GPT-6.1 Terra vs Ling 3.0 Flash
GPT-6.1 Terra leads Ling 3.0 Flash on coding, 77.4 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 41.2x less per output token ($0.1215 vs $5.00 per million). Updated October 29, 2026; 5 benchmarks have a result for both.
GPT-6.1 Terra or Ling 3.0 Flash: which to pick
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 41.2x cheaper ($0.1215 vs $5.00 per million).
- Shared results
- 5
- GPT-6.1 Terra only
- 31
- Ling 3.0 Flash only
- 18
- 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
GPT-6.1 Terra
GPT-6.1 Terra scores 77.4 against 29.6.
4 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-6.1 Terra has a AA Intelligence Index result, 51.0.
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
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
Web design
Design Arena: websites
No call
Only GPT-6.1 Terra has a Design Arena: websites result, 1283.
Directional only
General
Vals Index
No call
Only GPT-6.1 Terra has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
GPT-6.1 Terra $0.0035, Ling 3.0 Flash $0.0001 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
- GPT-6.1 Terra 77.4 from 4 benchmarks, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Coding, 47.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-6.1 Terra 84.0% against Ling 3.0 Flash 2.9%, 81.1 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Terra $5.00 against Ling 3.0 Flash $0.1215 per million output tokens, 41.2x apart.
- Context window
- GPT-6.1 Terra 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-6.1 Terra | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 5 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| LiveBench coding | 94.3% | Not measured | Only GPT-6.1 Terra measured |
| LMArena WebDev | 1673 | Not measured | Only GPT-6.1 Terra measured |
| AA Coding Index | Not measured | 50.6 | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 65.2% | Only Ling 3.0 Flash measured |
| Terminal-Bench 4 (Vals AI) | 49.5% | Not measured | Only GPT-6.1 Terra measured |
| Vibe Code Bench (Vals AI) | 84.0% | 2.9% | GPT-6.1 Terra |
| Code migration (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 50.2% | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | Not measured | 27.9% | Only Ling 3.0 Flash measured |
| SciCode | 55.1% | 42.0% | GPT-6.1 Terra |
| ALE-Bench | 2608.8 | Not measured | Only GPT-6.1 Terra measured |
| LMArena WebDev: React | 1657 | Not measured | Only GPT-6.1 Terra measured |
| LMArena WebDev: HTML | 1719 | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.0 | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| LiveBench research | 73.9% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench browser | 93.1% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench long agentic | 48.6% | Not measured | Only GPT-6.1 Terra measured |
| Finance Agent (Vals AI) | Not measured | 30.3% | Only Ling 3.0 Flash measured |
| Tax Agent Bench (Vals AI) | 15.2% | Not measured | Only GPT-6.1 Terra measured |
| Legal Research Bench (Vals AI) | 26.0% | 0.0% | GPT-6.1 Terra |
| Harvey's Legal Agent Benchmark (Vals AI) | 1.7% | Not measured | Only GPT-6.1 Terra measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 25.0% | Only Ling 3.0 Flash measured |
| APEX-Agents | 68.3% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.8% | Only Ling 3.0 Flash measured |
| LiveBench reasoning | 88.7% | Not measured | Only GPT-6.1 Terra measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.5% | Only Ling 3.0 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 46.2% | 23.7% | GPT-6.1 Terra |
| GPQA Diamond (Epoch AI run) | 94.3% | Not measured | Only GPT-6.1 Terra measured |
| Mystery game puzzles | 74.0% | Not measured | Only GPT-6.1 Terra measured |
| EBR-bench | 78.1% | Not measured | Only GPT-6.1 Terra measured |
| Humanity's Last Exam (Scale run) | 35.7% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6.1 Terra measured |
| FrontierMath, tiers 1 to 3 | 90.9% | Not measured | Only GPT-6.1 Terra measured |
| FrontierMath, tier 4 | 92.7% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.0% | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | Not measured | 79.7% | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | Not measured | 70.7% | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | Not measured | 32.3% | Only Ling 3.0 Flash measured |
| SimpleQA Verified | 57.3% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1283 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: UI components | 1277 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: game dev | 1348 | Not measured | Only GPT-6.1 Terra measured |
| Design Arena: all code categories | 1295 | Not measured | Only GPT-6.1 Terra measured |
| LiveBench design | 82.3% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Terra | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Vals Index | 60.5% | Not measured | Only GPT-6.1 Terra measured |
| LiveBench data analysis | 82.3% | Not measured | Only GPT-6.1 Terra measured |
| Public Benefits Bench (Vals AI) | Not measured | 50.7% | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | Not measured | 80.9% | Only Ling 3.0 Flash measured |
| Long context reasoning (AA-LCR) | 82.7% | 73.0% | GPT-6.1 Terra |
| Furniture assembly | 79.2% | Not measured | Only GPT-6.1 Terra measured |
| MultiChallenge | 74.2% | Not measured | Only GPT-6.1 Terra measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Terra | 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 | OpenAI GPT-6.1 Terra | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | October 26, 2026 | July 23, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.00 | $0.0405 |
| Output $/Mtok | $5.00 | $0.1215 |
| Coding index | 77.4 | 29.6 |
| Independent measurements behind it | 4 | 2 |
| Cost per solved task | Not computable | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Terra better than Ling 3.0 Flash for coding?
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 41.2x cheaper ($0.1215 vs $5.00 per million).
Which is cheaper, GPT-6.1 Terra or Ling 3.0 Flash?
GPT-6.1 Terra costs $1.00 per million input tokens and $5.00 per million output tokens; Ling 3.0 Flash costs $0.0405 and $0.1215. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, GPT-6.1 Terra or Ling 3.0 Flash?
GPT-6.1 Terra, with 1.05M tokens against 262K.
How many benchmarks is the GPT-6.1 Terra vs Ling 3.0 Flash comparison based on?
5 benchmarks have a result for both models as of October 29, 2026. GPT-6.1 Terra's coding index rests on 4 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.