GPT-5.6 Sol vs Ling 3.0 Flash
GPT-5.6 Sol leads Ling 3.0 Flash on coding, 73.5 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 164.6x less per output token ($0.1215 vs $20.00 per million). Updated October 22, 2026; 24 benchmarks have a result for both.
vs boards won of 24 shared
GPT-5.6 Sol or Ling 3.0 Flash: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 164.6x cheaper ($0.1215 vs $20.00 per million) and a lower cost per solved task ($0.05 against $1.20).
Model AGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved taskModel BLing 3.0 FlashinclusionAI29.6/1002 independent benchmarks$0.05 per solved task
- Shared results
- 24
- GPT-5.6 Sol only
- 124
- Ling 3.0 Flash only
- 0
- 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
GPT-5.6 Sol
GPT-5.6 Sol scores 73.5 against 29.6.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 20.1.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 19.3.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 84.8%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.6 Sol
GPT-5.6 Sol leads, 89.1% against 82.0%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.6 Sol has a Design Arena: websites result, 1321.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
GPT-5.6 Sol $0.014, Ling 3.0 Flash $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Ling 3.0 Flash
GPT-5.6 Sol $1.20, Ling 3.0 Flash $0.05.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Agentic work, 37.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.6 Sol 80.5% against Ling 3.0 Flash 2.9%, 77.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Ling 3.0 Flash $0.1215 per million output tokens, 164.6x apart.
- Context window
- GPT-5.6 Sol 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 | GPT-5.6 Sol | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 7 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 23 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index71
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | Not measured | Only GPT-5.6 Sol measured |
| AA Coding Index | 77.4 | 50.6 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | 84.0% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | 96.2% | 65.2% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | 2.9% | GPT-5.6 Sol |
| Code migration (Vals AI) | 52.9% | 0.0% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | 50.2% | GPT-5.6 Sol |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | 27.9% | GPT-5.6 Sol |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | 42.0% | GPT-5.6 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | Not measured | Only GPT-5.6 Sol measured |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1496 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | 1489 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1620 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: HTML | 1618 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: from an image | 1609 | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| SWE-Bench Pro V2 | 95.5% | Not measured | Only GPT-5.6 Sol measured |
| SWE Atlas: Codebase QnA | 46.0% | Not measured | Only GPT-5.6 Sol measured |
| SWE Atlas: Test Writing | 45.9% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 20.1 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | Not measured | Only GPT-5.6 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 19.3 | GPT-5.6 Sol |
| Design Arena: agents building web apps | 1245 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1248 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | 1239 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | 1260 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | 1266 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1225 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1214 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | 30.3% | GPT-5.6 Sol |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | 0.0% | GPT-5.6 Sol |
| Excel Modeling Benchmark (Vals AI) | 72.3% | 25.0% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | Not measured | Only GPT-5.6 Sol measured |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| MCP Atlas | 81.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | 84.8% | GPT-5.6 Sol |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 85.5% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 23.7% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | Not measured | Only GPT-5.6 Sol measured |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.6 Sol measured |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: expert | 1517 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | 1477 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Scale run) | 31.2% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: math | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.6 Sol measured |
| MathArena | 62.9% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | 82.0% | GPT-5.6 Sol |
| LegalBench (Vals AI) | 87.0% | 79.7% | GPT-5.6 Sol |
| CorpFin (Vals AI) | 64.4% | 61.9% | GPT-5.6 Sol |
| TaxEval (Vals AI) | 74.8% | 70.7% | GPT-5.6 Sol |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | 32.3% | GPT-5.6 Sol |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: science | 1455 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | 1431 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1457 | Not measured | Only GPT-5.6 Sol measured |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| PRBench Finance | 50.4% | Not measured | Only GPT-5.6 Sol measured |
| PRBench Legal | 50.5% | Not measured | Only GPT-5.6 Sol 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 | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: UI components | 1336 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: game dev | 1367 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: data viz | 1323 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: 3D | 1384 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: all code categories | 1341 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: SVG | 1315 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1304 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | 50.7% | GPT-5.6 Sol |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | 80.9% | GPT-5.6 Sol |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | Not measured | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | 73.0% | GPT-5.6 Sol |
| DTBench | 95.7% | Not measured | Only GPT-5.6 Sol measured |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | 1449 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | 1451 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | 1525 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: German | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1442 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1443 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1471 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1461 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | 1462 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol measured |
| EQ-Bench 4 | 1250 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | 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
GPT-5.6 Sol$0.014Fits in one request
Ling 3.0 Flash$0.0001Fits in one request
Ling 3.0 Flash costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Ling 3.0 Flash$0.0024Fits in one request
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Ling 3.0 Flash$0.0093Fits in one request
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 | OpenAI GPT-5.6 Sol | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | July 23, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $4.00 | $0.0405 |
| Output $/Mtok | $20.00 | $0.1215 |
| Coding index | 73.5 | 29.6 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $1.20 | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Ling 3.0 Flash for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 164.6x cheaper ($0.1215 vs $20.00 per million) and a lower cost per solved task ($0.05 against $1.20).
Which is cheaper, GPT-5.6 Sol or Ling 3.0 Flash?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Per solved SWE-bench Verified task, GPT-5.6 Sol costs $1.20 and Ling 3.0 Flash $0.05.
Which has the bigger context window, GPT-5.6 Sol or Ling 3.0 Flash?
GPT-5.6 Sol, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Sol vs Ling 3.0 Flash comparison based on?
24 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol'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.