Gemini 3 Pro vs Llama 3.3 70B Instruct
Direct comparison between google and meta for chat models. Specs, prices, strengths, weaknesses — decide on facts.
At a glance
- · Cheaper: Llama 3.3 70B Instruct
- · Longer context window: Gemini 3 Pro at 2,000,000 tokens
- · Gemini 3 Pro: 2M token context — largest window among top-tier models
- · Llama 3.3 70B Instruct: Open weights available — no vendor lock-in
Spec comparison
| Spec | Gemini 3 Pro | Llama 3.3 70B Instruct |
|---|---|---|
| Provider | meta | |
| Input per 1M tokens | $1.25 / 1M | $0.72 / 1M |
| Output per 1M tokens | $5.00 / 1M | $0.72 / 1M |
| Context window | 2,000,000 tok | 128,000 tok |
| Max output | 65,536 tok | 4,096 tok |
| Knowledge cutoff | 2025-11 | 2023-12 |
| Vision | ||
| Audio | ||
| Tool calling | ||
| JSON mode | ||
| Structured output | ||
| Reasoning |
Gemini 3 Pro — strengths
- +2M token context — largest window among top-tier models
- +True multimodal input: video, audio, image, text
- +Very strong on structured output and JSON-schema enforcement
Llama 3.3 70B Instruct — strengths
- +Open weights available — no vendor lock-in
- +Very cheap per token, widely available across providers
- +Solid all-rounder for classification and simple generation
Gemini 3 Pro — weaknesses
- −Code generation slightly behind Claude Sonnet 4.6 and GPT-5.4
- −At very long contexts, latency becomes significant
Llama 3.3 70B Instruct — weaknesses
- −Knowledge cutoff December 2023 — outdated on recent facts
- −No native vision or audio input
- −Behind Claude Sonnet and GPT-5 across nearly all benchmarks