Ling 3.0 Flash vs Qwen3.5-Flash
Qwen3.5-Flash leads Ling 3.0 Flash on coding, 41.1 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 2.1x less per output token ($0.1215 vs $0.26 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
Ling 3.0 Flash or Qwen3.5-Flash: which to pick
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 2.1x cheaper ($0.1215 vs $0.26 per million) and a lower cost per solved task ($0.05 against $0.40).
- Shared results
- 9
- Ling 3.0 Flash only
- 15
- Qwen3.5-Flash only
- 46
- Like-for-like categories
- 4 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
Qwen3.5-Flash
Qwen3.5-Flash scores 41.1 against 29.6.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only Ling 3.0 Flash has a AA Intelligence Index result, 20.1.
Directional only
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 82.8%.
Same board, both measured
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
Qwen3.5-Flash
Qwen3.5-Flash leads, 84.1% against 82.0%.
Same board, both measured
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Ling 3.0 Flash $0.05, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- General, 10.3 points apart on average over 1 shared benchmark.
- Widest single gap
- MedScribe (Vals AI): Ling 3.0 Flash 80.9% against Qwen3.5-Flash 70.6%, 10.3 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Qwen3.5-Flash $0.26 per million output tokens, 2.1x apart.
- Context window
- Ling 3.0 Flash takes 262K tokens, Qwen3.5-Flash 1M.
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 | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 1 | 0 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Ling 3.0 Flash | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | 83.3% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 65.2% | 64.4% | Ling 3.0 Flash |
| 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 |
| ALE-Bench | Not measured | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | Not measured | 1412 | Only Qwen3.5-Flash measured |
| LMArena Text: software and IT | Not measured | 1416 | Only Qwen3.5-Flash measured |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | Not measured | Only Ling 3.0 Flash measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.5-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 |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | 82.8% | Ling 3.0 Flash |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | Not measured | Only Ling 3.0 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | Not measured | Only Ling 3.0 Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | Not measured | 1403 | Only Qwen3.5-Flash measured |
| LMArena Text: expert | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1409 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: mathematical work | Not measured | 1413 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Ling 3.0 Flash | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | 84.1% | Qwen3.5-Flash |
| LegalBench (Vals AI) | 79.7% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | 61.9% | 63.6% | Qwen3.5-Flash |
| TaxEval (Vals AI) | 70.7% | 72.2% | Qwen3.5-Flash |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | 32.3% | 33.0% | Qwen3.5-Flash |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | Not measured | 1415 | Only Qwen3.5-Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: legal and government | Not measured | 1398 | Only Qwen3.5-Flash measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.5-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 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | 80.9% | 70.6% | Ling 3.0 Flash |
| Long context reasoning (AA-LCR) | 73.0% | Not measured | Only Ling 3.0 Flash measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| LMArena Text | Not measured | 1397 | Only Qwen3.5-Flash measured |
| LMArena Text: instruction following | Not measured | 1374 | Only Qwen3.5-Flash measured |
| LMArena Text: creative writing | Not measured | 1343 | Only Qwen3.5-Flash measured |
| LMArena Text: English | Not measured | 1404 | Only Qwen3.5-Flash measured |
| LMArena Text: non-English | Not measured | 1385 | Only Qwen3.5-Flash measured |
| LMArena Text: Chinese | Not measured | 1445 | Only Qwen3.5-Flash measured |
| LMArena Text: German | Not measured | 1390 | Only Qwen3.5-Flash measured |
| LMArena Text: Japanese | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | Not measured | 1344 | Only Qwen3.5-Flash measured |
| LMArena Text: French | Not measured | 1413 | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | Not measured | 1400 | Only Qwen3.5-Flash measured |
| LMArena Text: Russian | Not measured | 1380 | Only Qwen3.5-Flash measured |
| LMArena Text: Polish | Not measured | 1376 | Only Qwen3.5-Flash measured |
| LMArena Text: multi-turn | Not measured | 1393 | Only Qwen3.5-Flash measured |
| LMArena Text: longer queries | Not measured | 1392 | Only Qwen3.5-Flash measured |
| LMArena Text: writing and language | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: entertainment and media | Not measured | 1347 | Only Qwen3.5-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 | Qwen3.5-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 | Qwen3.5-Flash |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | July 23, 2026 | February 25, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.0405 | $0.065 |
| Output $/Mtok | $0.1215 | $0.26 |
| Coding index | 29.6 | 41.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.05 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Qwen3.5-Flash for coding?
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 2.1x cheaper ($0.1215 vs $0.26 per million) and a lower cost per solved task ($0.05 against $0.40).
Which is cheaper, Ling 3.0 Flash or Qwen3.5-Flash?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, Ling 3.0 Flash costs $0.05 and Qwen3.5-Flash $0.40.
Which has the bigger context window, Ling 3.0 Flash or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 262K.
How many benchmarks is the Ling 3.0 Flash vs Qwen3.5-Flash comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.