Gemini 3.7 Flash vs Ling 3.0 Flash
Gemini 3.7 Flash leads Ling 3.0 Flash on coding, 58.0 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 30.9x less per output token ($0.1215 vs $3.75 per million). Updated October 22, 2026; 21 benchmarks have a result for both.
Gemini 3.7 Flash or Ling 3.0 Flash: which to pick
Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 30.9x cheaper ($0.1215 vs $3.75 per million) and a lower cost per solved task ($0.05 against $1.78).
- Shared results
- 21
- Gemini 3.7 Flash only
- 93
- Ling 3.0 Flash only
- 3
- 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
Gemini 3.7 Flash
Gemini 3.7 Flash scores 58.0 against 29.6.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 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
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 93.9% against 84.8%.
Same board, both measured
Knowledge
MMLU Pro
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 90.1% against 82.0%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Gemini 3.7 Flash has a Design Arena: websites result, 1303.
Directional only
Long documents
Listed context window
Gemini 3.7 Flash
Gemini 3.7 Flash takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Gemini 3.7 Flash $0.0026, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
Gemini 3.7 Flash $1.78, Ling 3.0 Flash $0.05.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 3.7 Flash 58.0 from 6 benchmarks, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Agentic work, 36.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Gemini 3.7 Flash 70.4% against Ling 3.0 Flash 2.9%, 67.5 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.7 Flash $3.75 against Ling 3.0 Flash $0.1215 per million output tokens, 30.9x apart.
- Context window
- Gemini 3.7 Flash takes 1.05M 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 | Gemini 3.7 Flash | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 21 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LiveBench coding | 80.5% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev | 1591 | Not measured | Only Gemini 3.7 Flash measured |
| AA Coding Index | 71.5 | 50.6 | Gemini 3.7 Flash |
| LiveCodeBench (Vals AI) | 88.7% | 84.0% | Gemini 3.7 Flash |
| SWE-bench Verified (Vals AI) | 80.8% | 65.2% | Gemini 3.7 Flash |
| Terminal-Bench 4 (Vals AI) | 12.1% | Not measured | Only Gemini 3.7 Flash measured |
| Vibe Code Bench (Vals AI) | 70.4% | 2.9% | Gemini 3.7 Flash |
| Code migration (Vals AI) | 34.8% | 0.0% | Gemini 3.7 Flash |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.7 Flash measured |
| IOI (Vals AI) | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | 50.2% | Gemini 3.7 Flash |
| SkillsBench (Vals AI) | 65.9% | 27.9% | Gemini 3.7 Flash |
| CyberBench (Vals AI) | 43.8% | Not measured | Only Gemini 3.7 Flash measured |
| SciCode | 59.8% | 42.0% | Gemini 3.7 Flash |
| FrontierCode | 43.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierSWE | 20.3% | Not measured | Only Gemini 3.7 Flash measured |
| ALE-Bench | 904.3 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: coding | 1499 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: software and IT | 1498 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: React | 1587 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: HTML | 1593 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: from an image | 1599 | Not measured | Only Gemini 3.7 Flash measured |
| SciCode | 13.8% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.6 | 20.1 | Gemini 3.7 Flash |
| Epoch Capabilities Index | 158.0 | Not measured | Only Gemini 3.7 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| Design Arena: agents building web apps | 1223 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building full-stack apps | 1208 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building mobile apps | 1229 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Android apps | 1243 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Godot games | 1264 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building games | 1235 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: spreadsheets | 1058 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench research | 76.5% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench browser | 56.0% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench long agentic | 28.3% | Not measured | Only Gemini 3.7 Flash measured |
| SREBench (Vals AI) | 4.6% | Not measured | Only Gemini 3.7 Flash measured |
| Finance Agent (Vals AI) | 59.0% | 30.3% | Gemini 3.7 Flash |
| Tax Agent Bench (Vals AI) | 22.8% | Not measured | Only Gemini 3.7 Flash measured |
| Legal Research Bench (Vals AI) | 34.6% | 0.0% | Gemini 3.7 Flash |
| Excel Modeling Benchmark (Vals AI) | 71.3% | 25.0% | Gemini 3.7 Flash |
| Terminal-Bench-Science (Vals AI) | 7.1% | Not measured | Only Gemini 3.7 Flash measured |
| APEX-Agents | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Remote Labor Index | 5.0% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 93.9% | 84.8% | Gemini 3.7 Flash |
| LiveBench reasoning | 87.8% | Not measured | Only Gemini 3.7 Flash measured |
| MMMU Pro (Vals AI) | 89.0% | Not measured | Only Gemini 3.7 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 94.5% | 85.5% | Gemini 3.7 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 23.7% | Gemini 3.7 Flash |
| GPQA Diamond (Epoch AI run) | 94.8% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI | 95.5% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI-2 | 84.6% | Not measured | Only Gemini 3.7 Flash measured |
| Chess puzzles | 66.0% | Not measured | Only Gemini 3.7 Flash measured |
| Mystery game puzzles | 37.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: hard prompts | 1495 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: expert | 1512 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: hard prompts in English | 1487 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: diagrams | 1329 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: homework | 1320 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 97.2% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tiers 1 to 3 | 71.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tier 4 | 36.6% | Not measured | Only Gemini 3.7 Flash measured |
| ProofBench | 58.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: math | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: mathematical work | 1502 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 90.1% | 82.0% | Gemini 3.7 Flash |
| LegalBench (Vals AI) | 87.3% | 79.7% | Gemini 3.7 Flash |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | 74.7% | 70.7% | Gemini 3.7 Flash |
| MortgageTax (Vals AI) | 66.7% | Not measured | Only Gemini 3.7 Flash measured |
| MedCode (Vals AI) | 53.4% | 32.3% | Gemini 3.7 Flash |
| SimpleQA Verified | 69.2% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: science | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: medicine and healthcare | 1494 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: legal and government | 1496 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: business and finance | 1479 | Not measured | Only Gemini 3.7 Flash measured |
| FACTS Benchmark Suite | 71.2% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1303 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: UI components | 1296 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: game dev | 1317 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: data viz | 1315 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: 3D | 1307 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: all code categories | 1308 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: SVG | 1283 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: ASCII art | 1276 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench design | 73.2% | Not measured | Only Gemini 3.7 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.7 Flash | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 68.0% | Not measured | Only Gemini 3.7 Flash measured |
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| SAGE (Vals AI) | 49.2% | Not measured | Only Gemini 3.7 Flash measured |
| MedScribe (Vals AI) | 83.9% | 80.9% | Gemini 3.7 Flash |
| Long context reasoning (AA-LCR) | 83.0% | 73.0% | Gemini 3.7 Flash |
| DTBench | 96.8% | Not measured | Only Gemini 3.7 Flash measured |
| LMCA | 50.4% | Not measured | Only Gemini 3.7 Flash measured |
| Furniture assembly | 26.7% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text | 1489 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: instruction following | 1484 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: creative writing | 1488 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision | 1314 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: English | 1483 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: non-English | 1487 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Chinese | 1552 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: German | 1506 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Japanese | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Korean | 1481 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: French | 1500 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Spanish | 1495 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Russian | 1515 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Polish | 1503 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: multi-turn | 1485 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: longer queries | 1492 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: writing and language | 1491 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: entertainment and media | 1477 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: English | 1309 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: Chinese | 1394 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: reading text | 1322 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: humor | 1299 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: creative writing | 1312 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.7 Flash | 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 | Google Gemini 3.7 Flash | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | inclusionAI | |
| Weights | No public weights listed | Open weights |
| Released | August 13, 2026 | July 23, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.75 | $0.0405 |
| Output $/Mtok | $3.75 | $0.1215 |
| Coding index | 58.0 | 29.6 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $1.78 | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.7 Flash better than Ling 3.0 Flash for coding?
Pick Gemini 3.7 Flash for coding: it scores 58.0 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 30.9x cheaper ($0.1215 vs $3.75 per million) and a lower cost per solved task ($0.05 against $1.78).
Which is cheaper, Gemini 3.7 Flash or Ling 3.0 Flash?
Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and Ling 3.0 Flash $0.05.
Which has the bigger context window, Gemini 3.7 Flash or Ling 3.0 Flash?
Gemini 3.7 Flash, with 1.05M tokens against 262K.
How many benchmarks is the Gemini 3.7 Flash vs Ling 3.0 Flash comparison based on?
21 benchmarks have a result for both models as of October 22, 2026. Gemini 3.7 Flash's coding index rests on 6 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.