Your chat model guesses. A reasoning model works the problem.

Ask a chat model to prove a theorem and it might confidently say something plausible. Ask a reasoning model and it actually works through it — step by step, before it answers.

curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxxxxxxx" \
  -d '{
    "model": "deepseek-reasoner",
    "messages": [{"role": "user", "content": "Prove that sqrt(2) is irrational."}]
  }'

deepseek-reasoner spends its thinking budget before responding — exploring the problem, checking its work, then committing to an answer. For hard questions, that changes everything.

---

The two kinds of questions

Most of what we ask models falls into two buckets:

• Recall & reformat — "summarize this", "rewrite that", "extract the date". One-shot, no deep thought required.
• Reason & derive — "prove this", "find the bug", "which option is optimal". The answer depends on a chain of logic, not just memory.

A chat model handles bucket one beautifully. It stumbles on bucket two — not because it's bad, but because it answers in a single pass, and for multi-step reasoning, a single pass means guessing at the hard steps.

A reasoning model doesn't answer in one pass. It thinks first, then answers. The difference is the difference between "sounds right" and "is right."

When to reach for reasoning

Use deepseek-reasoner (or kimi-k3, which reasons by default) when the answer has to be derived, not retrieved:

Task	Chat model	Reasoning model
Summarize a doc	✅	Overkill
Rewrite copy	✅	Overkill
Math / proofs	Guesses	Derives
Multi-step logic	Fragile	Reliable
"Find the bug in this code"	May miss it	Traces it
Optimization / planning	Shallow	Explores alternatives

The rule of thumb: if you could answer it without scratch paper, a chat model is fine. If you'd need scratch paper, use a reasoning model.

Reasoning as one tier in a full stack

The point isn't "always use reasoning" — it's that you can now choose. AIBridge puts both modes behind one endpoint, so you route per question:

def model_for(prompt: str) -> str:
    if any(k in prompt.lower() for k in ("prove", "derive", "why", "optimize", "debug")):
        return "deepseek-reasoner"      # needs a chain of thought
    return "deepseek-chat"              # recall & reformat

Cheap chat model for the easy 90% of traffic, reasoning model for the hard 10% that actually needs it. You're not paying reasoning-token prices for a "summarize this email."

The full model menu

• DeepSeek — deepseek-reasoner, deepseek-v4-pro, deepseek-v4-flash, deepseek-coder, deepseek-chat
• Qwen — qwen3-235b-a22b, qwen-plus (131K), qwen-max
• GLM — glm-4-plus, glm-4-air, glm-4-flash
• Moonshot — kimi-k3 (1M context, always-on reasoning), moonshot-v1-128k / -32k / -8k

Pricing that doesn't punish thinking

• Free tier: 500K tokens/month (weighted)
• Pro: $9.90/month for 5M tokens
• Top-ups: 1M / $2.99 · 5M / $9.90 · 20M / $29.90 (never expire)

The takeaway

The right model depends on the question, not the product. Give recall to the chat model, give reasoning to the reasoner — and let both live on one endpoint.

Try deepseek-reasoner on a problem your chat model keeps flubbing.

→ aibridge-api.com · [e-mail]