Qwen3.5-235B-A22B vs Trinity-Large-Thinking
Qwen3.5-235B-A22B (2026) and Trinity-Large-Thinking (2026) are frontier reasoning models from Alibaba and Arcee AI. Qwen3.5-235B-A22B ships a 512k-token context window, while Trinity-Large-Thinking ships a 256K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Trinity-Large-Thinking is safer overall; choose Qwen3.5-235B-A22B when long-context analysis matters.
Specs
| Released | 2026-02-24 | 2026-04-01 |
| Context window | 512k | 256K |
| Parameters | 235B | 400B |
| Architecture | MoE | Sparse Mixture of Experts (MoE) |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Qwen3.5-235B-A22B | Trinity-Large-Thinking | |
|---|---|---|
| Input price | - | $0.22/1M tokens |
| Output price | - | $0.85/1M tokens |
| Providers | - |
Capabilities
| Qwen3.5-235B-A22B | Trinity-Large-Thinking | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: Trinity-Large-Thinking, function calling: Trinity-Large-Thinking, tool use: Trinity-Large-Thinking, and structured outputs: Trinity-Large-Thinking. Both models share the core language-model surface, 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.
Pricing coverage is uneven: Qwen3.5-235B-A22B has no token price sourced yet and Trinity-Large-Thinking has $0.22/1M input tokens. Provider availability is 0 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Qwen3.5-235B-A22B when long-context analysis and larger context windows are central to the workload. Choose Trinity-Large-Thinking when reasoning depth 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Qwen3.5-235B-A22B or Trinity-Large-Thinking?
Qwen3.5-235B-A22B supports 512k 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.
Is Qwen3.5-235B-A22B or Trinity-Large-Thinking open source?
Qwen3.5-235B-A22B 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 reasoning mode, Qwen3.5-235B-A22B 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.
Which is better for function calling, Qwen3.5-235B-A22B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Qwen3.5-235B-A22B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Qwen3.5-235B-A22B and Trinity-Large-Thinking?
Qwen3.5-235B-A22B is available on the tracked providers still being sourced. Trinity-Large-Thinking is available on Arcee AI and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.