Gemini 3.7 Flash vs GLM 4.7
Gemini 3.7 Flash leads GLM 4.7 on coding, 58.0 to 55.1 on the Vibe Coding Index, and GLM 4.7 costs 1.7x less per output token ($2.20 vs $3.75 per million). Updated October 22, 2026; 63 benchmarks have a result for both.
Gemini 3.7 Flash or GLM 4.7: which to pick
Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 55.1 for GLM 4.7.
Pick GLM 4.7 for output tokens 1.7x cheaper ($2.20 vs $3.75 per million) and a lower cost per solved task ($0.29 against $1.78).
- Shared results
- 63
- Gemini 3.7 Flash only
- 51
- GLM 4.7 only
- 18
- 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
Gemini 3.7 Flash
Gemini 3.7 Flash scores 58.0 against 55.1.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 80.0%.
Same board, both measured
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1303 against 1225.
Same board, both measured
Long documents
Listed context window
Gemini 3.7 Flash
Gemini 3.7 Flash takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
Gemini 3.7 Flash $0.0026, GLM 4.7 $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
Gemini 3.7 Flash $1.78, GLM 4.7 $0.29.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 3.7 Flash 58.0 from 6 benchmarks, GLM 4.7 55.1 from 2 benchmarks.
- Widest category
- Math, 23.9 points apart on average over 4 shared benchmarks.
- Widest single gap
- ProofBench: Gemini 3.7 Flash 58.0% against GLM 4.7 6.0%, 52.0 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.7 Flash $3.75 against GLM 4.7 $2.20 per million output tokens, 1.7x apart.
- Context window
- Gemini 3.7 Flash takes 1.05M tokens, GLM 4.7 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 3.7 Flash | Bars | GLM 4.7 | Ties |
|---|---|---|---|---|
| Coding | 9 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 9 | 0 | ||
| Web design | 8 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 63 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index90
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| LiveBench coding | 80.5% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev | 1591 | 1435 | Gemini 3.7 Flash |
| AA Coding Index | 71.5 | 45.3 | Gemini 3.7 Flash |
| LiveCodeBench (Vals AI) | 88.7% | 82.2% | Gemini 3.7 Flash |
| SWE-bench Verified (Vals AI) | 80.8% | 69.4% | Gemini 3.7 Flash |
| Terminal-Bench 4 (Vals AI) | 12.1% | Not measured | Only Gemini 3.7 Flash measured |
| Vibe Code Bench (Vals AI) | 70.4% | Not measured | Only Gemini 3.7 Flash measured |
| Code migration (Vals AI) | 34.8% | Not measured | Only Gemini 3.7 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.7 Flash measured |
| IOI (Vals AI) | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | Not measured | Only Gemini 3.7 Flash measured |
| SkillsBench (Vals AI) | 65.9% | Not measured | Only Gemini 3.7 Flash measured |
| CyberBench (Vals AI) | 43.8% | Not measured | Only Gemini 3.7 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 89.4% | Only GLM 4.7 measured |
| SciCode | 59.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.8% | Only GLM 4.7 measured |
| Terminal-Bench | Not measured | 33.4% | Only GLM 4.7 measured |
| FrontierCode | 43.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierSWE | 20.3% | Not measured | Only Gemini 3.7 Flash measured |
| ALE-Bench | 904.3 | 399.5 | Gemini 3.7 Flash |
| LMArena Text: coding | 1499 | 1454 | Gemini 3.7 Flash |
| LMArena Text: software and IT | 1498 | 1453 | Gemini 3.7 Flash |
| LMArena WebDev: React | 1587 | 1420 | Gemini 3.7 Flash |
| LMArena WebDev: HTML | 1593 | 1447 | Gemini 3.7 Flash |
| LMArena WebDev: from an image | 1599 | Not measured | Only Gemini 3.7 Flash measured |
| SciCode | 13.8% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.6 | 22.2 | Gemini 3.7 Flash |
| Epoch Capabilities Index | 158.0 | 143.6 | Gemini 3.7 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1223 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building full-stack apps | 1208 | 1050 | Gemini 3.7 Flash |
| Design Arena: agents building mobile apps | 1229 | 1105 | Gemini 3.7 Flash |
| Design Arena: agents building Android apps | 1243 | 1112 | Gemini 3.7 Flash |
| Design Arena: agents building Godot games | 1264 | 1058 | Gemini 3.7 Flash |
| Design Arena: agents building games | 1235 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: spreadsheets | 1058 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench research | 76.5% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench browser | 56.0% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench long agentic | 28.3% | Not measured | Only Gemini 3.7 Flash measured |
| SREBench (Vals AI) | 4.6% | Not measured | Only Gemini 3.7 Flash measured |
| Finance Agent (Vals AI) | 59.0% | Not measured | Only Gemini 3.7 Flash measured |
| Tax Agent Bench (Vals AI) | 22.8% | Not measured | Only Gemini 3.7 Flash measured |
| Legal Research Bench (Vals AI) | 34.6% | Not measured | Only Gemini 3.7 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 71.3% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench-Science (Vals AI) | 7.1% | Not measured | Only Gemini 3.7 Flash measured |
| τ²-Bench Telecom | Not measured | 95.9% | Only GLM 4.7 measured |
| APEX-Agents | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Remote Labor Index | 5.0% | Not measured | Only Gemini 3.7 Flash measured |
| Vending-Bench 2 | Not measured | $2,377 | Only GLM 4.7 measured |
| BrowseComp-Plus | Not measured | 64.7% | Only GLM 4.7 measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| GPQA Diamond | 93.9% | 80.0% | Gemini 3.7 Flash |
| LiveBench reasoning | 87.8% | Not measured | Only Gemini 3.7 Flash measured |
| MMMU Pro (Vals AI) | 89.0% | Not measured | Only Gemini 3.7 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 94.5% | 85.9% | Gemini 3.7 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 27.4% | Gemini 3.7 Flash |
| GPQA Diamond (Epoch AI run) | 94.8% | 83.3% | Gemini 3.7 Flash |
| ARC-AGI | 95.5% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI-2 | 84.6% | Not measured | Only Gemini 3.7 Flash measured |
| SimpleBench | Not measured | 47.7% | Only GLM 4.7 measured |
| Chess puzzles | 66.0% | 21.0% | Gemini 3.7 Flash |
| Mystery game puzzles | 37.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: hard prompts | 1495 | 1443 | Gemini 3.7 Flash |
| LMArena Text: expert | 1512 | 1424 | Gemini 3.7 Flash |
| LMArena Text: hard prompts in English | 1487 | 1452 | Gemini 3.7 Flash |
| LMArena Vision: diagrams | 1329 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: homework | 1320 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| AIME | Not measured | 93.3% | Only GLM 4.7 measured |
| MGSM (Vals AI) | Not measured | 88.2% | Only GLM 4.7 measured |
| AA Math Index | Not measured | 95.0 | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.0% | Only GLM 4.7 measured |
| OTIS Mock AIME | 97.2% | 83.3% | Gemini 3.7 Flash |
| FrontierMath, tiers 1 to 3 | 71.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tier 4 | 36.6% | Not measured | Only Gemini 3.7 Flash measured |
| ProofBench | 58.0% | 6.0% | Gemini 3.7 Flash |
| LMArena Text: math | 1511 | 1424 | Gemini 3.7 Flash |
| LMArena Text: mathematical work | 1502 | 1411 | Gemini 3.7 Flash |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| MMLU Pro | 90.1% | 82.7% | Gemini 3.7 Flash |
| LegalBench (Vals AI) | 87.3% | 83.4% | Gemini 3.7 Flash |
| CaseLaw (Vals AI) | Not measured | 54.9% | Only GLM 4.7 measured |
| CorpFin (Vals AI) | Not measured | 46.4% | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 74.7% | 68.8% | Gemini 3.7 Flash |
| MortgageTax (Vals AI) | 66.7% | Not measured | Only Gemini 3.7 Flash measured |
| MedQA (Vals AI) | Not measured | 93.7% | Only GLM 4.7 measured |
| MedCode (Vals AI) | 53.4% | 32.8% | Gemini 3.7 Flash |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.6% | Only GLM 4.7 measured |
| SimpleQA Verified | 69.2% | 32.2% | Gemini 3.7 Flash |
| LMArena Text: science | 1511 | 1457 | Gemini 3.7 Flash |
| LMArena Text: medicine and healthcare | 1494 | 1469 | Gemini 3.7 Flash |
| LMArena Text: legal and government | 1496 | 1444 | Gemini 3.7 Flash |
| LMArena Text: business and finance | 1479 | 1429 | Gemini 3.7 Flash |
| FACTS Benchmark Suite | 71.2% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| Design Arena: websites | 1303 | 1225 | Gemini 3.7 Flash |
| Design Arena: UI components | 1296 | 1196 | Gemini 3.7 Flash |
| Design Arena: game dev | 1317 | 1188 | Gemini 3.7 Flash |
| Design Arena: data viz | 1315 | 1195 | Gemini 3.7 Flash |
| Design Arena: 3D | 1307 | 1190 | Gemini 3.7 Flash |
| Design Arena: all code categories | 1308 | 1216 | Gemini 3.7 Flash |
| Design Arena: SVG | 1283 | 1142 | Gemini 3.7 Flash |
| Design Arena: ASCII art | 1276 | 1165 | Gemini 3.7 Flash |
| LiveBench design | 73.2% | Not measured | Only Gemini 3.7 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | Gemini 3.7 Flash | GLM 4.7 | Leader |
|---|---|---|---|
| LiveBench data analysis | 68.0% | Not measured | Only Gemini 3.7 Flash measured |
| SAGE (Vals AI) | 49.2% | Not measured | Only Gemini 3.7 Flash measured |
| MedScribe (Vals AI) | 83.9% | 68.6% | Gemini 3.7 Flash |
| IFBench | Not measured | 67.9% | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 83.0% | 71.0% | Gemini 3.7 Flash |
| DTBench | 96.8% | Not measured | Only Gemini 3.7 Flash measured |
| LMCA | 50.4% | Not measured | Only Gemini 3.7 Flash measured |
| CL-bench | Not measured | 15.9% | Only GLM 4.7 measured |
| Furniture assembly | 26.7% | Not measured | Only Gemini 3.7 Flash measured |
| CL-bench Life | Not measured | 10.9% | Only GLM 4.7 measured |
| LMArena Text | 1489 | 1435 | Gemini 3.7 Flash |
| LMArena Text: instruction following | 1484 | 1412 | Gemini 3.7 Flash |
| LMArena Text: creative writing | 1488 | 1401 | Gemini 3.7 Flash |
| LMArena Vision | 1314 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: English | 1483 | 1453 | Gemini 3.7 Flash |
| LMArena Text: non-English | 1487 | 1417 | Gemini 3.7 Flash |
| LMArena Text: Chinese | 1552 | 1495 | Gemini 3.7 Flash |
| LMArena Text: German | 1506 | 1426 | Gemini 3.7 Flash |
| LMArena Text: Japanese | 1511 | 1439 | Gemini 3.7 Flash |
| LMArena Text: Korean | 1481 | 1399 | Gemini 3.7 Flash |
| LMArena Text: French | 1500 | 1432 | Gemini 3.7 Flash |
| LMArena Text: Spanish | 1495 | 1434 | Gemini 3.7 Flash |
| LMArena Text: Russian | 1515 | 1423 | Gemini 3.7 Flash |
| LMArena Text: Polish | 1503 | 1435 | Gemini 3.7 Flash |
| LMArena Text: multi-turn | 1485 | 1446 | Gemini 3.7 Flash |
| LMArena Text: longer queries | 1492 | 1432 | Gemini 3.7 Flash |
| LMArena Text: writing and language | 1491 | 1411 | Gemini 3.7 Flash |
| LMArena Text: entertainment and media | 1477 | 1400 | Gemini 3.7 Flash |
| LMArena Vision: English | 1309 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: Chinese | 1394 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: reading text | 1322 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: humor | 1299 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: creative writing | 1312 | Not measured | Only Gemini 3.7 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 3.7 Flash | GLM 4.7 | 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 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 3.7 Flash | Z.ai GLM 4.7 |
|---|---|---|
| Maker | Z.ai | |
| Weights | No public weights listed | Open weights |
| Released | August 13, 2026 | December 22, 2025 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $0.75 | $0.60 |
| Output $/Mtok | $3.75 | $2.20 |
| Coding index | 58.0 | 55.1 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $1.78 | $0.29 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.7 Flash better than GLM 4.7 for coding?
Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for output tokens 1.7x cheaper ($2.20 vs $3.75 per million) and a lower cost per solved task ($0.29 against $1.78).
Which is cheaper, Gemini 3.7 Flash or GLM 4.7?
Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and GLM 4.7 $0.29.
Which has the bigger context window, Gemini 3.7 Flash or GLM 4.7?
Gemini 3.7 Flash, with 1.05M tokens against 205K.
How many benchmarks is the Gemini 3.7 Flash vs GLM 4.7 comparison based on?
63 benchmarks have a result for both models as of October 22, 2026. Gemini 3.7 Flash's coding index rests on 6 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.