GLM-5.1 vs Trinity-Large-Thinking
GLM-5.1 (2026) and Trinity-Large-Thinking (2026) are frontier-tier reasoning models from Zhipu AI and Arcee AI. GLM-5.1 ships a 200k-token context window, while Trinity-Large-Thinking ships a 256K-token context window. On Google-Proof Q&A, Trinity-Large-Thinking leads by 2.4 pts. On pricing, Trinity-Large-Thinking costs $0.22/1M input tokens versus $0.95/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Trinity-Large-Thinking is ~332% cheaper at $0.22/1M; pay for GLM-5.1 only for coding workflow support.
Specs
| Released | 2026-03-27 | 2026-04-01 |
| Context window | 200k | 256K |
| Parameters | 744B total, 40-44B active | 400B |
| Architecture | mixture of experts | Sparse Mixture of Experts (MoE) |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| GLM-5.1 | Trinity-Large-Thinking | |
|---|---|---|
| Input price | $0.95/1M tokens | $0.22/1M tokens |
| Output price | $3.15/1M tokens | $0.85/1M tokens |
| Providers |
Capabilities
| GLM-5.1 | Trinity-Large-Thinking | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GLM-5.1 | Trinity-Large-Thinking |
|---|---|---|
| Google-Proof Q&A | 86.8 | 89.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has GLM-5.1 at 86.8 and Trinity-Large-Thinking at 89.2, with Trinity-Large-Thinking ahead by 2.4 points. The largest visible gap is 2.4 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 code execution: GLM-5.1. Both models share reasoning mode, function calling, tool use, and 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, GLM-5.1 lists $0.95/1M input and $3.15/1M 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 $1.2 per million blended tokens. Availability is 2 providers versus 2, so concentration risk also matters.
Choose GLM-5.1 when coding workflow support are central to the workload. Choose Trinity-Large-Thinking when long-context analysis, larger context windows, and lower input-token cost 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, GLM-5.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking supports 256K tokens, while GLM-5.1 supports 200k 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, GLM-5.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking is cheaper on tracked token pricing. GLM-5.1 costs $0.95/1M input and $3.15/1M 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 GLM-5.1 or Trinity-Large-Thinking open source?
GLM-5.1 is listed under Proprietary. 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, GLM-5.1 or Trinity-Large-Thinking?
Both GLM-5.1 and Trinity-Large-Thinking expose reasoning mode. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, GLM-5.1 or Trinity-Large-Thinking?
Both GLM-5.1 and Trinity-Large-Thinking expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GLM-5.1 and Trinity-Large-Thinking?
GLM-5.1 is available on Z.ai and 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.