Nano Banana 2.1 vs GPT-5.2-Codex
Nano Banana 2.1 costs 1.9x less per output token than GPT-5.2-Codex ($7.50 vs $14.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $7.50
- Coding
- 45.4
- Overall
- No data
- Output $/M
- $14.00
Nano Banana 2.1 or GPT-5.2-Codex: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nano Banana 2.1 has none, so the two cannot be ranked on coding.
Pick Nano Banana 2.1 for output tokens 1.9x cheaper ($7.50 vs $14.00 per million).
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-5.2-Codex on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev. 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
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.2-Codex has a AA Intelligence Index result, 28.5.
Directional only
Image generation
Design Arena: images
No call
Only Nano Banana 2.1 has a Design Arena: images result, 1293.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nano Banana 2.1
Nano Banana 2.1 $0.0053, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- Nano Banana 2.1 not benchmarked, GPT-5.2-Codex 45.4 from 3 benchmarks.
- 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-5.2-Codex $14.00 per million output tokens, 1.9x apart.
- Context window
- Nano Banana 2.1 takes 66K tokens, GPT-5.2-Codex 400K.
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 Index11 boards measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | Not measured | 1339 | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 88.0% | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.1% | Only GPT-5.2-Codex measured |
| Terminal-Bench | Not measured | 66.5% | Only GPT-5.2-Codex measured |
| ALE-Bench | Not measured | 1299.9 | Only GPT-5.2-Codex measured |
| LMArena WebDev: React | Not measured | 1329 | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | Not measured | 1348 | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 28.5 | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | Not measured | 92.1% | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.9% | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.7% | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 20 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 77.6% | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 82.3% | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 44 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Nano Banana 2.1 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: images | 1293 | 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-5.2-Codex |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | October 6, 2026 | January 14, 2026 |
| Context window | 66K | 400K |
| Input $/Mtok | $1.50 | $1.75 |
| Output $/Mtok | $7.50 | $14.00 |
| Coding index | Not benchmarked | 45.4 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nano Banana 2.1 better than GPT-5.2-Codex for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nano Banana 2.1 has none, so the two cannot be ranked on coding. Pick Nano Banana 2.1 for output tokens 1.9x cheaper ($7.50 vs $14.00 per million).
Which is cheaper, Nano Banana 2.1 or GPT-5.2-Codex?
Nano Banana 2.1 costs $1.50 per million input tokens and $7.50 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, Nano Banana 2.1 or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 66K.
Has any benchmark tested both Nano Banana 2.1 and GPT-5.2-Codex?
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-5.2-Codex on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev. Compare them on price and context instead. Data as of October 22, 2026.