Qwen3.5 397B A17B vs Step 3.5 Flash 2603
Head-to-head specifications
| Metric | Qwen3.5 397B A17B | Step 3.5 Flash 2603 | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 29.0 | +13.8% |
| Context window | 512K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.65 | $0.06 | +983.3% |
| Output speed (tokens/s) | 58 | 248 | -76.6% |
| Access | Open weights | Proprietary API | — |
- Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 29.0).
- Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 10.8× cheaper.
- Qwen3.5 397B A17B offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: Qwen3.5 397B A17B or Step 3.5 Flash 2603?
Qwen3.5 397B A17B advantages
- General intelligence (+12%)
- Context window (+49%)
Step 3.5 Flash 2603 advantages
- Affordability (+91%)
- Output speed (+77%)
Which should you choose?
- Choose the Qwen3.5 397B A17B if you need the strongest overall reasoning and accuracy.
- Choose the Step 3.5 Flash 2603 if you want the lowest cost per token at scale.
- Choose the Qwen3.5 397B A17B if you work with long documents or large codebases.
Value for money
Step 3.5 Flash 2603 offers more intelligence per dollar (9.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Qwen3.5 397B A17B vs Step 3.5 Flash 2603: which should you choose?
Qwen3.5 397B A17B — Alibaba text model with an Intelligence Index of 33, a 512K-token context window and a blended price of $0.65/1M tokens (open weights).
Step 3.5 Flash 2603 — StepFun multimodal model with an Intelligence Index of 29, a 262K-token context window and a blended price of $0.06/1M tokens.
Qwen3.5 397B A17B vs Step 3.5 Flash 2603: Qwen3.5 397B A17B scores higher on the Intelligence Index. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 29.0). Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 10.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.5 397B A17B scores 33.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Qwen3.5 397B A17B accepts up to 512K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Step 3.5 Flash 2603 generates faster (248 vs 58 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Step 3.5 Flash 2603 is the cheaper model to run ($0.06 vs $0.65 per 1M tokens). Qwen3.5 397B A17B is open weights and Step 3.5 Flash 2603 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Qwen3.5 397B A17B better than the Step 3.5 Flash 2603?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 29.0).
What is the main difference between the Qwen3.5 397B A17B and the Step 3.5 Flash 2603?
Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 29.0). Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 10.8× cheaper.
Which is better value?
Step 3.5 Flash 2603 offers more intelligence per dollar (9.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Qwen3.5 397B A17B if you need the strongest overall reasoning and accuracy. Choose the Step 3.5 Flash 2603 if you want the lowest cost per token at scale.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.