Nano Banana 2.1 vs GPT-4
Nano Banana 2.1 costs 8.0x less per output token than GPT-4 ($7.50 vs $60.00 per million). Updated October 22, 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
- $60.00
Nano Banana 2.1 or GPT-4: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nano Banana 2.1 for output tokens 8.0x cheaper ($7.50 vs $60.00 per million) and a larger context window (66K against 8K 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-4 on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set). 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
Intelligence
AA Intelligence Index
No call
Only GPT-4 has a AA Intelligence Index result, 6.7.
Directional only
Image generation
Design Arena: images
No call
Only Nano Banana 2.1 has a Design Arena: images result, 1291.
Directional only
Long documents
Listed context window
Nano Banana 2.1
Nano Banana 2.1 takes 66K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nano Banana 2.1
Nano Banana 2.1 $0.0053, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- Nano Banana 2.1 not benchmarked, GPT-4 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-4 $60.00 per million output tokens, 8.0x apart.
- Context window
- Nano Banana 2.1 takes 66K tokens, GPT-4 8K.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 22.4% | Only GPT-4 measured |
| SWE-bench Lite | Not measured | 39.7% | Only GPT-4 measured |
| SWE-bench (full test set) | Not measured | 12.5% | Only GPT-4 measured |
| AA Coding Index | Not measured | 13.1 | Only GPT-4 measured |
| LMArena Text: coding | Not measured | 1209 | Only GPT-4 measured |
| LMArena Text: software and IT | Not measured | 1195 | Only GPT-4 measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only GPT-4 measured |
| Epoch Capabilities Index | Not measured | 125.7 | Only GPT-4 measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 34.9% | Only GPT-4 measured |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | Not measured | 1200 | Only GPT-4 measured |
| LMArena Text: expert | Not measured | 1149 | Only GPT-4 measured |
| LMArena Text: hard prompts in English | Not measured | 1209 | Only GPT-4 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | Not measured | 1230 | Only GPT-4 measured |
| LMArena Text: mathematical work | Not measured | 1228 | Only GPT-4 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 56.2% | Only GPT-4 measured |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | Not measured | 1177 | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1145 | Only GPT-4 measured |
| LMArena Text: legal and government | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: business and finance | Not measured | 1145 | Only GPT-4 measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 33.2% | Only GPT-4 measured |
| DTBench | Not measured | 62.7% | Only GPT-4 measured |
| LMCA | Not measured | 17.1% | Only GPT-4 measured |
| HellaSwag | Not measured | 95.3% | Only GPT-4 measured |
| WinoGrande | Not measured | 87.5% | Only GPT-4 measured |
| LMArena Text | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: instruction following | Not measured | 1201 | Only GPT-4 measured |
| LMArena Text: creative writing | Not measured | 1191 | Only GPT-4 measured |
| LMArena Text: English | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: non-English | Not measured | 1194 | Only GPT-4 measured |
| LMArena Text: Chinese | Not measured | 1184 | Only GPT-4 measured |
| LMArena Text: German | Not measured | 1198 | Only GPT-4 measured |
| LMArena Text: Japanese | Not measured | 1137 | Only GPT-4 measured |
| LMArena Text: Korean | Not measured | 1087 | Only GPT-4 measured |
| LMArena Text: French | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: Spanish | Not measured | 1197 | Only GPT-4 measured |
| LMArena Text: Russian | Not measured | 1195 | Only GPT-4 measured |
| LMArena Text: Polish | Not measured | 1265 | Only GPT-4 measured |
| LMArena Text: multi-turn | Not measured | 1206 | Only GPT-4 measured |
| LMArena Text: longer queries | Not measured | 1190 | Only GPT-4 measured |
| LMArena Text: writing and language | Not measured | 1219 | Only GPT-4 measured |
| LMArena Text: entertainment and media | Not measured | 1184 | Only GPT-4 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Nano Banana 2.1 | GPT-4 | Leader |
|---|---|---|---|
| Design Arena: images | 1291 | 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
Nano Banana 2.1 costs less
Repository review
50K input + 3K output tokens
Nano Banana 2.1 costs less
Agent loop
200K input + 10K output tokens
Nano Banana 2.1 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-4 |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | October 6, 2026 | May 28, 2023 |
| Context window | 66K | 8K |
| Input $/Mtok | $1.50 | $30.00 |
| Output $/Mtok | $7.50 | $60.00 |
| 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-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nano Banana 2.1 for output tokens 8.0x cheaper ($7.50 vs $60.00 per million) and a larger context window (66K against 8K tokens).
Which is cheaper, Nano Banana 2.1 or GPT-4?
Nano Banana 2.1 costs $1.50 per million input tokens and $7.50 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nano Banana 2.1 or GPT-4?
Nano Banana 2.1, with 66K tokens against 8K.
Has any benchmark tested both Nano Banana 2.1 and GPT-4?
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-4 on SWE-bench Verified, SWE-bench Lite and SWE-bench (full test set). Compare them on price and context instead. Data as of October 22, 2026.