Nano Banana 2.1 vs GPT-3.5 Turbo (older v0613)
GPT-3.5 Turbo (older v0613) costs 3.8x less per output token than Nano Banana 2.1 ($2.00 vs $7.50 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $7.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
Nano Banana 2.1 or GPT-3.5 Turbo (older v0613): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nano Banana 2.1 for a larger context window (66K against 4K tokens).
No benchmark has tested both yet. Nano Banana 2.1 has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Image generation
Design Arena: images
No call
Only Nano Banana 2.1 has a Design Arena: images result, 1290.
Directional only
Long documents
Listed context window
Nano Banana 2.1
Nano Banana 2.1 takes 66K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo (older v0613)
Nano Banana 2.1 $0.0053, GPT-3.5 Turbo (older v0613) $0.0020 per turn.
List prices
At a glance
- Coding index
- Nano Banana 2.1 not benchmarked, GPT-3.5 Turbo (older v0613) not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Nano Banana 2.1 $7.50 against GPT-3.5 Turbo (older v0613) $2.00 per million output tokens, 3.8x apart.
- Context window
- Nano Banana 2.1 takes 66K tokens, GPT-3.5 Turbo (older v0613) 4K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nano Banana 2.1 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| GSM8K | Not measured | 46.9% | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nano Banana 2.1 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| MMLU | Not measured | 68.9% | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nano Banana 2.1 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| WinoGrande | Not measured | 81.6% | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 47 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Nano Banana 2.1 | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| Design Arena: images | 1290 | Not measured | Only Nano Banana 2.1 measured |
| Design Arena: graphic design | 1333 | Not measured | Only Nano Banana 2.1 measured |
| Design Arena: logos | 1277 | Not measured | Only Nano Banana 2.1 measured |
| LMArena Text to Image | 1328 | Not measured | Only Nano Banana 2.1 measured |
| LMArena Image Edit | 1426 | Not measured | Only Nano Banana 2.1 measured |
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-3.5 Turbo (older v0613) costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo (older v0613) costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo (older v0613) costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Google Nano Banana 2.1 | OpenAI GPT-3.5 Turbo (older v0613) |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | October 6, 2026 | January 25, 2024 |
| Context window | 66K | 4K |
| Input $/Mtok | $1.50 | $1.00 |
| Output $/Mtok | $7.50 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nano Banana 2.1 better than GPT-3.5 Turbo (older v0613) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nano Banana 2.1 for a larger context window (66K against 4K tokens).
Which is cheaper, Nano Banana 2.1 or GPT-3.5 Turbo (older v0613)?
Nano Banana 2.1 costs $1.50 per million input tokens and $7.50 per million output tokens; GPT-3.5 Turbo (older v0613) costs $1.00 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nano Banana 2.1 or GPT-3.5 Turbo (older v0613)?
Nano Banana 2.1, with 66K tokens against 4K.
Has any benchmark tested both Nano Banana 2.1 and GPT-3.5 Turbo (older v0613)?
No benchmark has tested both yet. Nano Banana 2.1 has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. Compare them on price and context instead. Data as of October 23, 2026.