GPT-4o-mini vs Ling 3.0 Flash
Ling 3.0 Flash leads GPT-4o-mini on the Artificial Analysis Intelligence Index, 20.1 to 6.7, and Ling 3.0 Flash costs 4.9x less per output token ($0.1215 vs $0.60 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
GPT-4o-mini or Ling 3.0 Flash: which to pick
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 9
- GPT-4o-mini only
- 20
- Ling 3.0 Flash only
- 15
- 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
No call
Only Ling 3.0 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.0 Flash
Ling 3.0 Flash leads, 20.1 against 6.7.
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
Ling 3.0 Flash
Ling 3.0 Flash leads, 84.8% against 44.2%.
Same board, both measured
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
Ling 3.0 Flash
Ling 3.0 Flash leads, 82.0% against 62.7%.
Same board, both measured
Long documents
Listed context window
Ling 3.0 Flash
Ling 3.0 Flash takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
GPT-4o-mini $0.0004, 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-4o-mini not benchmarked, Ling 3.0 Flash 29.6 from 2 benchmarks.
- Widest category
- Coding, 48.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- LiveCodeBench (Vals AI): GPT-4o-mini 26.4% against Ling 3.0 Flash 84.0%, 57.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against Ling 3.0 Flash $0.1215 per million output tokens, 4.9x apart.
- Context window
- GPT-4o-mini takes 128K 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-4o-mini | Bars | Ling 3.0 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Knowledge | 0 | 3 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | 50.6 | Ling 3.0 Flash |
| LiveCodeBench (Vals AI) | 26.4% | 84.0% | Ling 3.0 Flash |
| SWE-bench Verified (Vals AI) | Not measured | 65.2% | Only Ling 3.0 Flash measured |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Ling 3.0 Flash measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| SciCode | Not measured | 42.0% | Only Ling 3.0 Flash measured |
| SciCode | 1.5% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 20.1 | Ling 3.0 Flash |
| Epoch Capabilities Index | 126.5 | Not measured | Only GPT-4o-mini measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 19.3 | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | Not measured | 30.3% | Only Ling 3.0 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 0.0% | Only Ling 3.0 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 25.0% | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | 84.8% | Ling 3.0 Flash |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | 85.5% | Ling 3.0 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 23.7% | Ling 3.0 Flash |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| Kagi LLM Benchmark | 28.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | 82.0% | Ling 3.0 Flash |
| LegalBench (Vals AI) | Not measured | 79.7% | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | 45.5% | 61.9% | Ling 3.0 Flash |
| TaxEval (Vals AI) | 60.5% | 70.7% | Ling 3.0 Flash |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MedCode (Vals AI) | Not measured | 32.3% | Only Ling 3.0 Flash measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | Not measured | Only GPT-4o-mini measured |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o-mini | Ling 3.0 Flash | Leader |
|---|---|---|---|
| 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 |
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| Long context reasoning (AA-LCR) | Not measured | 73.0% | Only Ling 3.0 Flash measured |
| DTBench | 54.4% | Not measured | Only GPT-4o-mini measured |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 39 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | 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-4o-mini | inclusionAI Ling 3.0 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | July 23, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.15 | $0.0405 |
| Output $/Mtok | $0.60 | $0.1215 |
| Coding index | Not benchmarked | 29.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.05 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini better than Ling 3.0 Flash for coding?
Only Ling 3.0 Flash has a coding score, 29.6 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-4o-mini or Ling 3.0 Flash?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 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-4o-mini or Ling 3.0 Flash?
Ling 3.0 Flash, with 262K tokens against 128K.
How many benchmarks is the GPT-4o-mini vs Ling 3.0 Flash comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini's coding index rests on 0 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.