R1 0528 vs Ling 3.0 Flash
Ling 3.0 Flash leads R1 0528 on the Artificial Analysis Intelligence Index, 20.1 to 13.1, and Ling 3.0 Flash costs 17.9x less per output token ($0.1215 vs $2.18 per million). Updated October 23, 2026; 4 benchmarks have a result for both.
R1 0528 or Ling 3.0 Flash: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 4
- R1 0528 only
- 59
- Ling 3.0 Flash only
- 20
- 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
Only Ling 3.0 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash has a AA Agentic Index result, 19.3.
Directional only
Reasoning
GPQA Diamond
No call
Only Ling 3.0 Flash has a GPQA Diamond result, 84.8%.
Directional only
Knowledge
MMLU Pro
No call
Only Ling 3.0 Flash has a MMLU Pro result, 82.0%.
Directional only
Web design
Design Arena: websites
No call
Only R1 0528 has a Design Arena: websites result, 1149.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
R1 0528 $0.0016, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Ling 3.0 Flash has the number, $0.05.
Directional only
At a glance
- Coding index
- R1 0528 not benchmarked, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- General, 17.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): R1 0528 55.7% against Ling 3.0 Flash 73.0%, 17.3 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against Ling 3.0 Flash $0.1215 per million output tokens, 17.9x apart.
- Context window
- R1 0528 takes 164K 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 | R1 0528 | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index15 boards, none shared
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| AA Coding Index | Not measured | 50.6 | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 65.2% | Only Ling 3.0 Flash measured |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Ling 3.0 Flash measured |
| Code migration (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 50.2% | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | Not measured | 27.9% | Only Ling 3.0 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| SciCode | Not measured | 42.0% | Only Ling 3.0 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | Not measured | Only R1 0528 measured |
| LMArena Text: coding | 1427 | Not measured | Only R1 0528 measured |
| LMArena Text: software and IT | 1435 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 20.1 | Ling 3.0 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | Not measured | 30.3% | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 25.0% | Only Ling 3.0 Flash measured |
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| DeepResearch Bench | 35.1% | Not measured | Only R1 0528 measured |
| BrowseComp-Plus | 16.4% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.8% | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | 85.5% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 23.7% | Ling 3.0 Flash |
| GPQA Diamond (Epoch AI run) | 76.3% | Not measured | Only R1 0528 measured |
| ARC-AGI | 21.2% | Not measured | Only R1 0528 measured |
| ARC-AGI-2 | 1.1% | Not measured | Only R1 0528 measured |
| SimpleBench | 40.8% | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only R1 0528 measured |
| LMArena Text: expert | 1393 | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts in English | 1422 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Math Index | 76.0 | Not measured | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | 76.0% | Not measured | Only R1 0528 measured |
| OTIS Mock AIME | 66.4% | Not measured | Only R1 0528 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | Not measured | Only R1 0528 measured |
| LMArena Text: mathematical work | 1415 | Not measured | Only R1 0528 measured |
| MathArena | 12.9% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.0% | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | Not measured | 79.7% | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | Not measured | 70.7% | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | Not measured | 32.3% | Only Ling 3.0 Flash measured |
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| LMArena Text: science | 1430 | Not measured | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | 1445 | Not measured | Only R1 0528 measured |
| LMArena Text: legal and government | 1419 | Not measured | Only R1 0528 measured |
| LMArena Text: business and finance | 1401 | Not measured | Only R1 0528 measured |
| PRBench Finance | 32.7% | Not measured | Only R1 0528 measured |
| PRBench Legal | 36.6% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | Not measured | Only R1 0528 measured |
| Design Arena: UI components | 1100 | Not measured | Only R1 0528 measured |
| Design Arena: game dev | 1097 | Not measured | Only R1 0528 measured |
| Design Arena: data viz | 1183 | Not measured | Only R1 0528 measured |
| Design Arena: 3D | 1112 | Not measured | Only R1 0528 measured |
| Design Arena: all code categories | 1138 | Not measured | Only R1 0528 measured |
| Design Arena: SVG | 1038 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | Not measured | 80.9% | Only Ling 3.0 Flash measured |
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | 73.0% | Ling 3.0 Flash |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| LMArena Text | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: instruction following | 1381 | Not measured | Only R1 0528 measured |
| LMArena Text: creative writing | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: English | 1437 | Not measured | Only R1 0528 measured |
| LMArena Text: non-English | 1412 | Not measured | Only R1 0528 measured |
| LMArena Text: Chinese | 1442 | Not measured | Only R1 0528 measured |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: Korean | 1359 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | Not measured | Only R1 0528 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only R1 0528 measured |
| LMArena Text: Russian | 1423 | Not measured | Only R1 0528 measured |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: longer queries | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: writing and language | 1401 | Not measured | Only R1 0528 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | 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 | DeepSeek R1 0528 | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | DeepSeek | inclusionAI |
| Weights | Open weights | Open weights |
| Released | May 28, 2025 | July 23, 2026 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.50 | $0.0405 |
| Output $/Mtok | $2.18 | $0.1215 |
| Coding index | Not benchmarked | 29.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than Ling 3.0 Flash for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, R1 0528 or Ling 3.0 Flash?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, R1 0528 or Ling 3.0 Flash?
Ling 3.0 Flash, with 262K tokens against 164K.
How many benchmarks is the R1 0528 vs Ling 3.0 Flash comparison based on?
4 benchmarks have a result for both models as of October 23, 2026. R1 0528's coding index rests on 0 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.