasoning | Qwen3.5-122B-A10B60.2 | Margin→ 0.4 | Qwen3.5-27B60.6 |
| Multilingual | Qwen3.5-122B-A10B82.2 | MarginTie | Qwen3.5-27B82.2 |
| Multimodal | Qwen3.5-122B-A10B77.2 | MarginNo overlap | Qwen3.5-27BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 49.4%B 41.6%Winner: Qwen3.5-122B-A10BΔ 7.8Terminal-Bench 2.0: Qwen3.5-122B-A10B scored 49.4%; Qwen3.5-27B scored 41.6%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
BrowseComp
AgenticA 63.8%B 61%Winner: Qwen3.5-122B-A10BΔ 2.8BrowseComp: Qwen3.5-122B-A10B scored 63.8%; Qwen3.5-27B scored 61%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
OSWorld-Verified
AgenticA 58%B 56.2%Winner: Qwen3.5-122B-A10BΔ 1.8OSWorld-Verified: Qwen3.5-122B-A10B scored 58%; Qwen3.5-27B scored 56.2%. Qwen3.5-122B-A10B wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 93.4%B 95%Winner: Qwen3.5-27BΔ 1.6IFEval: Qwen3.5-122B-A10B scored 93.4%; Qwen3.5-27B scored 95%. Qwen3.5-27B wins this benchmark. - Source ↗
SuperGPQA
KnowledgeA 67.1%B 65.6%Winner: Qwen3.5-122B-A10BΔ 1.5SuperGPQA: Qwen3.5-122B-A10B scored 67.1%; Qwen3.5-27B scored 65.6%. Qwen3.5-122B-A10B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Qwen3.5-122B-A10B | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.5-122B-A10B$0 input / $0 output | Qwen3.5-27B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Qwen3.5-122B-A10BNot available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.5-122B-A10BNot available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.5-122B-A10B262K | Qwen3.5-27B262K | Listed context windows are equal. |
Benchmark Deep Dive
AgenticQwen3.5-122B-A10B wins8 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 49.4% | 41.6% | Qwen3.5-122B-A10B leads |
| BrowseCompSource | 63.8% | 61% | Qwen3.5-122B-A10B leads |
| OSWorld-VerifiedSource | 58% | 56.2% | Qwen3.5-122B-A10B leads |
| τ²-bench resultsSource | 93.6% | 93.9% | Qwen3.5-27B leads |
| AA Agentic IndexSource | 20.7% | — | Not comparable |
| GDPval-AASource | 23.9% | — | Not comparable |
| GDPval-AASource | 978 | — | Not comparable |
| Gert LabsSource | — | 39.41% | Not comparable |
CodingQwen3.5-122B-A10B wins4 benchmarks
ReasoningQwen3.5-27B wins3 benchmarks
KnowledgeQwen3.5-122B-A10B wins9 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProSource | 86.7% | 86.1% | Qwen3.5-122B-A10B leads |
| SuperGPQASource | 67.1% | 65.6% | Qwen3.5-122B-A10B leads |
| GPQASource | 86.6% | 85.5% | Qwen3.5-122B-A10B leads |
| Artificial Analysis Intelligence IndexSource | 32.3% | 33.8% | Qwen3.5-27B leads |
| AA-GPQA DiamondSource | 85.7% | 85.8% | Qwen3.5-27B leads |
| AA-HLESource | 23.4% | 22.2% | Qwen3.5-122B-A10B leads |
| AA-Omniscience IndexSource | -39.6% | -42.0% | Qwen3.5-122B-A10B leads |
| AA-Omniscience AccuracySource | 24.7% | 21.0% | Qwen3.5-122B-A10B leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 79.7% | Qwen3.5-27B leads |
MultilingualTie1 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | 82.2% | 82.2% | Tie |
Multimodal6 benchmarks
Frequently Asked Questions (7)
Which is better, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-27B is ahead on BenchLM's BenchAlign leaderboard, 60.7 to 60.56. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 49.4% and 41.6%.
Which is better for knowledge tasks, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 83.6 versus 82.7. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 72 versus 64.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for reasoning, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 60.2. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56.4 versus 52. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for instruction following, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 93.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Which is better for multilingual tasks, Qwen3.5-122B-A10B or Qwen3.5-27B?
Qwen3.5-122B-A10B and Qwen3.5-27B are effectively tied for multilingual tasks here, both landing at 82.2 on average.
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