Qwen3.5 397B A17B vs Trinity Large Thinking
Head-to-head specifications
| Metric | Qwen3.5 397B A17B | Trinity Large Thinking | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 28.0 | +17.9% |
| Context window | 512K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.65 | $0.24 | +170.8% |
| Output speed (tokens/s) | 58 | 157 | -63.1% |
| Access | Open weights | Open weights | — |
- Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 28.0).
- Trinity Large Thinking is the cheaper model to run at $0.24/1M blended tokens — about 2.7× cheaper.
- Trinity Large Thinking offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: Qwen3.5 397B A17B or Trinity Large Thinking?
Qwen3.5 397B A17B advantages
- General intelligence (+15%)
Trinity Large Thinking advantages
- Context window (+44%)
- Affordability (+63%)
- Output speed (+63%)
Which should you choose?
- Choose the Qwen3.5 397B A17B if you need the strongest overall reasoning and accuracy.
- Choose the Trinity Large Thinking if you work with long documents or large codebases.
Value for money
Trinity Large Thinking offers more intelligence per dollar (2.3× 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 Trinity Large Thinking: 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).
Trinity Large Thinking — Trinity text model with an Intelligence Index of 28, a 922K-token context window and a blended price of $0.24/1M tokens (open weights).
Qwen3.5 397B A17B vs Trinity Large Thinking: Qwen3.5 397B A17B scores higher on the Intelligence Index. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 28.0). Trinity Large Thinking is the cheaper model to run at $0.24/1M blended tokens — about 2.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.5 397B A17B scores 33.0 versus 28.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Trinity Large Thinking accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Trinity Large Thinking generates faster (157 vs 58 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Trinity Large Thinking is the cheaper model to run ($0.24 vs $0.65 per 1M tokens). Qwen3.5 397B A17B is open weights and Trinity Large Thinking 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 Trinity Large Thinking?
Trinity Large Thinking takes the overall edge, though Qwen3.5 397B A17B wins in specific areas worth weighing. Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 28.0).
What is the main difference between the Qwen3.5 397B A17B and the Trinity Large Thinking?
Qwen3.5 397B A17B leads overall capability (Intelligence Index 33.0 vs 28.0). Trinity Large Thinking is the cheaper model to run at $0.24/1M blended tokens — about 2.7× cheaper.
Which is better value?
Trinity Large Thinking offers more intelligence per dollar (2.3× 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 need the strongest overall reasoning and accuracy. Choose the Trinity Large Thinking if you work with long documents or large codebases.
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.