Qwen3.5 397B A17B vs Qwen3.5 Omni Plus
Head-to-head specifications
| Metric | Qwen3.5 397B A17B | Qwen3.5 Omni Plus | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 32.0 | +3.1% |
| Context window | 512K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.65 | $0.63 | +3.2% |
| Output speed (tokens/s) | 58 | 53 | +9.4% |
| Access | Open weights | Open weights | — |
- Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 32.0).
- Both cost about the same to run (~$0.63/1M blended tokens), so capability and speed should decide.
- Qwen3.5 397B A17B offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: Qwen3.5 397B A17B or Qwen3.5 Omni Plus?
Qwen3.5 397B A17B advantages
- Context window (+22%)
- Output speed (+9%)
Qwen3.5 Omni Plus advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Qwen3.5 397B A17B if you work with long documents or large codebases.
- Choose the Qwen3.5 397B A17B if low latency and fast generation matter for your application.
Value for money
Qwen3.5 Omni Plus offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Qwen3.5 397B A17B vs Qwen3.5 Omni Plus: 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).
Qwen3.5 Omni Plus — Alibaba multimodal model with an Intelligence Index of 32, a 400K-token context window and a blended price of $0.63/1M tokens (open weights).
Qwen3.5 397B A17B vs Qwen3.5 Omni Plus: Qwen3.5 397B A17B scores higher on the Intelligence Index. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 32.0). Both cost about the same to run (~$0.63/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.5 397B A17B scores 33.0 versus 32.0. 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, Qwen3.5 397B A17B generates faster (58 vs 53 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 Omni Plus is the cheaper model to run ($0.63 vs $0.65 per 1M tokens). Qwen3.5 397B A17B is open weights and Qwen3.5 Omni Plus is open weights. 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 Qwen3.5 Omni Plus?
Qwen3.5 397B A17B is the clearly stronger overall choice, winning most of the dimensions that matter. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 32.0).
What is the main difference between the Qwen3.5 397B A17B and the Qwen3.5 Omni Plus?
Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 32.0). Both cost about the same to run (~$0.63/1M blended tokens), so capability and speed should decide.
Which is better value?
Qwen3.5 Omni Plus offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Qwen3.5 397B A17B if you work with long documents or large codebases. Choose the Qwen3.5 397B A17B if low latency and fast generation matter for your application.
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.