AI model comparison
Trinity Large Thinking vs GLM 4.6V
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Arcee AI
Trinity Large Thinking
Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks.
- Input / 1M
- $0.25
- Output / 1M
- $0.80
- Context
- 262K
Z.ai
GLM 4.6V
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media.
- Input / 1M
- $0.30
- Output / 1M
- $0.90
- Context
- 131K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Trinity Large Thinking
1.2x cheaper for a typical chat
Higher intelligence score
GLM 4.6V
11.2 vs 10.8 on Artificial Analysis
Larger context window
Trinity Large Thinking
262K vs 131K tokens
Newer release
Trinity Large Thinking
Released April 1, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Coding index
Agentic index
Pricing
Trinity Large Thinking vs GLM 4.6V API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $0.25 | $0.30 |
| Output tokensPer 1M tokens | $0.80 | $0.90 |
| Cached input (read)Per 1M tokens | $0.06 | $0.06 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Coding task
30K in, 4K out
Long document summary
150K in, 2K out
Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | ||
|---|---|---|
| Context window | 262K tokens | 131K tokens |
| Max output | 80K tokens | 33K tokens |
| Input types | Text | Image, Text, Video |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | April 1, 2026 | December 8, 2025 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Not supported |
| JSON mode | Not supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Not supported | Supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
- Trinity Large Thinking vs Qwen3 235B A22B Instruct 2507
- Trinity Large Thinking vs Qwen3 Coder 480B A35B
- Trinity Large Thinking vs Mistral Small 4
- Trinity Large Thinking vs GLM 4.5 Air
- Trinity Large Thinking vs Gemini 2.5 Flash Lite
- Trinity Large Thinking vs GPT-4.1 Mini
- Trinity Large Thinking vs Llama 4 Maverick
- Trinity Large Thinking vs Qwen3 Next 80B A3B Instruct
- Trinity Large Thinking vs Qwen3 Coder Next
- GLM 4.6V vs DeepSeek V3.1
- GLM 4.6V vs GLM 4.5
- GLM 4.6V vs Qwen3 235B A22B Instruct 2507
- GLM 4.6V vs Qwen3 Coder 480B A35B
- GLM 4.6V vs Mistral Small 4
- GLM 4.6V vs Qwen3 Next 80B A3B Thinking
- GLM 4.6V vs GLM 4.5 Air
- GLM 4.6V vs Gemini 2.5 Flash Lite
- GLM 4.6V vs GPT-4.1 Mini
- GLM 4.6V vs DeepSeek V3 0324
- GLM 4.6V vs Qwen3 Next 80B A3B Instruct
Our take
Trinity Large Thinking is 1.2x cheaper for a typical chat. GLM 4.6V scores higher on the Artificial Analysis Intelligence Index (11.2 vs 10.8). Trinity Large Thinking has the larger context window (262K vs 131K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Trinity Large Thinking or GLM 4.6V cheaper?
Trinity Large Thinking is cheaper for a typical chat (2K input tokens and 500 output tokens). Trinity Large Thinking costs $0.25 per 1M input tokens and $0.80 per 1M output tokens, while GLM 4.6V costs $0.30 per 1M input tokens and $0.90 per 1M output tokens.
Which has a bigger context window, Trinity Large Thinking or GLM 4.6V?
Trinity Large Thinking supports up to 262K tokens of context, compared with 131K for GLM 4.6V.
Is Trinity Large Thinking smarter than GLM 4.6V?
On the Artificial Analysis Intelligence Index, Trinity Large Thinking scores 10.8 and GLM 4.6V scores 11.2, putting GLM 4.6V ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Trinity Large Thinking and GLM 4.6V at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Trinity Large Thinking and GLM 4.6V answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Trinity Large Thinking and GLM 4.6V at the same time. Compare the answers side by side and keep the best one.