Compare · ModelsLive · 2 picked · head to head
Qwen3.5 397B A17B vs Step 3.5 Flash
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.5 397B A17B wins on 5/10 benchmarks
Qwen3.5 397B A17B wins 5 of 10 shared benchmarks. Leads in arena · knowledge.
Category leads
speed·Step 3.5 Flasharena·Qwen3.5 397B A17Bmath·Step 3.5 Flashknowledge·Qwen3.5 397B A17Blanguage·Step 3.5 Flashcoding·Step 3.5 Flash
Hype vs Reality
Attention vs performance
Qwen3.5 397B A17B
#5 by perf·no signal
Step 3.5 Flash
#9 by perf·#11 by attention
Best value
Step 3.5 Flash
7.0x better value than Qwen3.5 397B A17B
Qwen3.5 397B A17B
55.3 pts/$
$1.42/M
Step 3.5 Flash
384.5 pts/$
$0.20/M
Vendor risk
Mixed exposure
One or more vendors flagged
Alibaba (Qwen)
$293.0B·Tier 1
StepFun
$5.0B·Tier 1
Head to head
10 benchmarks · 2 models
Qwen3.5 397B A17BStep 3.5 Flash
Artificial Analysis · Agentic Index
Step 3.5 Flash leads by +32.1
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Qwen3.5 397B A17B
19.9
Step 3.5 Flash
52.0
Artificial Analysis · Coding Index
Qwen3.5 397B A17B leads by +16.6
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Qwen3.5 397B A17B
48.2
Step 3.5 Flash
31.6
Artificial Analysis · Quality Index
Step 3.5 Flash leads by +4.1
Qwen3.5 397B A17B
33.7
Step 3.5 Flash
37.8
Chatbot Arena Elo · Overall
Qwen3.5 397B A17B leads by +48.5
Qwen3.5 397B A17B
1443.7
Step 3.5 Flash
1395.2
OpenCompass · AIME2025
Step 3.5 Flash leads by +3.4
Qwen3.5 397B A17B
92.3
Step 3.5 Flash
95.7
OpenCompass · GPQA-Diamond
Qwen3.5 397B A17B leads by +4.7
Qwen3.5 397B A17B
88.4
Step 3.5 Flash
83.7
OpenCompass · HLE
Qwen3.5 397B A17B leads by +5.9
Qwen3.5 397B A17B
27.5
Step 3.5 Flash
21.6
OpenCompass · IFEval
Step 3.5 Flash leads by +1.7
Qwen3.5 397B A17B
91.5
Step 3.5 Flash
93.2
OpenCompass · LiveCodeBenchV6
Step 3.5 Flash leads by +0.9
Qwen3.5 397B A17B
83.0
Step 3.5 Flash
83.9
OpenCompass · MMLU-Pro
Qwen3.5 397B A17B leads by +4.1
Qwen3.5 397B A17B
87.6
Step 3.5 Flash
83.5
Full benchmark table
| Benchmark | Qwen3.5 397B A17B | Step 3.5 Flash |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 19.9 | 52.0 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 48.2 | 31.6 |
Artificial Analysis · Quality Index | 33.7 | 37.8 |
Chatbot Arena Elo · Overall | 1443.7 | 1395.2 |
OpenCompass · AIME2025 | 92.3 | 95.7 |
OpenCompass · GPQA-Diamond | 88.4 | 83.7 |
OpenCompass · HLE | 27.5 | 21.6 |
OpenCompass · IFEval | 91.5 | 93.2 |
OpenCompass · LiveCodeBenchV6 | 83.0 | 83.9 |
OpenCompass · MMLU-Pro | 87.6 | 83.5 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.39 | $2.45 | 256K tokens (~128 books) | $9.01 | |
| $0.10 | $0.30 | 262K tokens (~131 books) | $1.50 |