Comment by d4rkp4ttern

For token-generation speed, a challenging test is to see how it performs in a code-agent harness like Claude Code, which has anywhere between 15-40K tokens from the system prompt itself (+ tools/skills etc).

Here the 26B-A4B variant is head and shoulders above recent open-weight models, at least on my trusty M1 Max 64GB MacBook.

I set up Claude Code to use this variant via llama-server, with 37K tokens initial context, and it performs very well: ~40 tokens/sec, far better than Qwen3.5-35B-A3B, though I don't know yet about the intelligence or tool-calling consistency. Prompt processing speed is comparable to the Qwen variant at ~400 tok/s.

My informal tests, all with roughly 30K-37K tokens initial context:

    ┌────────────────────┬───────────────┬────────────┐
    │       Model        │ Active Params │ tg (tok/s) │
    ├────────────────────┼───────────────┼────────────┤
    │ Gemma-4-26B-A4B    │ 4B            │ ~40        │
    ├────────────────────┼───────────────┼────────────┤
    │ GPT-OSS-20B        │ 3.6B          │ ~17-38     │
    ├────────────────────┼───────────────┼────────────┤
    │ Qwen3-30B-A3B      │ 3B            │ ~15-27     │
    ├────────────────────┼───────────────┼────────────┤
    │ GLM-4.7-Flash      │ 3B            │ ~12-13     │
    ├────────────────────┼───────────────┼────────────┤
    │ Qwen3.5-35B-A3B    │ 3B            │ ~12        │
    ├────────────────────┼───────────────┼────────────┤
    │ Qwen3-Next-80B-A3B │ 3B            │ ~3-5       │
    └────────────────────┴───────────────┴────────────┘

Full instructions for running this and other open-weight models with Claude Code are here:

https://pchalasani.github.io/claude-code-tools/integrations/...