API cost decision in 10 seconds

Trinity Large Thinking (free) vs Qwen3.5-122B-A10B

Pick Trinity Large Thinking (free) when budget is the priority.

Page updated: 2026-05-26T10:03:26Z Data confirmed: 2026-05-26T12:43:06Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Trinity Large Thinking (free) when budget is the priority.

On the standard 1M input plus 500K output workload, Trinity Large Thinking (free) is estimated at $0 vs $1.3 for Qwen3.5-122B-A10B, saving $1.3 (100% lower).

Cost-first pickTrinity Large Thinking (free)

Context-first pickBoth models

Sample savings$1.3100%

10x traffic gap$13

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $13. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Trinity Large Thinking (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Trinity Large Thinking (free)	Qwen3.5-122B-A10B
Input-heavy / RAG	5M input + 500K output	Trinity Large Thinking (free)	$0	$2.34
Balanced workload	1M input + 1M output	Trinity Large Thinking (free)	$0	$2.34
Output-heavy chatbot	1M input + 5M output	Trinity Large Thinking (free)	$0	$10.66

Cheaper input Trinity Large Thinking (free) $0 vs $0.26 / 1M

Trinity Large Thinking (free) is free for input tokens while Qwen3.5-122B-A10B costs $0.26 per 1M tokens.

Cheaper output Trinity Large Thinking (free) $0 vs $2.08 / 1M

Trinity Large Thinking (free) is free for output tokens while Qwen3.5-122B-A10B costs $2.08 per 1M tokens.

Larger context Tie 262.14K vs 262.14K

Both models report the same context window at 262.14K tokens.

Sample workload Trinity Large Thinking (free) $0 vs $1.3

Trinity Large Thinking (free) is free for the standard workload while the other model is estimated at $1.3.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Trinity Large Thinking (free) Calculating… Estimated API cost

Qwen3.5-122B-A10B Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Trinity Large Thinking (free) has the lower input price; Trinity Large Thinking (free) has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Trinity Large Thinking (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Trinity Large Thinking (free) and $1.3 for Qwen3.5-122B-A10B.

Best Fit

Choose Trinity Large Thinking (free) when you care most about lower input-token price, and lower output-token price.

Choose Qwen3.5-122B-A10B when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Decision Notes

On the standard 1M input plus 500K output workload, Trinity Large Thinking (free) is estimated at $0 vs $1.3 for Qwen3.5-122B-A10B, saving $1.3 (100% lower).
Trinity Large Thinking (free) is free for the standard workload while the other model is estimated at $1.3.
Trinity Large Thinking (free) is free for input tokens while Qwen3.5-122B-A10B costs $0.26 per 1M tokens.
Trinity Large Thinking (free) is free for output tokens while Qwen3.5-122B-A10B costs $2.08 per 1M tokens.
Both models report the same context window at 262.14K tokens.

Head-to-Head Specs

Feature	Trinity Large Thinking (free) (Arcee AI)	Qwen3.5-122B-A10B (Qwen)
Input Price prompt tokens per 1M	$0	$0.26
Completion Price per 1M tokens	$0	$2.08
Sample Workload Cost 1M input + 500K output	$0	$1.3
Context Window	262.14K	262.14K
Release Date	2026-04-01	2026-02-25

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Trinity Large Thinking (free)	On the standard 1M input plus 500K output workload, Trinity Large Thinking (free) is estimated at $0 vs $1.3 for Qwen3.5-122B-A10B, saving $1.3 (100% lower).
High-volume input processing	Trinity Large Thinking (free)	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Trinity Large Thinking (free)	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Tie	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Qwen3 Next 80B A3B Instruct (free) can replace Qwen3.5-122B-A10B when lower sample workload cost matters most: $0.
Qwen3 Coder 480B A35B (free) can replace Qwen3.5-122B-A10B when lower sample workload cost matters most: $0.
Qwen2.5 7B Instruct can replace Qwen3.5-122B-A10B when lower sample workload cost matters most: $0.09.
Qwen3.5-9B can replace Qwen3.5-122B-A10B when lower sample workload cost matters most: $0.11.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
DeepSeek V4 Pro offers 1.05M context with $0.87 sample workload cost.

Popular competitors

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Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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Arcee AI catalog

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Qwen catalog

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Trinity Large Thinking (free)

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. Launch video: https://youtu.be/Gc82AXLa0Rg?si=4RLn6WBz33qT--B7...

Qwen3.5-122B-A10B

The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...