GLM 5 Turbo vs Ling 3.0 Flash VL
GLM 5 Turbo leads Ling 3.0 Flash VL on the Artificial Analysis Intelligence Index, 26.6 to 24.6, and Ling 3.0 Flash VL costs 22.2x less per output token ($0.18 vs $4.00 per million). Updated October 23, 2026; 4 benchmarks have a result for both.
GLM 5 Turbo or Ling 3.0 Flash VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5 Turbo for a larger context window (203K against 131K tokens) and work measured by GPQA Diamond (Artificial Analysis run) or Long context reasoning (AA-LCR), where it leads.
- Shared results
- 4
- GLM 5 Turbo only
- 12
- Ling 3.0 Flash VL only
- 3
- 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
GLM 5 Turbo
GLM 5 Turbo leads, 26.6 against 24.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash VL has a AA Agentic Index result, 28.7.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5 Turbo has a Design Arena: websites result, 1262.
Directional only
Long documents
Listed context window
GLM 5 Turbo
GLM 5 Turbo takes 203K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash VL
GLM 5 Turbo $0.0032, Ling 3.0 Flash VL $0.0001 per turn.
List prices
At a glance
- Coding index
- GLM 5 Turbo not benchmarked, Ling 3.0 Flash VL not benchmarked.
- Widest category
- General, 6.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 5 Turbo 71.7% against Ling 3.0 Flash VL 78.3%, 6.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5 Turbo $4.00 against Ling 3.0 Flash VL $0.18 per million output tokens, 22.2x apart.
- Context window
- GLM 5 Turbo takes 203K tokens, Ling 3.0 Flash VL 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 | GLM 5 Turbo | Bars | Ling 3.0 Flash VL | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 2 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 57.0 | Only Ling 3.0 Flash VL measured |
| SciCode | Not measured | 44.2% | Only Ling 3.0 Flash VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GLM 5 Turbo measured |
| ALE-Bench | 634.0 | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.6 | 24.6 | GLM 5 Turbo |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 28.7 | Only Ling 3.0 Flash VL measured |
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.7% | 86.2% | Ling 3.0 Flash VL |
| Humanity's Last Exam (Artificial Analysis run) | 27.8% | 22.0% | GLM 5 Turbo |
| Neither model has a result on the other 21 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 14 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: UI components | 1261 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: game dev | 1247 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: data viz | 1264 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: 3D | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: all code categories | 1261 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: SVG | 1205 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: ASCII art | 1160 | Not measured | Only GLM 5 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 5 Turbo | Ling 3.0 Flash VL | Leader |
|---|---|---|---|
| IFBench | 73.2% | Not measured | Only GLM 5 Turbo measured |
| Long context reasoning (AA-LCR) | 71.7% | 78.3% | Ling 3.0 Flash VL |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | Ling 3.0 Flash VL | 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 VL costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash VL costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash VL 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 Turbo | inclusionAI Ling 3.0 Flash VL |
|---|---|---|
| Maker | Z.ai | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | March 15, 2026 | September 10, 2026 |
| Context window | 203K | 131K |
| Input $/Mtok | $1.20 | $0.06 |
| Output $/Mtok | $4.00 | $0.18 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 Turbo better than Ling 3.0 Flash VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5 Turbo for a larger context window (203K against 131K tokens) and work measured by GPQA Diamond (Artificial Analysis run) or Long context reasoning (AA-LCR), where it leads.
Which is cheaper, GLM 5 Turbo or Ling 3.0 Flash VL?
GLM 5 Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5 Turbo or Ling 3.0 Flash VL?
GLM 5 Turbo, with 203K tokens against 131K.
How many benchmarks is the GLM 5 Turbo vs Ling 3.0 Flash VL comparison based on?
4 benchmarks have a result for both models as of October 23, 2026. GLM 5 Turbo's coding index rests on 0 independent benchmarks and Ling 3.0 Flash VL's on 0. Where only one model has a result, the page says so instead of naming a winner.