Gemini 3.5 Flash vs GLM 4.6
Updated October 21, 2026. 56 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 42.8 for Gemini 3.5 Flash.
Pick Gemini 3.5 Flash when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 56
- Gemini 3.5 Flash only
- 54
- GLM 4.6 only
- 10
- Like-for-like categories
- 8 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, four public coding benchmarks
GLM 4.6
GLM 4.6 scores 52.2 against 42.8.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 74.5%.
Same board, both measured
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 1174.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
Gemini 3.5 Flash $0.0060, GLM 4.6 $0.0016 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 42.8 from 1 benchmark, GLM 4.6 52.2 from 2 benchmarks.
- Widest category
- Coding, 19.2 points apart on average over 8 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Gemini 3.5 Flash 48.7% against GLM 4.6 3.1%, 45.6 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GLM 4.6 $2.20 per million output tokens, 4.1x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GLM 4.6 205K.
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 4.6 | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 4 | 1 | ||
| Reasoning | 6 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 8 | 0 | ||
| Web design | 7 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 55 | 1 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index80
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 68.2% | Only GLM 4.6 measured |
| LMArena WebDev | 1499 | 1341 | Gemini 3.5 Flash |
| AA Coding Index | Not measured | 45.8 | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 87.6% | 81.0% | Gemini 3.5 Flash |
| 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% | 3.1% | Gemini 3.5 Flash |
| 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 | 69.5% | Only GLM 4.6 measured |
| SciCode | 53.9% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | 28.8% | Gemini 3.5 Flash |
| Terminal-Bench | Not measured | 24.5% | Only GLM 4.6 measured |
| WeirdML | 62.6% | Not measured | Only Gemini 3.5 Flash measured |
| ALE-Bench | 911.0 | 340.8 | Gemini 3.5 Flash |
| LMArena Text: coding | 1493 | 1449 | Gemini 3.5 Flash |
| LMArena Text: software and IT | 1494 | 1455 | Gemini 3.5 Flash |
| LMArena WebDev: React | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: HTML | 1522 | 1350 | Gemini 3.5 Flash |
| LMArena WebDev: from an image | 1504 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 18.5 | 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 index41
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building full-stack apps | 1197 | 1029 | Gemini 3.5 Flash |
| Design Arena: agents building mobile apps | 1175 | 1096 | Gemini 3.5 Flash |
| Design Arena: agents building Android apps | 1190 | 1090 | Gemini 3.5 Flash |
| Design Arena: agents building Godot games | 1098 | 1180 | GLM 4.6 |
| 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 | 1178 | 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% | 76.9% | 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 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | 74.5% | Gemini 3.5 Flash |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only Gemini 3.5 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 92.2% | 78.0% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 14.5% | 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 | 1440 | Gemini 3.5 Flash |
| LMArena Text: expert | 1495 | 1432 | Gemini 3.5 Flash |
| LMArena Text: hard prompts in English | 1483 | 1445 | 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 |
| 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 4.6 | Leader |
|---|---|---|---|
| AIME | Not measured | 92.7% | Only GLM 4.6 measured |
| MGSM (Vals AI) | Not measured | 89.8% | Only GLM 4.6 measured |
| AA Math Index | Not measured | 86.0 | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 86.0% | Only GLM 4.6 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 | 1432 | Gemini 3.5 Flash |
| LMArena Text: mathematical work | 1495 | 1447 | Gemini 3.5 Flash |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | 82.2% | Gemini 3.5 Flash |
| LegalBench (Vals AI) | 83.6% | 79.6% | Gemini 3.5 Flash |
| CorpFin (Vals AI) | 64.7% | 56.8% | Gemini 3.5 Flash |
| TaxEval (Vals AI) | 74.4% | 66.2% | Gemini 3.5 Flash |
| MortgageTax (Vals AI) | 68.1% | Not measured | Only Gemini 3.5 Flash measured |
| MedQA (Vals AI) | Not measured | 92.2% | Only GLM 4.6 measured |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.9% | Only GLM 4.6 measured |
| SimpleQA Verified | 66.2% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: science | 1495 | 1456 | Gemini 3.5 Flash |
| LMArena Text: medicine and healthcare | 1483 | 1455 | Gemini 3.5 Flash |
| LMArena Text: legal and government | 1488 | 1448 | Gemini 3.5 Flash |
| LMArena Text: business and finance | 1467 | 1416 | Gemini 3.5 Flash |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Gemini 3.5 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 1174 | Gemini 3.5 Flash |
| Design Arena: UI components | 1272 | 1147 | Gemini 3.5 Flash |
| Design Arena: game dev | 1263 | 1151 | Gemini 3.5 Flash |
| Design Arena: data viz | 1236 | 1161 | Gemini 3.5 Flash |
| Design Arena: 3D | 1244 | 1129 | Gemini 3.5 Flash |
| Design Arena: all code categories | 1266 | 1166 | Gemini 3.5 Flash |
| Design Arena: SVG | 1254 | 1108 | Gemini 3.5 Flash |
| Design Arena: ASCII art | 1253 | 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 4.6 | 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% | 43.4% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 54.0% | 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 | 1440 | Gemini 3.5 Flash |
| LMArena Text: instruction following | 1467 | 1410 | Gemini 3.5 Flash |
| LMArena Text: creative writing | 1470 | 1412 | Gemini 3.5 Flash |
| LMArena Vision | 1308 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: English | 1481 | 1449 | Gemini 3.5 Flash |
| LMArena Text: non-English | 1475 | 1426 | Gemini 3.5 Flash |
| LMArena Text: Chinese | 1527 | 1499 | Gemini 3.5 Flash |
| LMArena Text: German | 1494 | 1447 | Gemini 3.5 Flash |
| LMArena Text: Japanese | 1486 | 1393 | Gemini 3.5 Flash |
| LMArena Text: Korean | 1448 | 1400 | Gemini 3.5 Flash |
| LMArena Text: French | 1490 | 1459 | Gemini 3.5 Flash |
| LMArena Text: Spanish | 1480 | 1436 | Gemini 3.5 Flash |
| LMArena Text: Russian | 1493 | 1418 | Gemini 3.5 Flash |
| LMArena Text: Polish | 1516 | 1447 | Gemini 3.5 Flash |
| LMArena Text: multi-turn | 1481 | 1427 | Gemini 3.5 Flash |
| LMArena Text: longer queries | 1483 | 1422 | Gemini 3.5 Flash |
| LMArena Text: writing and language | 1478 | 1415 | Gemini 3.5 Flash |
| LMArena Text: entertainment and media | 1459 | 1409 | 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 4.6 | 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 4.6 costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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 4.6 |
|---|---|---|
| Maker | Z.ai | |
| Weights | No public weights listed | Open weights |
| Released | May 19, 2026 | September 30, 2025 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $1.50 | $0.55 |
| Output $/Mtok | $9.00 | $2.20 |
| Coding index | 42.8 | 52.2 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $1.21 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GLM 4.6 for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 42.8 for Gemini 3.5 Flash. Pick Gemini 3.5 Flash when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, Gemini 3.5 Flash or GLM 4.6?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GLM 4.6 costs $0.55 and $2.20. Cost per solved task is known for Gemini 3.5 Flash only.
Which has the bigger context window, Gemini 3.5 Flash or GLM 4.6?
Gemini 3.5 Flash, with 1.05M tokens against 205K.
How many benchmarks is the Gemini 3.5 Flash vs GLM 4.6 comparison based on?
56 benchmarks have a result for both models as of October 21, 2026. Gemini 3.5 Flash's coding index rests on 1 independent benchmark and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.