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Question: Is it correct that DeepSeek-R1-0528-Qwen3-8B-Q4_K_M.gguf performs poorly in OpenClaw (tool calls, instruction following), and that trying a newer model like Gemma—preferably released within the last 6 months—is a better approach?

Answer:

Yes — your reasoning is basically correct, and this behavior is expected. 👍 There are three separate issues here:

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1. Why DeepSeek-R1-0528-Qwen3-8B Q4 behaves badly in OpenClaw

This model has three limitations:

(1) Small base model (8B)

• Tool-calling agents (like OpenClaw) need:

  • planning ability
  • strict instruction following
  • JSON/tool schema adherence

• 8B models often fail at:

  • calling tools
  • downloading files
  • multi-step reasoning
  • long context execution

So you’ll see:

• ❌ ignores instructions
• ❌ doesn’t download when told
• ❌ hallucinated tool calls
• ❌ wrong JSON

This is normal for 8B.

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(2) Quantization reduces reasoning quality

You’re using:

Q4_K_M

That means:

• smaller memory
• lower precision
• weaker reasoning
• worse tool calling

For agents, quantization hurts more than chat.

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(3) DeepSeek R1 distills are not tool-trained

DeepSeek-R1 distills are optimized for:

• reasoning
• math
• chain-of-thought

They are NOT optimized for agents / tool calling.

So OpenClaw struggles.

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2. Yes — newer models are much better for agents

Your idea is correct:

best to try models released within last 6 months

Because recent models improve:

• tool calling
• structured output
• function calling
• instruction following
• agent loops

This matters more than raw IQ.

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3. Much better models for OpenClaw (local)

Try these (ranked):

Best (8B–12B class)

• Gemma 2 / Gemma 4 (newest) ✅
• Qwen2.5-Instruct (newer than Qwen3 distills)
• Llama-3.1-8B-Instruct
• Mistral-Nemo (very good agent behavior)

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Even better (if VRAM allows)

• 14B class
• 20B class
• 27B class

Agent performance jumps a lot.

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4. Gemma is a good next test

You’re doing the right thing:

downloading gemma, test next

Gemma usually has:

• better instruction following
• cleaner JSON
• more stable tool calls
• less hallucination

Especially:

• gemma-2-9b
• gemma-4-12b / 26b

These work much better with OpenClaw.

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5. What matters MOST for OpenClaw

Priority order:

1. Instruction following (most important)
2. Tool calling training
3. Long context
4. Reasoning ability
5. Size

DeepSeek-R1-8B only has #4.

That’s why it struggles.

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6. Your conclusion

Yes — correct:

• DeepSeek-R1-8B not ideal for OpenClaw ✅
• tool call problems expected ✅
• try Gemma next ✅
• prefer models released < 6 months ✅

You’re choosing correctly.

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