Nano Banana Pro (Gemini 3 Pro Image) vs GLM 4.6V
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $12.00
- Coding
- No data
- Overall
- 11.2
- Output $/M
- $0.90
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 0
- Nano Banana Pro (Gemini 3 Pro Image) only
- 1
- GLM 4.6V only
- 38
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 GLM 4.6V has a AA Intelligence Index result, 11.2.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
Nano Banana Pro (Gemini 3 Pro Image) $0.0080, GLM 4.6V $0.0008 per turn.
List prices
At a glance
- Coding index
- Nano Banana Pro (Gemini 3 Pro Image) not benchmarked, GLM 4.6V not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- Nano Banana Pro (Gemini 3 Pro Image) $12.00 against GLM 4.6V $0.90 per million output tokens, 13.3x apart.
- Context window
- Both take 131K tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 41.1% | Only GLM 4.6V measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 14.4% | Only GLM 4.6V measured |
| LMArena Text: coding | Not measured | 1389 | Only GLM 4.6V measured |
| LMArena Text: software and IT | Not measured | 1391 | Only GLM 4.6V measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.2 | Only GLM 4.6V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 31.6% | Only GLM 4.6V measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 71.9% | Only GLM 4.6V measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 9.6% | Only GLM 4.6V measured |
| LMArena Text: hard prompts | Not measured | 1367 | Only GLM 4.6V measured |
| LMArena Text: expert | Not measured | 1371 | Only GLM 4.6V measured |
| LMArena Text: hard prompts in English | Not measured | 1391 | Only GLM 4.6V measured |
| LMArena Vision: diagrams | Not measured | 1182 | Only GLM 4.6V measured |
| LMArena Vision: homework | Not measured | 1155 | Only GLM 4.6V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 85.3 | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 85.3% | Only GLM 4.6V measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 79.9% | Only GLM 4.6V measured |
| LMArena Text: science | Not measured | 1389 | Only GLM 4.6V measured |
| LMArena Text: medicine and healthcare | Not measured | 1389 | Only GLM 4.6V measured |
| LMArena Text: legal and government | Not measured | 1393 | Only GLM 4.6V measured |
| LMArena Text: business and finance | Not measured | 1359 | Only GLM 4.6V measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| IFBench | Not measured | 30.1% | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | Not measured | 48.7% | Only GLM 4.6V measured |
| LMArena Text | Not measured | 1376 | Only GLM 4.6V measured |
| LMArena Text: instruction following | Not measured | 1350 | Only GLM 4.6V measured |
| LMArena Text: creative writing | Not measured | 1347 | Only GLM 4.6V measured |
| LMArena Vision | Not measured | 1164 | Only GLM 4.6V measured |
| LMArena Text: English | Not measured | 1395 | Only GLM 4.6V measured |
| LMArena Text: non-English | Not measured | 1358 | Only GLM 4.6V measured |
| LMArena Text: Chinese | Not measured | 1425 | Only GLM 4.6V measured |
| LMArena Text: Russian | Not measured | 1339 | Only GLM 4.6V measured |
| LMArena Text: multi-turn | Not measured | 1359 | Only GLM 4.6V measured |
| LMArena Text: longer queries | Not measured | 1357 | Only GLM 4.6V measured |
| LMArena Text: writing and language | Not measured | 1347 | Only GLM 4.6V measured |
| LMArena Text: entertainment and media | Not measured | 1343 | Only GLM 4.6V measured |
| LMArena Vision: English | Not measured | 1179 | Only GLM 4.6V measured |
| LMArena Vision: Chinese | Not measured | 1217 | Only GLM 4.6V measured |
| LMArena Vision: reading text | Not measured | 1171 | Only GLM 4.6V measured |
| LMArena Vision: creative writing | Not measured | 1113 | Only GLM 4.6V measured |
| Neither model has a result on the other 28 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nano Banana Pro (Gemini 3 Pro Image) | GLM 4.6V | Leader |
|---|---|---|---|
| LMArena Image Edit | 1390 | Not measured | Only Nano Banana Pro (Gemini 3 Pro Image) measured |
| Neither model has a result on the other 4 boards here | |||
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
GLM 4.6V costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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 Pro (Gemini 3 Pro Image) | Z.ai GLM 4.6V |
|---|---|---|
| Maker | Z.ai | |
| Weights | No public weights listed | Open weights |
| Released | June 18, 2026 | December 8, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $2.00 | $0.30 |
| Output $/Mtok | $12.00 | $0.90 |
| 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 Pro (Gemini 3 Pro Image) better than GLM 4.6V for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Nano Banana Pro (Gemini 3 Pro Image) or GLM 4.6V?
Nano Banana Pro (Gemini 3 Pro Image) costs $2.00 per million input tokens and $12.00 per million output tokens; GLM 4.6V costs $0.30 and $0.90. Cost per solved task is known for neither of them.
Which has the bigger context window, Nano Banana Pro (Gemini 3 Pro Image) or GLM 4.6V?
Neither: both take 131K tokens.
How many benchmarks is the Nano Banana Pro (Gemini 3 Pro Image) vs GLM 4.6V comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. Nano Banana Pro (Gemini 3 Pro Image)'s coding index rests on 0 independent benchmarks and GLM 4.6V's on 0. Where only one model has a result, the page says so instead of naming a winner.