Gemini 2.5 Flash vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •Gemini 2.5 Flash is meaningfully stronger at speed (90 vs 72 on our capability index).
- •Llama 4 is open-weights (free to self-host); Gemini 2.5 Flash is paid API only.
- •Llama 4 has a Up to 10M tokens (Scout); ~1M tokens (Maverick) context window vs 1,048,576 tokens (1M) input; up to 65,535 output - better for whole-repo or long-document work.
Specs comparison
| Gemini 2.5 Flash | Llama 4 | |
|---|---|---|
| Provider | Google DeepMind | Meta |
| Type | Closed source | Open source |
| Context window | 1,048,576 tokens (1M) input; up to 65,535 output | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | $0.30 | ✓Free (self-host) |
| Output / 1M tokens | $2.50 | Free (self-host) |
| Release date | 2025-06 | 2025-04 |
Benchmarks
| Benchmark | Gemini 2.5 Flash | Llama 4 |
|---|---|---|
| Context window | 1M tokens | - |
| Input price | $0.30/1M | - |
| 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 2.5 Flash if...
- →High-volume, latency-sensitive production workloads
- →Chatbots, extraction, classification, and summarization at scale
- →You need decent reasoning but must control costs
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