Flint vs Llama 4
Pricing, benchmarks, and use case comparison
Quick take
- •Llama 4 is meaningfully stronger at long context (95 vs 0).
Specs comparison
| Flint | Llama 4 | |
|---|---|---|
| Provider | Springboards | Meta |
| Type | Closed source | Open source |
| Context window | Not announced | ✓Up to 10M tokens (Scout); ~1M tokens (Maverick) |
| Input / 1M tokens | Not announced | Free (self-host) |
| Output / 1M tokens | Not announced | Free (self-host) |
| Release date | 2026-04 | 2025-04 |
Benchmarks
| Benchmark | Flint | Llama 4 |
|---|---|---|
| Novelty Bench | 7/10 | - |
| Intra-Model Similarity | 0.721 | - |
| NoveltyBench | 7.47 | - |
| 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 Flint if...
- →When exploring diverse creative directions
- →For early-stage ideation and concept generation
- →When variety and novelty are more important than accuracy
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