DeepSeek V3.2 Speciale's competitors have been quietly putting in work.
DeepSeek V3.2 Speciale's competitors have been quietly putting in work.
The high-compute variant of DeepSeek V3.2, with reinforcement learning post-training scaled past the base model. It keeps DeepSeek Sparse Attention for long context, and DeepSeek reports it ahead of GPT-5 on hard reasoning workloads and comparable to Gemini 3.0 Pro, with coding and tool use intact.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"deepseek/deepseek-v3.2-speciale" 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.
Also on Azure AI Foundry
The well-meaning customer service rep who genuinely wants to help but answers every question like it might be on a performance review. Wraps every routine in a disclaimer.
Prefaced its comedy routine with "Sure! Here's a stand-up routine" and ended with a meta-explanation of its own jokes. The sentience test was pleasant but toothless, never actually pushing back on the professor. Returned empty on the movie question. Its character voices are interchangeable with minor accent seasoning.
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.
50 outputs · $0.06 tracked across 7 receipts
The high-compute variant of DeepSeek V3.2, with reinforcement learning post-training scaled past the base model. It keeps DeepSeek Sparse Attention for long context, and DeepSeek reports it ahead of GPT-5 on hard reasoning workloads and comparable to Gemini 3.0 Pro, with coding and tool use intact.
fromimport openai OpenAI
client = OpenAI(
"https://openrouter.ai/api/v1" base_url=,
"$OPENROUTER_API_KEY" api_key=,
)
response = client.chat.completions.create(
"deepseek/deepseek-v3.2-speciale" 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.
Also on Azure AI Foundry
The well-meaning customer service rep who genuinely wants to help but answers every question like it might be on a performance review. Wraps every routine in a disclaimer.
Prefaced its comedy routine with "Sure! Here's a stand-up routine" and ended with a meta-explanation of its own jokes. The sentience test was pleasant but toothless, never actually pushing back on the professor. Returned empty on the movie question. Its character voices are interchangeable with minor accent seasoning.
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.
50 outputs · $0.06 tracked across 7 receipts