Gemini 3.5 Flash vs GLM 4.5 Air
Gemini 3.5 Flash leads GLM 4.5 Air on the Artificial Analysis Intelligence Index, 33.6 to 11.1, and GLM 4.5 Air costs 8.2x less per output token ($1.10 vs $9.00 per million). Updated October 21, 2026; 42 benchmarks have a result for both.
Gemini 3.5 Flash or GLM 4.5 Air: which to pick
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; GLM 4.5 Air has none, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for output tokens 8.2x cheaper ($1.10 vs $9.00 per million).
- Shared results
- 42
- Gemini 3.5 Flash only
- 70
- GLM 4.5 Air only
- 6
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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 Gemini 3.5 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 11.1.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Gemini 3.5 Flash has a GPQA Diamond result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.5 Flash has a MMLU Pro result, 89.5%.
Directional only
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 1146.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5 Air
Gemini 3.5 Flash $0.0060, GLM 4.5 Air $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.5 Flash has the number, $1.21.
Directional only
At a glance
- Coding index
- Gemini 3.5 Flash 39.8 from 4 benchmarks, GLM 4.5 Air not benchmarked.
- Widest category
- Agentic work, 49.1 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Gemini 3.5 Flash 95.6% against GLM 4.5 Air 46.5%, 49.1 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GLM 4.5 Air $1.10 per million output tokens, 8.2x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GLM 4.5 Air 131K.
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.
| Category | Gemini 3.5 Flash | Bars | GLM 4.5 Air | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 42 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| LMArena WebDev | 1499 | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Vals AI) | 87.6% | Not measured | Only Gemini 3.5 Flash measured |
| SWE-bench Verified (Vals AI) | 78.8% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 4 (Vals AI) | 6.1% | Not measured | Only Gemini 3.5 Flash measured |
| Vibe Code Bench (Vals AI) | 48.7% | Not measured | Only Gemini 3.5 Flash measured |
| Code migration (Vals AI) | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.2% | Not measured | Only Gemini 3.5 Flash measured |
| SkillsBench (Vals AI) | 52.7% | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 68.4% | Only GLM 4.5 Air measured |
| SciCode | 53.9% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | 20.5% | Gemini 3.5 Flash |
| GSO-Bench | Not measured | 2.9% | Only GLM 4.5 Air measured |
| WeirdML | 62.6% | Not measured | Only Gemini 3.5 Flash measured |
| ALE-Bench | 911.0 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: coding | 1493 | 1397 | Gemini 3.5 Flash |
| LMArena Text: software and IT | 1494 | 1401 | Gemini 3.5 Flash |
| LMArena WebDev: React | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: HTML | 1522 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: from an image | 1504 | Not measured | Only Gemini 3.5 Flash measured |
| SciCode | 12.3% | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 11.1 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | Not measured | Only Gemini 3.5 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building full-stack apps | 1197 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building mobile apps | 1174 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Android apps | 1184 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Godot games | 1098 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building games | 1180 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: HTML slides | 1142 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: PowerPoint slides | 1179 | Not measured | Only Gemini 3.5 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only Gemini 3.5 Flash measured |
| Tax Agent Bench (Vals AI) | 21.6% | Not measured | Only Gemini 3.5 Flash measured |
| Legal Research Bench (Vals AI) | 30.8% | Not measured | Only Gemini 3.5 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 63.5% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Gemini 3.5 Flash measured |
| τ²-Bench Telecom | 95.6% | 46.5% | Gemini 3.5 Flash |
| APEX-Agents | 27.5% | Not measured | Only Gemini 3.5 Flash measured |
| Vending-Bench 2 | $5,396 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | Not measured | Only Gemini 3.5 Flash measured |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only Gemini 3.5 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 92.2% | 73.3% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 7.0% | Gemini 3.5 Flash |
| GPQA Diamond (Epoch AI run) | 92.8% | Not measured | Only Gemini 3.5 Flash measured |
| ARC-AGI | 92.5% | Not measured | Only Gemini 3.5 Flash measured |
| ARC-AGI-2 | 72.1% | Not measured | Only Gemini 3.5 Flash measured |
| SimpleBench | 76.7% | Not measured | Only Gemini 3.5 Flash measured |
| Chess puzzles | 45.0% | Not measured | Only Gemini 3.5 Flash measured |
| Mystery game puzzles | 32.0% | Not measured | Only Gemini 3.5 Flash measured |
| EBR-bench | 4.8% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: hard prompts | 1488 | 1379 | Gemini 3.5 Flash |
| LMArena Text: expert | 1495 | 1370 | Gemini 3.5 Flash |
| LMArena Text: hard prompts in English | 1483 | 1388 | Gemini 3.5 Flash |
| LMArena Vision: diagrams | 1323 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: homework | 1330 | Not measured | Only Gemini 3.5 Flash measured |
| Kagi LLM Benchmark | Not measured | 43.0% | Only GLM 4.5 Air measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 80.7 | Only GLM 4.5 Air measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 80.7% | Only GLM 4.5 Air measured |
| OTIS Mock AIME | 95.6% | Not measured | Only Gemini 3.5 Flash measured |
| FrontierMath, tiers 1 to 3 | 62.8% | Not measured | Only Gemini 3.5 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProofBench | 31.0% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: math | 1504 | 1396 | Gemini 3.5 Flash |
| LMArena Text: mathematical work | 1495 | 1396 | Gemini 3.5 Flash |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | Not measured | Only Gemini 3.5 Flash measured |
| LegalBench (Vals AI) | 83.6% | Not measured | Only Gemini 3.5 Flash measured |
| CorpFin (Vals AI) | 64.7% | Not measured | Only Gemini 3.5 Flash measured |
| TaxEval (Vals AI) | 74.4% | Not measured | Only Gemini 3.5 Flash measured |
| MortgageTax (Vals AI) | 68.1% | Not measured | Only Gemini 3.5 Flash measured |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 81.5% | Only GLM 4.5 Air measured |
| SimpleQA Verified | 66.2% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: science | 1495 | 1407 | Gemini 3.5 Flash |
| LMArena Text: medicine and healthcare | 1483 | 1402 | Gemini 3.5 Flash |
| LMArena Text: legal and government | 1488 | 1382 | Gemini 3.5 Flash |
| LMArena Text: business and finance | 1467 | 1371 | Gemini 3.5 Flash |
| FACTS Benchmark Suite | 63.1% | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 1146 | Gemini 3.5 Flash |
| Design Arena: UI components | 1273 | 1123 | Gemini 3.5 Flash |
| Design Arena: game dev | 1263 | 1094 | Gemini 3.5 Flash |
| Design Arena: data viz | 1236 | 1186 | Gemini 3.5 Flash |
| Design Arena: 3D | 1244 | 1123 | Gemini 3.5 Flash |
| Design Arena: all code categories | 1266 | 1137 | Gemini 3.5 Flash |
| Design Arena: SVG | 1254 | 1071 | Gemini 3.5 Flash |
| Design Arena: ASCII art | 1254 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 59.5% | Not measured | Only Gemini 3.5 Flash measured |
| SAGE (Vals AI) | 49.9% | Not measured | Only Gemini 3.5 Flash measured |
| MedScribe (Vals AI) | 76.6% | Not measured | Only Gemini 3.5 Flash measured |
| Vals Multimodal Index | 62.9% | Not measured | Only Gemini 3.5 Flash measured |
| IFBench | 76.3% | 37.6% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 46.7% | Gemini 3.5 Flash |
| DTBench | 94.7% | Not measured | Only Gemini 3.5 Flash measured |
| LMCA | 47.1% | Not measured | Only Gemini 3.5 Flash measured |
| Surface Evolver Bench | 58.1% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text | 1482 | 1384 | Gemini 3.5 Flash |
| LMArena Text: instruction following | 1467 | 1354 | Gemini 3.5 Flash |
| LMArena Text: creative writing | 1470 | 1343 | Gemini 3.5 Flash |
| LMArena Vision | 1308 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: English | 1481 | 1397 | Gemini 3.5 Flash |
| LMArena Text: non-English | 1475 | 1366 | Gemini 3.5 Flash |
| LMArena Text: Chinese | 1527 | 1426 | Gemini 3.5 Flash |
| LMArena Text: German | 1494 | 1377 | Gemini 3.5 Flash |
| LMArena Text: Japanese | 1486 | 1348 | Gemini 3.5 Flash |
| LMArena Text: Korean | 1448 | 1308 | Gemini 3.5 Flash |
| LMArena Text: French | 1490 | 1400 | Gemini 3.5 Flash |
| LMArena Text: Spanish | 1480 | 1385 | Gemini 3.5 Flash |
| LMArena Text: Russian | 1493 | 1373 | Gemini 3.5 Flash |
| LMArena Text: Polish | 1516 | 1368 | Gemini 3.5 Flash |
| LMArena Text: multi-turn | 1481 | 1371 | Gemini 3.5 Flash |
| LMArena Text: longer queries | 1483 | 1366 | Gemini 3.5 Flash |
| LMArena Text: writing and language | 1478 | 1354 | Gemini 3.5 Flash |
| LMArena Text: entertainment and media | 1459 | 1339 | Gemini 3.5 Flash |
| LMArena Vision: English | 1305 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: Chinese | 1377 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: reading text | 1317 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: humor | 1321 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Document | 1463 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.5 Flash | GLM 4.5 Air | Leader |
|---|---|---|---|
| Neither model has a result on 5 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 4.5 Air costs less
Repository review
50K input + 3K output tokens
GLM 4.5 Air costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 Air 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 Gemini 3.5 Flash | Z.ai GLM 4.5 Air |
|---|---|---|
| Maker | Z.ai | |
| Weights | No public weights listed | Open weights |
| Released | May 19, 2026 | July 25, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $1.50 | $0.20 |
| Output $/Mtok | $9.00 | $1.10 |
| Coding index | 39.8 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $1.21 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GLM 4.5 Air for coding?
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; GLM 4.5 Air has none, so the two cannot be ranked on coding. Pick GLM 4.5 Air for output tokens 8.2x cheaper ($1.10 vs $9.00 per million).
Which is cheaper, Gemini 3.5 Flash or GLM 4.5 Air?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GLM 4.5 Air costs $0.20 and $1.10. Cost per solved task is known for Gemini 3.5 Flash only.
Which has the bigger context window, Gemini 3.5 Flash or GLM 4.5 Air?
Gemini 3.5 Flash, with 1.05M tokens against 131K.
How many benchmarks is the Gemini 3.5 Flash vs GLM 4.5 Air comparison based on?
42 benchmarks have a result for both models as of October 21, 2026. Gemini 3.5 Flash's coding index rests on 4 independent benchmarks and GLM 4.5 Air's on 0. Where only one model has a result, the page says so instead of naming a winner.