Ring 2.6 1T by Inclusionai — Pricing, Benchmarks & Real Outputs
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Compare Ring 2.6 1T
Alternatives to Ring 2.6 1T
Ring 2.6 1T's competitors have been quietly putting in work.
Ring 2.6 1T's competitors have been quietly putting in work.
Ring 2.6 1T's competitors have been quietly putting in work.
Ring-2.6-1T is a 1T-parameter thinking model with 63B active parameters from inclusionAI, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search. With adaptive reasoning effort across high and xhigh modes, the model dynamically allocates reasoning budget based on task complexity, enabling stronger performance with lower token overhead in tool-heavy and multi-turn agent workflows.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"inclusionai/ring-2.6-1t:free" model=,
"role""user""content""Hello!" messages=[{: , : }],
)
print(response.choices[0].message.content)Set OPENROUTER_API_KEY with your OpenRouter API key from openrouter.ai/keys.
Taste is judged on an uncapped scale, originality first. The space past 100 is craft today's models rarely reach.
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
38 outputs from Ring 2.6 1T
Ring-2.6-1T is a 1T-parameter thinking model with 63B active parameters from inclusionAI, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search. With adaptive reasoning effort across high and xhigh modes, the model dynamically allocates reasoning budget based on task complexity, enabling stronger performance with lower token overhead in tool-heavy and multi-turn agent workflows.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"inclusionai/ring-2.6-1t:free" model=,
"role""user""content""Hello!" messages=[{: , : }],
)
print(response.choices[0].message.content)Set OPENROUTER_API_KEY with your OpenRouter API key from openrouter.ai/keys.
Taste is judged on an uncapped scale, originality first. The space past 100 is craft today's models rarely reach.
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
38 outputs from Ring 2.6 1T