AI model comparison
Trinity Large Thinking vs Qwen3 235B A22B Instruct 2507
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
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass.
- Input / 1M
- $0.09
- Output / 1M
- $0.55
- Context
- 262K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Qwen3 235B A22B Instruct 2507
2.0x cheaper for a typical chat
Higher intelligence score
Qwen3 235B A22B Instruct 2507
12 vs 10.8 on Artificial Analysis
Larger context window
Tie
262K vs 262K tokens
Newer release
Trinity Large Thinking
Released April 1, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3 235B A22B Instruct 250712Coding index
Qwen3 235B A22B Instruct 2507Not availableAgentic index
Qwen3 235B A22B Instruct 2507Not availablePricing
Trinity Large Thinking vs Qwen3 235B A22B Instruct 2507 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3 235B A22B Instruct 2507 | |
|---|---|---|
| Input tokensPer 1M tokens | $0.25 | $0.09 |
| Output tokensPer 1M tokens | $0.80 | $0.55 |
| Cached input (read)Per 1M tokens | $0.06 | Not available |
| 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
Qwen3 235B A22B Instruct 2507$0.46Coding task
30K in, 4K out
Qwen3 235B A22B Instruct 2507$4.90Long document summary
150K in, 2K out
Qwen3 235B A22B Instruct 2507$14.60Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3 235B A22B Instruct 2507 | |
|---|---|---|
| Context window | 262K tokens | 262K tokens |
| Max output | 80K tokens | 16K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | April 1, 2026 | July 21, 2025 |
Features
Supported features
API features available for each model.
| Metric | Qwen3 235B A22B Instruct 2507 | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Supported |
| JSON mode | Not supported | Supported |
| Reasoning | Supported | Not 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.
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Qwen3 235B A22B Instruct 2507View full model details- Qwen3 235B A22B Instruct 2507 vs Gemini 2.5 Flash Lite
- Qwen3 235B A22B Instruct 2507 vs gpt-oss-120b
- Qwen3 235B A22B Instruct 2507 vs Qwen3.8 Flash
- Qwen3 235B A22B Instruct 2507 vs GPT-5 Nano
- Qwen3 235B A22B Instruct 2507 vs DeepSeek V3.1
- Qwen3 235B A22B Instruct 2507 vs Mercury 2.5
- Qwen3 235B A22B Instruct 2507 vs Qwen3.5-9B
- Qwen3 235B A22B Instruct 2507 vs Qwen3 Coder Next
- Qwen3 235B A22B Instruct 2507 vs Qwen3 Next 80B A3B Instruct
- Qwen3 235B A22B Instruct 2507 vs Granite 4.2 8B
- Qwen3 235B A22B Instruct 2507 vs Llama 4 Maverick
- Qwen3 235B A22B Instruct 2507 vs Qwen3 Coder 480B A35B
- Qwen3 235B A22B Instruct 2507 vs GLM 4.7 Flash
- Qwen3 235B A22B Instruct 2507 vs Nemotron 3 Super
- Qwen3 235B A22B Instruct 2507 vs GLM 4.5 Air
- Qwen3 235B A22B Instruct 2507 vs Mistral Small 4
- Qwen3 235B A22B Instruct 2507 vs Mercury 2
- Qwen3 235B A22B Instruct 2507 vs Qwen3 VL 30B A3B Instruct
- Qwen3 235B A22B Instruct 2507 vs Qwen3 Coder 30B A3B Instruct
- Qwen3 235B A22B Instruct 2507 vs Qwen3 235B A22B Thinking 2507
- Qwen3 235B A22B Instruct 2507 vs GLM 4.6V
- Qwen3 235B A22B Instruct 2507 vs Qwen3 Next 80B A3B Thinking
- Qwen3 235B A22B Instruct 2507 vs Qwen3 VL 30B A3B Thinking
- Qwen3 235B A22B Instruct 2507 vs Qwen3 30B A3B
Our take
Qwen3 235B A22B Instruct 2507 is 2.0x cheaper for a typical chat. Qwen3 235B A22B Instruct 2507 scores higher on the Artificial Analysis Intelligence Index (12 vs 10.8). 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 Qwen3 235B A22B Instruct 2507 cheaper?
Qwen3 235B A22B Instruct 2507 is cheaper for a typical chat (2K input tokens and 500 output tokens). Qwen3 235B A22B Instruct 2507 costs $0.09 per 1M input tokens and $0.55 per 1M output tokens, while Trinity Large Thinking costs $0.25 per 1M input tokens and $0.80 per 1M output tokens.
Which has a bigger context window, Trinity Large Thinking or Qwen3 235B A22B Instruct 2507?
Trinity Large Thinking supports up to 262K tokens of context, compared with 262K for Qwen3 235B A22B Instruct 2507.
Is Trinity Large Thinking smarter than Qwen3 235B A22B Instruct 2507?
On the Artificial Analysis Intelligence Index, Trinity Large Thinking scores 10.8 and Qwen3 235B A22B Instruct 2507 scores 12, putting Qwen3 235B A22B Instruct 2507 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Trinity Large Thinking and Qwen3 235B A22B Instruct 2507 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Trinity Large Thinking and Qwen3 235B A22B Instruct 2507 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Trinity Large Thinking and Qwen3 235B A22B Instruct 2507 at the same time. Compare the answers side by side and keep the best one.