GPT-5 Mini vs Ling 3.1 Flash
Ling 3.1 Flash leads GPT-5 Mini on the Artificial Analysis Intelligence Index, 41.1 to 20.6, and Ling 3.1 Flash costs 8.9x less per output token ($0.225 vs $2.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-5 Mini or Ling 3.1 Flash: which to pick
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; Ling 3.1 Flash has none, so the two cannot be ranked on coding.
Pick Ling 3.1 Flash for output tokens 8.9x cheaper ($0.225 vs $2.00 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
- Shared results
- 4
- GPT-5 Mini only
- 97
- Ling 3.1 Flash only
- 0
- 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 GPT-5 Mini has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 20.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 Mini has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only GPT-5 Mini has a AIME result, 91.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Mini has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5 Mini has a Design Arena: websites result, 1125.
Directional only
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
GPT-5 Mini $0.0013, Ling 3.1 Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Mini has the number, $0.08.
Directional only
At a glance
- Coding index
- GPT-5 Mini 34.2 from 2 benchmarks, Ling 3.1 Flash not benchmarked.
- Widest category
- Intelligence, 20.5 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-5 Mini 20.6 against Ling 3.1 Flash 41.1, 20.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Mini $2.00 against Ling 3.1 Flash $0.225 per million output tokens, 8.9x apart.
- Context window
- GPT-5 Mini takes 400K tokens, Ling 3.1 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 Mini | Bars | Ling 3.1 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 59.8% | Not measured | Only GPT-5 Mini measured |
| SWE-bench Multilingual | 39.7% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Vals AI) | 86.6% | Not measured | Only GPT-5 Mini measured |
| SWE-bench Verified (Vals AI) | 60.8% | Not measured | Only GPT-5 Mini measured |
| Vibe Code Bench (Vals AI) | 14.2% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.8% | Not measured | Only GPT-5 Mini measured |
| SciCode | 39.0% | 54.1% | Ling 3.1 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GPT-5 Mini measured |
| Terminal-Bench | 34.8% | Not measured | Only GPT-5 Mini measured |
| WeirdML | 52.7% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: coding | 1406 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: software and IT | 1394 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.6 | 41.1 | Ling 3.1 Flash |
| Epoch Capabilities Index | 145.3 | Not measured | Only GPT-5 Mini measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 71.1% | Not measured | Only GPT-5 Mini measured |
| Vending-Bench 2 | -$31 | Not measured | Only GPT-5 Mini measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.5% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only GPT-5 Mini measured |
| MMMU Pro (Vals AI) | 78.9% | Not measured | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 82.8% | Not measured | Only GPT-5 Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 21.5% | 39.4% | Ling 3.1 Flash |
| GPQA Diamond (Epoch AI run) | 75.0% | Not measured | Only GPT-5 Mini measured |
| ARC-AGI | 54.3% | Not measured | Only GPT-5 Mini measured |
| ARC-AGI-2 | 4.4% | Not measured | Only GPT-5 Mini measured |
| Chess puzzles | 24.0% | Not measured | Only GPT-5 Mini measured |
| Mystery game puzzles | 10.0% | Not measured | Only GPT-5 Mini measured |
| VPCT | 40.2% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | 1380 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: expert | 1379 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: hard prompts in English | 1383 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: diagrams | 1202 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: homework | 1210 | Not measured | Only GPT-5 Mini measured |
| Kagi LLM Benchmark | 70.3% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AIME | 91.5% | Not measured | Only GPT-5 Mini measured |
| MATH 500 (Vals AI) | 94.8% | Not measured | Only GPT-5 Mini measured |
| MGSM (Vals AI) | 92.6% | Not measured | Only GPT-5 Mini measured |
| AA Math Index | 90.7 | Not measured | Only GPT-5 Mini measured |
| AIME 2025 (Artificial Analysis run) | 90.7% | Not measured | Only GPT-5 Mini measured |
| OTIS Mock AIME | 86.7% | Not measured | Only GPT-5 Mini measured |
| FrontierMath, tiers 1 to 3 | 46.7% | Not measured | Only GPT-5 Mini measured |
| FrontierMath, tier 4 | 12.2% | Not measured | Only GPT-5 Mini measured |
| ProofBench | 9.0% | Not measured | Only GPT-5 Mini measured |
| MATH level 5 | 97.8% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: math | 1378 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: mathematical work | 1380 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GPT-5 Mini measured |
| LegalBench (Vals AI) | 81.8% | Not measured | Only GPT-5 Mini measured |
| CaseLaw (Vals AI) | 68.5% | Not measured | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | 60.2% | Not measured | Only GPT-5 Mini measured |
| TaxEval (Vals AI) | 75.2% | Not measured | Only GPT-5 Mini measured |
| MortgageTax (Vals AI) | 66.9% | Not measured | Only GPT-5 Mini measured |
| MedQA (Vals AI) | 96.1% | Not measured | Only GPT-5 Mini measured |
| MedCode (Vals AI) | 43.0% | Not measured | Only GPT-5 Mini measured |
| MMLU Pro (Artificial Analysis run) | 83.7% | Not measured | Only GPT-5 Mini measured |
| SimpleQA Verified | 21.6% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: science | 1371 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: medicine and healthcare | 1380 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: legal and government | 1375 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: business and finance | 1358 | Not measured | Only GPT-5 Mini measured |
| FACTS Benchmark Suite | 38.6% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1125 | Not measured | Only GPT-5 Mini measured |
| Design Arena: UI components | 1103 | Not measured | Only GPT-5 Mini measured |
| Design Arena: game dev | 1129 | Not measured | Only GPT-5 Mini measured |
| Design Arena: data viz | 1131 | Not measured | Only GPT-5 Mini measured |
| Design Arena: 3D | 1036 | Not measured | Only GPT-5 Mini measured |
| Design Arena: all code categories | 1114 | Not measured | Only GPT-5 Mini measured |
| Design Arena: SVG | 1091 | Not measured | Only GPT-5 Mini measured |
| Design Arena: ASCII art | 1125 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.0% | Not measured | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | 80.6% | Not measured | Only GPT-5 Mini measured |
| IFBench | 75.4% | Not measured | Only GPT-5 Mini measured |
| Long context reasoning (AA-LCR) | 72.3% | 83.0% | Ling 3.1 Flash |
| DTBench | 80.5% | Not measured | Only GPT-5 Mini measured |
| LMCA | 34.2% | Not measured | Only GPT-5 Mini measured |
| Fiction.LiveBench | 69.4% | Not measured | Only GPT-5 Mini measured |
| Creative writing (Lech Mazur) | 83.1% | Not measured | Only GPT-5 Mini measured |
| LMArena Text | 1373 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: instruction following | 1357 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: creative writing | 1325 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision | 1202 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: English | 1379 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: non-English | 1363 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Chinese | 1386 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: German | 1366 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Japanese | 1341 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Korean | 1308 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: French | 1386 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Spanish | 1354 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Russian | 1362 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Polish | 1378 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: multi-turn | 1363 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: longer queries | 1355 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: writing and language | 1344 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: entertainment and media | 1340 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: English | 1216 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | 1214 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: reading text | 1199 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: humor | 1205 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: captioning | 1217 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | 1226 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | 1181 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Mini | Ling 3.1 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 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 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 Mini | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | October 2, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.25 | $0.075 |
| Output $/Mtok | $2.00 | $0.225 |
| Coding index | 34.2 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.08 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Mini better than Ling 3.1 Flash for coding?
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; Ling 3.1 Flash has none, so the two cannot be ranked on coding. Pick Ling 3.1 Flash for output tokens 8.9x cheaper ($0.225 vs $2.00 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
Which is cheaper, GPT-5 Mini or Ling 3.1 Flash?
GPT-5 Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for GPT-5 Mini only.
Which has the bigger context window, GPT-5 Mini or Ling 3.1 Flash?
GPT-5 Mini, with 400K tokens against 262K.
How many benchmarks is the GPT-5 Mini vs Ling 3.1 Flash comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-5 Mini's coding index rests on 2 independent benchmarks and Ling 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.