Head-to-head · 2026
gpt-oss (120b / 20b) vs Gemma 4
Verdict. Gemma 4 wins for multimodal work on a single consumer GPU; gpt-oss wins on reasoning-per-gigabyte and scales to 120b on one 80GB card. Both are Apache 2.0.
| gpt-oss (120b / 20b) | Gemma 4 | |
|---|---|---|
| Provider | OpenAI | Google DeepMind |
| Released | 2025-08-05 | 2026-04-02 |
| Context | 131K | 256K |
| Max output | — | — |
| Input /1M | $0.04 | Open |
| Output /1M | $0.18 | Open |
| Modalities | text, code | text, vision, audio |
The analysis
These are the two best "runs on my hardware" open models. Gemma 4’s 31B dense flagship fits in roughly 18GB at Q4 — one consumer GPU — adds native vision (and audio on the smaller tiers), and posts a 1452 LMArena Elo. gpt-oss ships 20b (~16GB) and 120b (one 80GB GPU) MoE variants tuned for the best reasoning-per-VRAM with adjustable reasoning effort.
Pick by constraint. If you want multimodality and the strongest single-consumer-GPU quality, Gemma 4 is the default. If you want maximum reasoning that still fits on one card — or the headroom to scale to 120b on an 80GB GPU — gpt-oss is the sharper tool. Both carry clean Apache 2.0 licenses, so neither adds legal friction.
For an edge deployment, also weigh Gemma 4’s E2B/E4B tiers and Phi-4 — they go smaller than either flagship here.
Pick gpt-oss (120b / 20b) if…
- Reasoning-per-VRAM on one GPU; scales to 120b on 80GB
- Adjustable reasoning effort
- Pure text/reasoning self-host
Pick Gemma 4 if…
- Native multimodal (vision/audio) on one consumer GPU
- Strongest single-card general quality (LMArena 1452)
- Google-ecosystem tooling + small E2B/E4B tiers
gpt-oss (120b / 20b)
OpenAI’s open-weight family — Apache 2.0 reasoning that fits on one GPU or a laptop.
Gemma 4
Google’s open-weight flagship: a 31B frontier-tier model on one GPU, now Apache 2.0.