Ling 3.0 Flash vs o3
o3 leads Ling 3.0 Flash on the Artificial Analysis Intelligence Index, 20.2 to 20.1, and Ling 3.0 Flash costs 65.8x less per output token ($0.1215 vs $8.00 per million). Updated October 21, 2026; 12 benchmarks have a result for both.
Ling 3.0 Flash or o3: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.
Pick o3 for work measured by AA Intelligence Index or MMLU Pro, where it leads.
- Shared results
- 12
- Ling 3.0 Flash only
- 12
- o3 only
- 90
- Like-for-like categories
- 5 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
o3
o3 leads, 20.2 against 20.1.
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
Ling 3.0 Flash
Ling 3.0 Flash leads, 84.8% against 84.1%.
Same board, both measured
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 82.0%.
Same board, both measured
Web design
Design Arena: websites
No call
Only o3 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Ling 3.0 Flash $0.05, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, o3 not benchmarked.
- Widest category
- Knowledge, 5.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- MedCode (Vals AI): Ling 3.0 Flash 32.3% against o3 47.3%, 15.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against o3 $8.00 per million output tokens, 65.8x apart.
- Context window
- Ling 3.0 Flash takes 262K tokens, o3 200K.
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 | Ling 3.0 Flash | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 1 | 4 | ||
| General | 1 | 1 | ||
| All shared boards | 6 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | Not measured | 58.4% | Only o3 measured |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | 83.9% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 65.2% | Not measured | Only Ling 3.0 Flash measured |
| Vibe Code Bench (Vals AI) | 2.9% | Not measured | Only Ling 3.0 Flash measured |
| Code migration (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | 27.9% | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.8% | Only o3 measured |
| SciCode | 42.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.1% | Only o3 measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | Not measured | 933.5 | Only o3 measured |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 20.2 | o3 |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | Not measured | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | 30.3% | Not measured | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 25.0% | Not measured | Only Ling 3.0 Flash measured |
| τ²-Bench Telecom | Not measured | 80.7% | Only o3 measured |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 63.0% | Only o3 measured |
| BrowseComp-Plus | Not measured | 65.9% | Only o3 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | 84.1% | Ling 3.0 Flash |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 82.7% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 20.1% | Ling 3.0 Flash |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| AIME | Not measured | 85.3% | Only o3 measured |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | Not measured | 91.8% | Only o3 measured |
| AA Math Index | Not measured | 88.3 | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 88.3% | Only o3 measured |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | 85.6% | o3 |
| LegalBench (Vals AI) | 79.7% | 83.8% | o3 |
| CorpFin (Vals AI) | 61.9% | 59.7% | Ling 3.0 Flash |
| TaxEval (Vals AI) | 70.7% | 74.6% | o3 |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only o3 measured |
| MedCode (Vals AI) | 32.3% | 47.3% | o3 |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.3% | Only o3 measured |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1036 | Only o3 measured |
| Design Arena: UI components | Not measured | 1017 | Only o3 measured |
| Design Arena: game dev | Not measured | 1034 | Only o3 measured |
| Design Arena: data viz | Not measured | 1200 | Only o3 measured |
| Design Arena: all code categories | Not measured | 1020 | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Ling 3.0 Flash | o3 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | 80.9% | 76.7% | Ling 3.0 Flash |
| IFBench | Not measured | 71.4% | Only o3 measured |
| Long context reasoning (AA-LCR) | 73.0% | 74.7% | o3 |
| DTBench | Not measured | 84.8% | Only o3 measured |
| LMCA | Not measured | 39.7% | Only o3 measured |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | o3 | 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 | inclusionAI Ling 3.0 Flash | OpenAI o3 |
|---|---|---|
| Maker | inclusionAI | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 23, 2026 | April 16, 2025 |
| Context window | 262K | 200K |
| Input $/Mtok | $0.0405 | $2.00 |
| Output $/Mtok | $0.1215 | $8.00 |
| Coding index | 29.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.05 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than o3 for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. Pick o3 for work measured by AA Intelligence Index or MMLU Pro, where it leads.
Which is cheaper, Ling 3.0 Flash or o3?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, Ling 3.0 Flash costs $0.05 and o3 $0.571.
Which has the bigger context window, Ling 3.0 Flash or o3?
Ling 3.0 Flash, with 262K tokens against 200K.
How many benchmarks is the Ling 3.0 Flash vs o3 comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.