DeepSeek V4 vs Nano Banana 2 Lite
Pricing, benchmarks, and use case comparison
Quick take
- •DeepSeek V4 is meaningfully stronger at long context (97 vs 0 on our capability index).
Specs comparison
| DeepSeek V4 | Nano Banana 2 Lite | |
|---|---|---|
| Provider | DeepSeek | |
| Type | Open source | Closed source |
| Context window | ✓1M tokens | Not announced |
| Input / 1M tokens | Free (self-host) | Not announced |
| Output / 1M tokens | Free (self-host) | 33.6 |
| Release date | 2026-04 | 2026-06 |
Benchmarks
| Benchmark | DeepSeek V4 | Nano Banana 2 Lite |
|---|---|---|
| SWE-bench Verified | 80.6% | - |
| Math / STEM / Coding (open-model comparison) | Best among open models (per DeepSeek) | - |
| Arena.ai Text-to-Image Leaderboard | - | Ranked #5 |
| Text-to-Image Elo Score | - | 1251 |
| Single-Image Editing Elo Score | - | 1308 |
| Multiple-Image Editing Elo Score | - | 1294 |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose DeepSeek V4 if...
- →You need a frontier-class open model you can self-host for data control
- →Your workload involves very long documents, codebases, or agent trajectories (up to 1M tokens)
- →You want top-tier agentic coding at a fraction of closed-model cost
- →You need to fine-tune or customize a strong base model
Choose Nano Banana 2 Lite if...
- →When cost-per-sample is the primary constraint
- →For workflows requiring 100+ images per session
- →When generation latency must be under 5 seconds
- →For rapid prototyping and exploration