Model comparison
Qwen3.5-122B-A10B vs Qwen3.5 397B
Head-to-head evidence from 28 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 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.5-122B-A10B and Qwen3.5 397B share 28 comparable benchmark results. 7 of 8 categories are comparable. 3 results are unique to Qwen3.5-122B-A10B; 27 to Qwen3.5 397B.
Updated July 23, 2026- Shared results
- 28
- Qwen3.5-122B-A10B only
- 3
- Qwen3.5 397B only
- 27
- Comparable categories
- 7 / 8
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 28 shared benchmark results across 7 evidence categories; 7 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.5-122B-A10B is clearly ahead on the BenchAlign aggregate, 60.56 to 57.01. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in knowledge, where it averages 83.6 against 56.6. The single biggest benchmark swing on the page is SWE-bench Verified, 72% to 76.2%. Qwen3.5 397B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 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. Qwen3.5-122B-A10B 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. Qwen3.5-122B-A10B gives you the larger context window at 262K, 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 | Qwen3.5-122B-A10B | Δ | Qwen3.5 397B |
|---|---|---|---|
| Knowledge | Qwen3.5-122B-A10B83.6 | Margin← 27.0 | Qwen3.5 397B56.6 |
| Coding | Qwen3.5-122B-A10B72.0 | Margin← 5.5 | Qwen3.5 397B66.5 |
| Reasoning | Qwen3.5-122B-A10B60.2 | Margin→ 3.0 | Qwen3.5 397B63.2 |
| Multilingual | Qwen3.5-122B-A10B82.2 | Margin→ 2.5 | Qwen3.5 397B84.7 |
| Multimodal | Qwen3.5-122B-A10B77.2 | Margin→ 2.4 | Qwen3.5 397B79.6 |
| Inst. Following | Qwen3.5-122B-A10B93.4 | Margin← 0.8 | Qwen3.5 397B92.6 |
| Agentic | Qwen3.5-122B-A10B56.4 | Margin→ 0.1 | Qwen3.5 397B56.5 |
| Math | Qwen3.5-122B-A10BNot measured | MarginNo overlap | Qwen3.5 397B90.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 72%B 76.2%Winner: Qwen3.5 397BΔ 4.2SWE-bench Verified: Qwen3.5-122B-A10B scored 72%; Qwen3.5 397B scored 76.2%. Qwen3.5 397B wins this benchmark. - Source ↗
CharXiv
MultimodalA 77.2%B 80.8%Winner: Qwen3.5 397BΔ 3.6CharXiv: Qwen3.5-122B-A10B scored 77.2%; Qwen3.5 397B scored 80.8%. Qwen3.5 397B wins this benchmark. - Source ↗
SuperGPQA
KnowledgeA 67.1%B 70.4%Winner: Qwen3.5 397BΔ 3.3SuperGPQA: Qwen3.5-122B-A10B scored 67.1%; Qwen3.5 397B scored 70.4%. Qwen3.5 397B wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 49.4%B 52.5%Winner: Qwen3.5 397BΔ 3.1Terminal-Bench 2.0: Qwen3.5-122B-A10B scored 49.4%; Qwen3.5 397B scored 52.5%. Qwen3.5 397B wins this benchmark. - Source ↗
LongBench v2
ReasoningA 60.2%B 63.2%Winner: Qwen3.5 397BΔ 3LongBench v2: Qwen3.5-122B-A10B scored 60.2%; Qwen3.5 397B scored 63.2%. Qwen3.5 397B 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 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.5-122B-A10B$0 input / $0 output | Qwen3.5 397B$0.6 input / $3.6 output | Qwen3.5-122B-A10B has the lower combined listed price. |
| Generation speedtokens per second | Qwen3.5-122B-A10BNot available | Qwen3.5 397B96 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.5-122B-A10BNot available | Qwen3.5 397B2.44 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.5-122B-A10B262K | Qwen3.5 397B128K | Qwen3.5-122B-A10B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.5 397B wins19 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 49.4% | 52.5% | Qwen3.5 397B leads |
| BrowseCompSource | 63.8% | 62% | Qwen3.5-122B-A10B leads |
| OSWorld-VerifiedSource | 58% | — | Not comparable |
| τ²-bench resultsSource | 93.6% | 95.6% | Qwen3.5 397B leads |
| AA Agentic IndexSource | 20.7% | 19.9% | Qwen3.5-122B-A10B leads |
| GDPval-AASource | 23.9% | 23.1% | Qwen3.5-122B-A10B leads |
| GDPval-AASource | 978 | 962 | Qwen3.5-122B-A10B leads |
| 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 |
| ToolathlonSource | — | 36.3% | 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 |
CodingQwen3.5-122B-A10B wins5 benchmarks
ReasoningQwen3.5 397B wins4 benchmarks
KnowledgeQwen3.5-122B-A10B wins12 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5 397B | Result |
|---|---|---|---|
| MMLU-ProSource | 86.7% | 87.8% | Qwen3.5 397B leads |
| SuperGPQASource | 67.1% | 70.4% | Qwen3.5 397B leads |
| GPQASource | 86.6% | 88.4% | Qwen3.5 397B leads |
| Artificial Analysis Intelligence IndexSource | 32.3% | 33.7% | Qwen3.5 397B leads |
| AA-GPQA DiamondSource | 85.7% | 89.3% | Qwen3.5 397B leads |
| AA-HLESource | 23.4% | 27.3% | Qwen3.5 397B leads |
| AA-Omniscience IndexSource | -39.6% | -29.8% | Qwen3.5 397B leads |
| AA-Omniscience AccuracySource | 24.7% | 31.4% | Qwen3.5 397B leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 89.1% | Qwen3.5-122B-A10B leads |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
Math5 benchmarks
MultilingualQwen3.5 397B wins2 benchmarks
MultimodalQwen3.5 397B wins9 benchmarks
| Benchmark | Qwen3.5-122B-A10B | Qwen3.5 397B | Result |
|---|---|---|---|
| MMMUSource | 83.9% | — | Not comparable |
| MMVUSource | 74.7% | — | Not comparable |
| MathVisionSource | 86.2% | 88.6% | Qwen3.5 397B leads |
| CharXivSource | 77.2% | 80.8% | Qwen3.5 397B leads |
| V*Source | 93.2% | 95.8% | Qwen3.5 397B leads |
| AA-MMMU-ProSource | 75.0% | 77.3% | Qwen3.5 397B leads |
| MMMU-ProSource | — | 79% | Not comparable |
| VideoMMMUSource | — | 84.7% | Not comparable |
| ScreenSpot ProSource | — | 65.6% | Not comparable |
Frequently Asked Questions (8)
Which is better, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5-122B-A10B is ahead on BenchLM's BenchAlign leaderboard, 60.56 to 57.01. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72% and 76.2%.
Which is better for knowledge tasks, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 83.6 versus 56.6. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 72 versus 66.5. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for reasoning, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5 397B has the edge for reasoning in this comparison, averaging 63.2 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.5 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, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 77.2. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
Which is better for instruction following, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5-122B-A10B has the edge for instruction following in this comparison, averaging 93.4 versus 92.6. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
Which is better for multilingual tasks, Qwen3.5-122B-A10B or Qwen3.5 397B?
Qwen3.5 397B has the edge for multilingual tasks in this comparison, averaging 84.7 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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