Qwen3.5-397B-A17B vs Trinity-Large-Thinking
Qwen3.5-397B-A17B (2026) and Trinity-Large-Thinking (2026) are frontier reasoning models from Alibaba and Arcee AI. Qwen3.5-397B-A17B ships a 262K-token context window, while Trinity-Large-Thinking ships a 256K-token context window. On Google-Proof Q&A, Qwen3.5-397B-A17B leads by a hair. On pricing, Trinity-Large-Thinking costs $0.22/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Trinity-Large-Thinking is ~77% cheaper at $0.22/1M; pay for Qwen3.5-397B-A17B only for long-context analysis.
Specs
| Released | 2026-02-16 | 2026-04-01 |
| Context window | 262K | 256K |
| Parameters | 397B | 400B |
| Architecture | MoE | Sparse Mixture of Experts (MoE) |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Qwen3.5-397B-A17B | Trinity-Large-Thinking | |
|---|---|---|
| Input price | $0.39/1M tokens | $0.22/1M tokens |
| Output price | $2.34/1M tokens | $0.85/1M tokens |
| Providers |
Capabilities
| Qwen3.5-397B-A17B | Trinity-Large-Thinking | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Qwen3.5-397B-A17B | Trinity-Large-Thinking |
|---|---|---|
| Google-Proof Q&A | 89.3 | 89.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Qwen3.5-397B-A17B at 89.3 and Trinity-Large-Thinking at 89.2, with Qwen3.5-397B-A17B ahead by 0.1 points. The largest visible gap is 0.1 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on multimodal input: Qwen3.5-397B-A17B, reasoning mode: Trinity-Large-Thinking, function calling: Trinity-Large-Thinking, and tool use: Trinity-Large-Thinking. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens, while Trinity-Large-Thinking lists $0.22/1M input and $0.85/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Trinity-Large-Thinking lower by about $0.57 per million blended tokens. Availability is 1 providers versus 2, so concentration risk also matters.
Choose Qwen3.5-397B-A17B when long-context analysis and larger context windows are central to the workload. Choose Trinity-Large-Thinking when reasoning depth, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Qwen3.5-397B-A17B or Trinity-Large-Thinking?
Qwen3.5-397B-A17B supports 262K tokens, while Trinity-Large-Thinking supports 256K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Qwen3.5-397B-A17B or Trinity-Large-Thinking?
Trinity-Large-Thinking is cheaper on tracked token pricing. Qwen3.5-397B-A17B costs $0.39/1M input and $2.34/1M output tokens. Trinity-Large-Thinking costs $0.22/1M input and $0.85/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Qwen3.5-397B-A17B or Trinity-Large-Thinking open source?
Qwen3.5-397B-A17B is listed under Apache 2.0. Trinity-Large-Thinking is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for multimodal input, Qwen3.5-397B-A17B or Trinity-Large-Thinking?
Qwen3.5-397B-A17B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for reasoning mode, Qwen3.5-397B-A17B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Qwen3.5-397B-A17B and Trinity-Large-Thinking?
Qwen3.5-397B-A17B is available on OpenRouter. Trinity-Large-Thinking is available on Arcee AI and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.