Ling 3.0 Flash vs Qwen3.6 27B
Qwen3.6 27B leads Ling 3.0 Flash on coding, 36.8 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 22.2x less per output token ($0.1215 vs $2.70 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Ling 3.0 Flash or Qwen3.6 27B: which to pick
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 22.2x cheaper ($0.1215 vs $2.70 per million) and a lower cost per solved task ($0.05 against $1.30).
- Shared results
- 11
- Ling 3.0 Flash only
- 13
- Qwen3.6 27B only
- 14
- Like-for-like categories
- 6 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
Qwen3.6 27B
Qwen3.6 27B scores 36.8 against 29.6.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 20.1.
Same board, both measured
Agentic work
AA Agentic Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 19.3 against 18.5.
Same board, both measured
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, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Ling 3.0 Flash $0.05, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Coding, 4.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Ling 3.0 Flash 2.9% against Qwen3.6 27B 11.9%, 9.0 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Qwen3.6 27B $2.70 per million output tokens, 22.2x 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 | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 3 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Coding Index | 50.6 | 53.7 | Qwen3.6 27B |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | 65.2% | 70.0% | Qwen3.6 27B |
| Vibe Code Bench (Vals AI) | 2.9% | 11.9% | Qwen3.6 27B |
| 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% | 42.8% | Qwen3.6 27B |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.1 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | 18.5 | Ling 3.0 Flash |
| 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 | 94.2% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | Not measured | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | 84.2% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | 23.1% | Ling 3.0 Flash |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | 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 |
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 61.9% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 70.7% | 71.3% | Qwen3.6 27B |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| MedCode (Vals AI) | 32.3% | Not measured | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| MedScribe (Vals AI) | 80.9% | Not measured | Only Ling 3.0 Flash measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | 73.0% | 77.3% | Qwen3.6 27B |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| EQ-Bench 4 | Not measured | 1026 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Qwen3.6 27B | 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 | Qwen3.6 27B |
|---|---|---|
| Maker | inclusionAI | Qwen |
| Weights | Open weights | Open weights |
| Released | July 23, 2026 | April 27, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.0405 | $0.45 |
| Output $/Mtok | $0.1215 | $2.70 |
| Coding index | 29.6 | 36.8 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.05 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Qwen3.6 27B for coding?
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 22.2x cheaper ($0.1215 vs $2.70 per million) and a lower cost per solved task ($0.05 against $1.30).
Which is cheaper, Ling 3.0 Flash or Qwen3.6 27B?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Ling 3.0 Flash costs $0.05 and Qwen3.6 27B $1.30.
Which has the bigger context window, Ling 3.0 Flash or Qwen3.6 27B?
Neither: both take 262K tokens.
How many benchmarks is the Ling 3.0 Flash vs Qwen3.6 27B comparison based on?
11 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 Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.