Ling 3.0 Flash vs Step 3.5 Flash
Ling 3.0 Flash leads Step 3.5 Flash on the Artificial Analysis Intelligence Index, 20.1 to 17.0, and Ling 3.0 Flash costs 2.5x less per output token ($0.1215 vs $0.30 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
Ling 3.0 Flash or Step 3.5 Flash: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding.
Pick Step 3.5 Flash for work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 4
- Ling 3.0 Flash only
- 20
- Step 3.5 Flash only
- 31
- 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 17.0.
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
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, Step 3.5 Flash $0.0003 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
- Ling 3.0 Flash 29.6 from 2 benchmarks, Step 3.5 Flash not benchmarked.
- Widest category
- General, 10.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Ling 3.0 Flash 73.0% against Step 3.5 Flash 63.0%, 10.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Step 3.5 Flash $0.30 per million output tokens, 2.5x apart.
- Context window
- Both take 262K tokens.
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 | Step 3.5 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 3 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards, none shared
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Ling 3.0 Flash measured |
| 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 |
| SciCode | 42.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Step 3.5 Flash measured |
| LMArena Text: coding | Not measured | 1436 | Only Step 3.5 Flash measured |
| LMArena Text: software and IT | Not measured | 1434 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 17.0 | 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 index5 boards, none shared
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | 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 | 94.4% | Only Step 3.5 Flash measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | Not measured | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 83.1% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 24.5% | Step 3.5 Flash |
| LMArena Text: hard prompts | Not measured | 1411 | Only Step 3.5 Flash measured |
| LMArena Text: expert | Not measured | 1421 | Only Step 3.5 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1421 | Only Step 3.5 Flash 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 | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: mathematical work | Not measured | 1421 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | Not measured | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | 61.9% | Not measured | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | 70.7% | Not measured | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | 32.3% | Not measured | Only Ling 3.0 Flash measured |
| LMArena Text: science | Not measured | 1415 | Only Step 3.5 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: legal and government | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Step 3.5 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | 80.9% | Not measured | Only Ling 3.0 Flash measured |
| IFBench | Not measured | 66.5% | Only Step 3.5 Flash measured |
| Long context reasoning (AA-LCR) | 73.0% | 63.0% | Ling 3.0 Flash |
| LMArena Text | Not measured | 1403 | Only Step 3.5 Flash measured |
| LMArena Text: instruction following | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: creative writing | Not measured | 1357 | Only Step 3.5 Flash measured |
| LMArena Text: English | Not measured | 1417 | Only Step 3.5 Flash measured |
| LMArena Text: non-English | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: Chinese | Not measured | 1446 | Only Step 3.5 Flash measured |
| LMArena Text: German | Not measured | 1405 | Only Step 3.5 Flash measured |
| LMArena Text: Japanese | Not measured | 1353 | Only Step 3.5 Flash measured |
| LMArena Text: Korean | Not measured | 1351 | Only Step 3.5 Flash measured |
| LMArena Text: French | Not measured | 1421 | Only Step 3.5 Flash measured |
| LMArena Text: Spanish | Not measured | 1419 | Only Step 3.5 Flash measured |
| LMArena Text: Russian | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: Polish | Not measured | 1389 | Only Step 3.5 Flash measured |
| LMArena Text: multi-turn | Not measured | 1404 | Only Step 3.5 Flash measured |
| LMArena Text: longer queries | Not measured | 1402 | Only Step 3.5 Flash measured |
| LMArena Text: writing and language | Not measured | 1372 | Only Step 3.5 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1360 | Only Step 3.5 Flash 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 | Ling 3.0 Flash | Step 3.5 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 | inclusionAI Ling 3.0 Flash | StepFun Step 3.5 Flash |
|---|---|---|
| Maker | inclusionAI | StepFun |
| Weights | Open weights | Open weights |
| Released | July 23, 2026 | January 29, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.0405 | $0.10 |
| Output $/Mtok | $0.1215 | $0.30 |
| Coding index | 29.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.05 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Step 3.5 Flash for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding. Pick Step 3.5 Flash for work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Ling 3.0 Flash or Step 3.5 Flash?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, Ling 3.0 Flash or Step 3.5 Flash?
Neither: both take 262K tokens.
How many benchmarks is the Ling 3.0 Flash vs Step 3.5 Flash comparison based on?
4 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 Step 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.