Ling 2.6 1T by Inclusionai — Pricing, Benchmarks & Real Outputs
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Ling 2.6 1T's competitors have been quietly putting in work.
Ling 2.6 1T's competitors have been quietly putting in work.
Ling 2.6 1T's competitors have been quietly putting in work.
Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company's trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a 'fast thinking' approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance. The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"inclusionai/ling-2.6-1t:free" model=,
"role""user""content""Hello!" messages=[{: , : }],
)
print(response.choices[0].message.content)Set OPENROUTER_API_KEY with your OpenRouter API key from openrouter.ai/keys.
Taste is judged on an uncapped scale, originality first. The space past 100 is craft today's models rarely reach.
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Ling 2.6 1T
Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company's trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a 'fast thinking' approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance. The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"inclusionai/ling-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.
53 outputs from Ling 2.6 1T