Model comparison
Agents-A1 vs Qwen3.5 397B
Head-to-head evidence from 5 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Agents-A1 unranked (Not scored); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Agents-A1 and Qwen3.5 397B share 5 comparable benchmark results. 4 of 8 categories are comparable. 1 result is unique to Agents-A1; 50 to Qwen3.5 397B.
Updated July 23, 2026- Shared results
- 5
- Agents-A1 only
- 1
- Qwen3.5 397B only
- 50
- Comparable categories
- 4 / 8
Treat this as a split decision. Agents-A1 makes more sense if agentic is the priority or you need the larger 262K context window; Qwen3.5 397B is the better fit if knowledge 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 5 shared benchmark results across 4 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Agents-A1 and Qwen3.5 397B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Agents-A1 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. Agents-A1 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 | Agents-A1 | Δ | Qwen3.5 397B |
|---|---|---|---|
| Agentic | Agents-A175.5 | Margin← 19.0 | Qwen3.5 397B56.5 |
| Knowledge | Agents-A147.6 | Margin→ 9.0 | Qwen3.5 397B56.6 |
| Reasoning | Agents-A160.2 | Margin→ 3.0 | Qwen3.5 397B63.2 |
| Inst. Following | Agents-A194.8 | Margin← 2.2 | Qwen3.5 397B92.6 |
| Coding | Agents-A1Not measured | MarginNo overlap | Qwen3.5 397B66.5 |
| Math | Agents-A1Not measured | MarginNo overlap | Qwen3.5 397B90.6 |
| Multilingual | Agents-A1Not measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Multimodal | Agents-A1Not measured | MarginNo overlap | Qwen3.5 397B79.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 47.6%B 28.7%Winner: Agents-A1Δ 18.9HLE: Agents-A1 scored 47.6%; Qwen3.5 397B scored 28.7%. Agents-A1 wins this benchmark. - Source ↗
BrowseComp
AgenticA 75.5%B 62%Winner: Agents-A1Δ 13.5BrowseComp: Agents-A1 scored 75.5%; Qwen3.5 397B scored 62%. Agents-A1 wins this benchmark. - Source ↗
LongBench v2
ReasoningA 60.2%B 63.2%Winner: Qwen3.5 397BΔ 3LongBench v2: Agents-A1 scored 60.2%; Qwen3.5 397B scored 63.2%. Qwen3.5 397B wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 94.8%B 92.6%Winner: Agents-A1Δ 2.2IFEval: Agents-A1 scored 94.8%; Qwen3.5 397B scored 92.6%. Agents-A1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Agents-A1 | Qwen3.5 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Agents-A1Not available | Qwen3.5 397B$0.6 input / $3.6 output | A complete price comparison is not available. |
| Generation speedtokens per second | Agents-A1Not available | Qwen3.5 397B96 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Agents-A1Not available | Qwen3.5 397B2.44 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Agents-A1262K | Qwen3.5 397B128K | Agents-A1 lists the larger context window. |
Benchmark Deep Dive
AgenticAgents-A1 wins19 benchmarks
| Benchmark | Agents-A1 | Qwen3.5 397B | Result |
|---|---|---|---|
| BrowseCompSource | 75.5% | 62% | Agents-A1 leads |
| HLE w/ toolsSource | 47.6% | — | Not comparable |
| VITA-BenchSource | 38.8% | 43.7% | Qwen3.5 397B leads |
| Terminal-Bench 2.0Source | — | 52.5% | Not comparable |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | 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 |
| τ²-bench resultsSource | — | 95.6% | Not comparable |
| Gert LabsSource | — | 46.76% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| AA Agentic IndexSource | — | 19.9% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
| GDPval-AASource | — | 23.1% | Not comparable |
| GDPval-AASource | — | 962 | Not comparable |
Coding5 benchmarks
ReasoningQwen3.5 397B wins4 benchmarks
KnowledgeQwen3.5 397B wins12 benchmarks
| Benchmark | Agents-A1 | Qwen3.5 397B | Result |
|---|---|---|---|
| HLESource | 47.6% | 28.7% | Agents-A1 leads |
| GPQASource | — | 88.4% | Not comparable |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ProSource | — | 87.8% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.7% | Not comparable |
| AA-GPQA DiamondSource | — | 89.3% | Not comparable |
| AA-HLESource | — | 27.3% | Not comparable |
| AA-Omniscience IndexSource | — | -29.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 31.4% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 89.1% | Not comparable |
Math5 benchmarks
Multilingual2 benchmarks
Multimodal7 benchmarks
Frequently Asked Questions (5)
Which is better, Agents-A1 or Qwen3.5 397B?
Agents-A1 and Qwen3.5 397B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Agents-A1 or Qwen3.5 397B?
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 56.6 versus 47.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for reasoning, Agents-A1 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, Agents-A1 or Qwen3.5 397B?
Agents-A1 has the edge for agentic tasks in this comparison, averaging 75.5 versus 56.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Which is better for instruction following, Agents-A1 or Qwen3.5 397B?
Agents-A1 has the edge for instruction following in this comparison, averaging 94.8 versus 92.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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