Gemini 3.5 vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •Gemini 3.5 is meaningfully stronger at coding (90 vs 72 on our capability index).
- •Llama 4 is open-weights (free to self-host); Gemini 3.5 is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 1,048,576 tokens (Gemini 3.5 Flash; Pro variant not yet released) - better for whole-repo or long-document work.
Specs comparison
| Gemini 3.5 | Llama 4 | |
|---|---|---|
| Provider | Google DeepMind | Meta |
| Type | Closed source | Open source |
| Context window | 1,048,576 tokens (Gemini 3.5 Flash; Pro variant not yet released) | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | $1.50 | ✓Free (self-host) |
| Output / 1M tokens | $9.00 | Free (self-host) |
| Release date | 2026-05 | 2025-04 |
Benchmarks
| Benchmark | Gemini 3.5 | Llama 4 |
|---|---|---|
| Terminal-Bench 2.1 (coding) | 76.2% | - |
| MCP Atlas (tool use) | 83.6% | - |
| CharXiv Reasoning (multimodal) | 84.2% | - |
| Scout context window | - | 10M tokens |
| Scout size | - | 17B active / 109B total (16 experts) |
| Maverick size | - | 17B active / 400B total (128 experts) |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose Gemini 3.5 if...
- →You need frontier agent/coding performance without frontier prices
- →Building autonomous agents that make many tool calls
- →High-throughput production workloads that were previously too costly on a Pro model
- →You want a strong default multimodal model with a 1M-token context window
Choose Llama 4 if...
- →You need extremely long context in an open model (Scout's 10M window)
- →Self-hosted or on-prem multimodal deployment
- →You want an efficient MoE that activates few parameters per token
- →Fine-tuning or full control over the model