GLM 5.2 vs Ling 3.0 Flash
GLM 5.2 leads Ling 3.0 Flash on coding, 61.1 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 36.2x less per output token ($0.1215 vs $4.40 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
GLM 5.2 or Ling 3.0 Flash: which to pick
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 36.2x cheaper ($0.1215 vs $4.40 per million) and a lower cost per solved task ($0.05 against $0.86).
- Shared results
- 23
- GLM 5.2 only
- 75
- Ling 3.0 Flash only
- 1
- Like-for-like categories
- 6 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
GLM 5.2
GLM 5.2 scores 61.1 against 29.6.
4 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 20.1.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 19.3.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.2
GLM 5.2 leads, 85.6% against 84.8%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 82.0%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
GLM 5.2 $0.0036, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
GLM 5.2 $0.86, Ling 3.0 Flash $0.05.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Agentic work, 26.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.2 64.0% against Ling 3.0 Flash 2.9%, 61.1 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against Ling 3.0 Flash $0.1215 per million output tokens, 36.2x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, Ling 3.0 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 5.2 | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 7 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 22 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index71
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | Not measured | Only GLM 5.2 measured |
| AA Coding Index | 68.8 | 50.6 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 84.0% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 82.8% | 65.2% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 64.0% | 2.9% | GLM 5.2 |
| Code migration (Vals AI) | 37.9% | 0.0% | GLM 5.2 |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 50.2% | GLM 5.2 |
| SkillsBench (Vals AI) | 45.1% | 27.9% | GLM 5.2 |
| SciCode | 51.2% | 42.0% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: software and IT | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: React | 1617 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: HTML | 1565 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 20.1 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | Not measured | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 19.3 | GLM 5.2 |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1178 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1125 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1170 | Not measured | Only GLM 5.2 measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | 30.3% | GLM 5.2 |
| Legal Research Bench (Vals AI) | 31.3% | 0.0% | GLM 5.2 |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 25.0% | GLM 5.2 |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 84.8% | GLM 5.2 |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 85.5% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 23.7% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | Not measured | Only GLM 5.2 measured |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | Not measured | Only GLM 5.2 measured |
| LMArena Text: expert | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | 1481 | Not measured | Only GLM 5.2 measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | Not measured | Only GLM 5.2 measured |
| LMArena Text: mathematical work | 1489 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 82.0% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 79.7% | GLM 5.2 |
| CorpFin (Vals AI) | 66.1% | 61.9% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 70.7% | GLM 5.2 |
| MedCode (Vals AI) | 40.8% | 32.3% | GLM 5.2 |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | Not measured | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: legal and government | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena Text: business and finance | 1451 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: UI components | 1294 | Not measured | Only GLM 5.2 measured |
| Design Arena: game dev | 1268 | Not measured | Only GLM 5.2 measured |
| Design Arena: data viz | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: 3D | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: all code categories | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: SVG | 1213 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1191 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | 83.5% | 80.9% | GLM 5.2 |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 73.0% | GLM 5.2 |
| DTBench | 93.6% | Not measured | Only GLM 5.2 measured |
| LMCA | 45.8% | Not measured | Only GLM 5.2 measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GLM 5.2 measured |
| LMArena Text: creative writing | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: English | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Chinese | 1522 | Not measured | Only GLM 5.2 measured |
| LMArena Text: German | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1440 | Not measured | Only GLM 5.2 measured |
| LMArena Text: French | 1479 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Spanish | 1478 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Russian | 1468 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Polish | 1456 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.2 measured |
| LMArena Text: longer queries | 1480 | Not measured | Only GLM 5.2 measured |
| LMArena Text: writing and language | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Ling 3.0 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 3.0 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 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 5.2 | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | Z.ai | inclusionAI |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | July 23, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.40 | $0.0405 |
| Output $/Mtok | $4.40 | $0.1215 |
| Coding index | 61.1 | 29.6 |
| Independent measurements behind it | 4 | 2 |
| Cost per solved task | $0.86 | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Ling 3.0 Flash for coding?
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 36.2x cheaper ($0.1215 vs $4.40 per million) and a lower cost per solved task ($0.05 against $0.86).
Which is cheaper, GLM 5.2 or Ling 3.0 Flash?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Ling 3.0 Flash $0.05.
Which has the bigger context window, GLM 5.2 or Ling 3.0 Flash?
GLM 5.2, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.2 vs Ling 3.0 Flash comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Ling 3.0 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.