GLM 5V Turbo vs Ling 3.1 Flash
Ling 3.1 Flash leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 41.1 to 23.5, and Ling 3.1 Flash costs 17.8x less per output token ($0.225 vs $4.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GLM 5V Turbo or Ling 3.1 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 3
- GLM 5V Turbo only
- 62
- Ling 3.1 Flash only
- 1
- Like-for-like categories
- 3 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 23.5.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5V Turbo has a Design Arena: websites result, 1230.
Directional only
Long documents
Listed context window
Ling 3.1 Flash
Ling 3.1 Flash takes 262K tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
GLM 5V Turbo $0.0032, Ling 3.1 Flash $0.0002 per turn.
List prices
At a glance
- Coding index
- GLM 5V Turbo not benchmarked, Ling 3.1 Flash not benchmarked.
- Widest category
- Reasoning, 22.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GLM 5V Turbo 17.1% against Ling 3.1 Flash 39.4%, 22.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5V Turbo $4.00 against Ling 3.1 Flash $0.225 per million output tokens, 17.8x apart.
- Context window
- GLM 5V Turbo takes 203K tokens, Ling 3.1 Flash 262K.
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 | GLM 5V Turbo | Bars | Ling 3.1 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1402 | Not measured | Only GLM 5V Turbo measured |
| SciCode | Not measured | 54.1% | Only Ling 3.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: coding | 1466 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: software and IT | 1460 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: React | 1386 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: HTML | 1356 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: from an image | 1425 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.5 | 41.1 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1124 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building full-stack apps | 1149 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building mobile apps | 1134 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Android apps | 1267 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Godot games | 957 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building games | 1102 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: HTML slides | 1060 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: PowerPoint slides | 1126 | Not measured | Only GLM 5V Turbo measured |
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 80.9% | Not measured | Only GLM 5V Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 39.4% | Ling 3.1 Flash |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: expert | 1453 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: hard prompts in English | 1456 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: diagrams | 1286 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: homework | 1273 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LMArena Text: math | 1442 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LMArena Text: science | 1448 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: medicine and healthcare | 1433 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: legal and government | 1436 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: business and finance | 1435 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: UI components | 1210 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: game dev | 1218 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: data viz | 1202 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: 3D | 1208 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: all code categories | 1227 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: SVG | 1150 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: ASCII art | 1116 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | Leader |
|---|---|---|---|
| IFBench | 61.1% | Not measured | Only GLM 5V Turbo measured |
| Long context reasoning (AA-LCR) | 70.3% | 83.0% | Ling 3.1 Flash |
| LMArena Text | 1437 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: instruction following | 1423 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: creative writing | 1417 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: English | 1454 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: non-English | 1420 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Chinese | 1491 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: German | 1422 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Korean | 1397 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: French | 1443 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Spanish | 1452 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Russian | 1432 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Polish | 1432 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: multi-turn | 1431 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: longer queries | 1438 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: writing and language | 1420 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: English | 1274 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: Chinese | 1329 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: reading text | 1270 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: humor | 1268 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: captioning | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: recognizing things | 1241 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: creative writing | 1252 | Not measured | Only GLM 5V Turbo measured |
| LMArena Document | 1416 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5V Turbo | Ling 3.1 Flash | 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
Ling 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 Flash 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 | Z.ai GLM 5V Turbo | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | Z.ai | inclusionAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 1, 2026 | October 2, 2026 |
| Context window | 203K | 262K |
| Input $/Mtok | $1.20 | $0.075 |
| Output $/Mtok | $4.00 | $0.225 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5V Turbo better than Ling 3.1 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5V Turbo for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GLM 5V Turbo or Ling 3.1 Flash?
GLM 5V Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5V Turbo or Ling 3.1 Flash?
Ling 3.1 Flash, with 262K tokens against 203K.
How many benchmarks is the GLM 5V Turbo vs Ling 3.1 Flash comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GLM 5V Turbo's coding index rests on 1 independent benchmark and Ling 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.